The story of winged human flight begins without a tail--the Wright Brothers' first successful glider didn't have one. Soon, biplanes ushered in the now-standard tube-and-wing design for aircraft, but experiments with blended wings never really stopped. The planes are potentially more aerodynamic and consume less fuel, though they are harder to maneuver. Researchers hope that computerized, fly-by-wire systems will soon overcome the control challenges and spawn an era of fuel-efficient heavy lifters. One proposed design is the SAX-40, an airliner that could trim fuel use by more than 20 percent and fly quietly enough to take off and land during late-night hours that are currently restricted. According to Jim Hileman, a researcher at MIT and chief engineer on the project, expanding the hours of operation for airports could reduce air traffic congestion--and the fuel wasted by circling planes--while avoiding legal battles over new runway construction.
The plane is just a thought experiment for now, created by Hileman and his colleagues in the Silent Aircraft Initiative, a U.K.-funded collaboration between Cambridge University and MIT. But the design's enthusiasts are encouraged by successful, ongoing tests of the X-48B, a blended-wing prototype built by Boeing in cooperation with NASA and the Air Force. The company is focusing purely on military applications, but Hileman points out that wrestling civilian benefits from defense research is a grand old aviation tradition--the 707, Boeing's first commercial passenger jet, had a military lineage. "Maybe the U.S. Air Force will build a better tanker or bomber, which leads to a blended-wing airliner." Ultimately, Hileman says, aeronautical engineers will have to step up their game. "We haven't reached full maturity with our designs," he says. "We can still make a real impact on fuel use and aircraft noise."
Like so many of the best things in life, the inspiration for cloaking technology comes from the Klingons, who used it on their starships. Researchers have had some success "cloaking" an object by redirecting light around it to render it invisible. But the principle might work even better to shield buildings from earthquake damage. The structures would incorporate "metamaterials" patterned with tiny circles whose size is proportional to the wavelength of seismic disturbances. The waves would travel along the material, missing the structure. Real-World Potential: The theory seems sound, but years of experimentation lie ahead. And engineers would then need to devise ways to build the technology into new buildings. (Retrofits would likely remain impossible.)
Oil companies employ drones called "pigs" to crawl through pipelines, spotting corrosion. Fancier pipe bots are in development, destined for heroic feats such as shimmying through shattered plumbing to find earthquake survivors. But the most useful job for such robots could be patrolling thousands of miles of leaking municipal water lines. One design group took the inspiration for its bloblike robot from amoebas, but most of the new bots resemble snakes. A Canadian robot called Regina Pipe Crawler (RPC) is nearing commercialization. Controlled remotely, RPC can inspect a bending 6-inch-diameter pipe while the water is flowing at full strength. With enough pipe bots on the job, engineers could stop wasteful leaks and prevent catastrophic failures.
Star Trek-style teleporters will never, ever be invented. And that's okay--after all, who would agree to be obliterated and then reconstituted by a guy named Scotty, trusting that no atom or eyeball was out of place? But scientists at the University of Maryland have teleported data, swapping the quantum states of two atoms positioned a meter apart. It was a step toward the creation of quantum computers, which could perform many simultaneous operations, crunching data exponentially faster than today's systems. Real-World Potential: On a rudimentary level, the technology works now, but practical (let alone world-changing) quantum computing is decades in the future.
This past March, a 154-foot-wide asteroid came within 48,800 miles of Earth, just twice the altitude of some satellites. It was big enough to destroy a city. No one saw it coming. Not that it would have helped: There's no procedure in place for deflecting space rocks, just a list of concepts. But two astronauts--Rusty Schweickart, chairman of the Association of Space Explorers-Near Earth Object Committee, and Thomas D. Jones, a PM contributing editor--have a plan.
(1) Build more asteroid-hunting telescopes. Projects that need more funding include a Canadian space-based telescope and a series of ground-based systems in Hawaii.
(2) Assign asteroid-deflection authority to an international committee. With no one in charge, individual nations might launch Pyrrhic schemes: "In the past, there was a Russian proposal to have a 50-megaton nuclear missile in the silo, ready to launch at any asteroid that shows up," Jones says. Bad idea--it could create a lethal storm of fragments.
(3) Run a rehearsal mission. One plan is to park an unmanned spacecraft alongside the offending asteroid, while kinetic impactors--guided missiles minus the warheads--slam into it. The first craft helps the impactors target the object and acts as a gravity tractor, using its mass to nudge the rock off course. If something really huge heads our way, we could always resort to that 50-megaton nuke tactic--with luck the bomb would ignite gases in the asteroid that would spew outwards and nudge the rock from its apocalyptic path.


If you have ever applied for a job before you’ve likely encountered this requirement: critical thinking skills.
Throughout our day-to-day lives, we are constantly presented with choices that we need to make. Should I hit the snooze button? Should I wear a tie or not? Should I ask for a raise at work?
All these choices make us stop for a moment to evaluate our options. If I hit the snooze button, then I’ll get more sleep but might be late for work. If I don’t hit the snooze button I might be tired for work, but at least I’ll show up on time. This deconstruction of weighing the pros and cons is what critical thinking is all about.
According to the University of Toronto, critical thinking is the practice of using a number of different advanced thinking techniques in a variety of complex ways.
Obviously, this can sound like a fairly vague definition. In its most basic sense, critical thinking involves gathering massive amounts of information, systematically analyzing that information, and making rational and informed conclusions.
To go into more detail, we split critical thinking skills into three general components:
- it focuses on how things are proven or presented,
- it involves reflection on our decisions and the process,
- and it is discipline specific.
How is critical thinking different than regular thinking?
To examine the difference between these two thinking techniques, we need to look at three things:
- what we are focusing on,
- how do we do it,
- and what’s the ultimate goal.
With regular thinking, we focus on the facts at hand. For example, it’s 7:30 am, I’m going to be late for work.
Next, we attempt to construct relationships between different ideas and develop inferences based on those relationships.
Finally, we form a plan of action for what we are thinking about.
When it comes to critical thinking skills, the main idea is that the regular thinking process is undertaken in much more detail. We focus on different points of views or opinions and the merits of each.
Next, we examine the relationships in depth. We must evaluate not only other people’s methods of thinking, but also our own.
Finally, we use the material we have assessed to make an informed decision about what we have been thinking about, and how we thought about it.
In a sense, we are thinking about thinking.
Simple enough right?
Well, without further ado, here are 10 sure-fire ways to improve your critical thinking skills.
1. Know what question you want to ask
Before thinking about any idea critically, you want to know what question you are trying to ask.
You must approach the question with an open mind and understand the reason why you want this particular problem solved. To improve your critical thinking skills, you must examine the question from a logical standpoint, not an emotional one.
2. Be self-aware
One of the most important characteristics of people who think critically is that they are self-aware. They know that they aren’t always right.
Critical thinkers are open to the views and opinions of others and will take their input into consideration with the same weight as their own.
3. Act with integrity
Again, we are trying to improve our thinking skills, not our ability to always be right.
To be a productive thinker, one must act honestly and with integrity. It’s only by acting with integrity that eventually we can come to a rational and logical conclusion.
4. Ask simple questions
Going back to tip #1, the question you want to ask doesn’t need to be profoundly difficult. Does every earthly problem require a drawn out and elaborate thinking process?
Sometimes when we overthink things, the original question gets lost in the quagmire.
To combat this, break the overall question into smaller ones: what do I currently know about the problem? How did I come to know this information? What am I trying to solve?
5. Don’t assume things
Assuming makes an *** out of you and me. You know the old saying. Even if something is globally assumed, you should question it.
Way back in the day people assumed the Earth was flat. However, because critical thinkers don’t assume things, they analyzed the data and came to know that the Earth was a sphere.
6. Swap relationships
For example, let’s just say that excessive video game use causes us to smoke. Instead of looking at relationships from one point of view, try swapping them. Does smoking cause excessive video game use?
Although this example is merely hypothetical, switching variables in relationships allows to deconstruct these relationships and make more informed decisions.
7. Gather the information you’re presented with and evaluate it without bias
Tip #2 tells us that to be a critical thinker we must be self-aware. Aware that other people’s opinions are just as important as our own. Therefore, we need to take the information they present to us and evaluate it in the same way that we evaluate our own.
For example, if someone told you about the relationship between video games and smoking, you should ask yourself how they got this information and why.
This is the main concept behind the media reporting on a new scientific study. Every day the media tells us that some new study shows how X causes Y. But, as all scientists know, correlation does not prove causation. We need to examine who conducted the study, how they conducted it, and why they conducted it.
8. Don’t solely rely on others
Although critical thinking requires intense levels of research and analysis, don’t sell yourself short. Even if you are not an expert in the question you want answered, you should never discount your own views and ideas. Sure, you might not be an expert on Quantum Entanglement, but always include your own thoughts (however limited they may be) in the thinking process.
9. Combine all the information gathered from tips #1-#8
You’ve been open-minded, you sought others advice, you were unbiased, and you didn’t make assumptions. Now you need to combine all of this information to make a conclusion.
You have all your deconstructed ideas and opinions and now need to weigh the implications of each decision. In other words, you’re examining the pros and cons of one decision vs. the other.
You’ve done your research on Quantum Entanglement so now it’s time to decide if you are for it, or against it. Weigh the pros and the cons, examine the implications of your choice, and arrive at a logical conclusion.
10. Don’t try and think critically exclusively
Critical thinking involves massive amounts of research, information processing, and analysis. Obviously, you can’t think this way all the time. You would never get anything done!
Should you hit the snooze button? “Well, let’s examine my own rationale and the views of my co-workers, and then conduct extensive literature research on the relationship between sleeping and work productivity”.
By the time you thought about this decision critically, you already missed a full day of work and the point is moot. Save your critical thinking skills for the important decisions in life. Like that honors thesis or your investment strategy.
There you have it, 10 sure-fire ways to improve your critical thinking skills.
When it comes to improving thinking skills, the jargon can get fairly wordy and complicated. If this all seems confusing, the best course of action would be to think critically about critical thinking!
Okay, maybe that didn’t lessen the confusion.
Regardless, if you want to make informed and sound decisions in life, critical thinking is your friend. It is in your best interests to learn these tips, apply them, and get thinking about thinking!


Break RSA encryption with this one weird trick
Cryptographers HATE it!
Too much math; didn’t read — Shor’s algorithm doesn’t brute force the entire key by trying factors until it finds one, but instead uses the quantum computer to find the period of a function which contains the RSA key and classically computes the greatest common divisor.
RSA encryption is strong because factoring is a one-way problem. It’s very easy to multiply two primes together, but very difficult to find prime factors of a large number. That’s what the technology relies on. And the simplicity of RSA encryption made it very popular.
However, one technology can render RSA useless.
(Hint: it’s a quantum computer)
Shor’s algorithm can crack RSA. But how does it really work?
It’s not about trying all prime factor possibilities simultaneously.
In (relatively) simple language: We can crack RSA if we have a fast way of finding the period of a known periodic function f(x) = m^x (mod N)
Five Steps of Shor
So how does Shor’s algorithm work? In the five steps of Shor’s algorithm, only ONE requires the use of a quantum computer. The other steps can be solved classically.
Step 1: use the classical greatest common divisor (gcd) algorithm on N and m, where N is the number you are trying to factor, and m is a random positive integer less than N.
If the gcd(m, N) = 1, continue. If you find a factor using gcd, you’ve found a non-trivial factor and are done.
Step 2: find the period P of:
m mod N, m^2 mod N, m^3 mod N
This is the quantum step
Step 3: if the period P is odd, go back to step 1 and choose another random integer. Otherwise, continue
Step 4: check that
If that is true, go to Step 5
Otherwise, go back to Step 1
Step 5: Solve
The answer is a non-trivial prime factor of N, and you now have the key to break RSA.
How does Step 2 work?
But how does a quantum computer find the period of the function, as in step 2? And why is this important?
We are looking for the phase (period P) of
m mod N, m^2 mod N, m^3 mod N
(While this is an exponential function, we can transform a complex exponential into hyperbolic sin and cos and get a periodicity)
This period finding step relies on the quantum superposition. With a quantum computer and its ability to be in a superposition of states, we can find the period of the function. To do so, we:
1. Apply the Hadamard gate to create a quantum superposition
2. Implement the function into a quantum transform
3. Perform the quantum Fourier transform.
Like it’s classical analog, after these transformations, a measurement will yield an approximation to the period of the function (you can read the ‘peak’, like in the classical Fourier transform, with a high probability). Using the quantum Fourier transform, we can solve the order-finding problem and factoring problem, which are equivalent. The quantum Fourier transform allows a quantum computer to perform phase estimation (the approximation of eigenvalues of a unitary operator).
As you exit the quantum portion (step 2), you check the period for validity and use another classical greatest common divisor algorithm to get the prime factor of the key.
Interestingly enough, since the technique is not about trying all the potential prime factors, just the potential periods, you do not have to try many random numbers to successfully find a prime factor of N. The probability that P is odd, and you have to return to step one, is
where k is the number of distinct prime factors of N. So even if you double the key length (N), there will not be a slowdown in finding the factors. RSA is not secure and doubling key size will not help in achieving a level of safety against a quantum adversary.
The RSA-2048 Challenge Problem would take 1 billion years with a classical computer. A quantum computer could do it in 100 seconds
–Dr. Krysta Svore, Microsoft Research
The quantum Fourier transform is applied to a quantum circuit built just out of 1 qubit and 2 qubit gates, making the physical implementation of Shor’s algorithm one of the easiest tasks for a quantum computer.
The quantum Fourier transform is the key to many of these quantum algorithms. It doesn’t speed up finding classical Fourier transforms, but can perform a Fourier transform on a quantum amplitude. It is exponentially faster to solve the quantum Fourier transfer on a quantum computer. Though there are subtleties beyond directly mapping classical Fourier transform problems, a quantum computer can also, for example, solve the hidden subgroup problem, which solves the discrete logarithm problem, or counting solutions, which crack other forms of modern cryptography. More importantly, the quantum Fourier transform can be applied to machine learning, chemistry, materials science, and, obviously, simulating quantum systems.
At the core of Shor’s factoring algorithm is order finding, which can be reduced to the Abelian hidden subgroup problem, which is solved using the quantum Fourier transform.
— NIST Quantum Zoo
Just one of the steps of Shor’s algorithm needs to be implemented on a quantum computer, while the rest can be done on a classical supercomputer. The quantum subroutine will be performed and fed back to the supercomputer to continue the calculation. A quantum computer will likely never be a standalone system, but together with a supercomputer, the time to break an RSA key will be quite reasonable.
There are a lot of mathematical details that have been glossed over, as well as the proofs of these steps as it is beyond the scope of this article. If you’re curious about the mathematical explanations, with intense linear algebra, group theory, and higher level mathematics, check out these sources:
NIST Quantum Zoo — http://math.nist.gov/quantum/zoo/ — a list of all the quantum algorithms


Generation Z (or the iGeneration), who are in school and university today, have never witnessed the introduction of the Internet, smart phones and tablets, video games, on-demand TV and film, and social media. For them, this is all very much the norm and part of their everyday lives. The skills they are developing interacting with technology every day, will be essential as they step out into an adult world of emerging technology breakthroughs in AI, robotics, quantum computing, biotechnology and so on.
Education has been late off the technology starting-blocks, but is now learning how to harness technology to support learning and teaching. Teachers are learning from each other how to successfully use technology as a teaching and learning tool, by sharing knowledge and outcomes with each other, both on a micro-level within their own schools and local communities, and on a macro-level through the Internet.
As well as supporting teaching and learning, most educators across the globe will agree that assessment is another key aspect of schooling that technology has a phenomenal potential to enhance. The world is changing and changing fast, and the challenge for educators across the globe is to ensure student assessment remains fit for purpose and relevant in the digital age. To succeed in an increasingly connected world, students need the right attitudes, adaptable skills and cultural sensitivity – and assessment needs to be able to evaluate and help foster these skills in learners. By harnessing the digital tools around us, educators can better assess students’ abilities; identify their strongest and weakest skills; and encourage them to demonstrate their whole skillset – all the while moving away from traditional, memory-based, and often stressful, examination methods.
Combining traditional academic merit and contemporary digital innovation, in 2016 the International Baccalaureate (IB) introduced eAssessment to its Middle Years Programme (MYP). MYP eAssessment is designed to focus on scenarios in which students must use knowledge and skills to analyse unfamiliar situations, thus challenging them to connect what they have learned with what they might learn next, and apply big ideas to solve real-world problems. This has traditionally been harder to achieve using paper-based examinations but, with technology, more is possible.
Different types of tasks are used within the on-screen examinations to test specific skills, meaning that students’ achievement against all subject objectives are thoroughly tested. For example, writing a short static essay assesses writing capability, whilst creating an infographic assesses interactive communication and presentation skills. With the use of images, videos, animations and models, and through interactive tools, candidates can create, manipulate and make decisions about how to manage data. On-screen tools can help students who aren’t working in their first language too, and built-in adaptive technologies can ensure that the eAssessment is open to students with access and inclusion needs, providing all participants with the best opportunity possible to demonstrate their knowledge, skills and abilities.
Commenting on the MYP eAssessment, IB Co-ordinator Jaya Kalsy from Victorious Kidss Educares in India, said: “Students are encouraged to utilize online learning tools, as well as exploring digital methods to present their work. Digital methods have become the window to the world. There is a sense of encouragement and advancement in the process of schooling as well as the assessment process.”
Increasingly, it is recognised that technology can add real value for students, and MYP schools offering the eAssessment say that it has enabled them to participate in exciting innovation in education. Feedback from schools, via an IB survey conducted with educators all over the globe, illustrates the natural connection between eAssessment and what is being taught and learnt in the classroom.
Schools have said that, through digital assessment, they are able to assess skills, concepts and thinking, in context, rather than knowledge recall. From our research, it is rewarding to see that schools understand that eAssessment supports conceptual teaching and learning, and is not something that can be crammed for – only good MYP practice supports good preparation. Unlike other education environments, in the MYP educators do not teach to test, but their teaching does align with assessment requirements and this brings enrichment and focus to their students’ learning.
MYP eAssessment is still relatively new but the results of the first four years clearly demonstrate an encouraging upward trend, which shows that teachers are able to connect what’s happening in their classrooms to what’s happening in the on-screen assessments, examining students’ higher thinking skills and pushing them well beyond the rote memorisation of subject-specific content.
In 2018, the MYP eAssessment was successfully recognised in the ‘Best assessment solution’ category at the ScooNews Global Education Awards in Udaipur, India as well as winning the ‘Best use of summative assessment’ award in the eAssessment Awards in London.
For more information about the IB and the MYP, please visit www.ibo.org .
The above article is authored by Eleonore Kromhout, Senior Manager Assessment Development and Delivery, International Baccalaureate


Today’s computing systems, although having significantly improved decade after decade, can only solve problems up to a certain size and complexity. More complex issues require advanced computational power, and quantum computing promises to deliver such power.
Classical computers rely on individual bits to store and process information as binary 0 and 1 states. Quantum computers rely on quantum bits – qubits – to process information; in doing so, they use two key quantum mechanical properties: superposition and entanglement.
Superposition is the ability of a quantum system to be in multiple states at the same time. Qubits still use the binary 0 and 1 system, but the superposition property allows them to represent a 0, a 1, or both at the same time. Instead of analysing 0s and 1s sequence by sequence, two qubits in superposition can represent four scenarios at the same time, thus reducing the time needed to process a data set.
Entanglement is a strong correlation between quantum particles, allowing them to be inextricably linked in perfect unison, even if separated by great distances. When two qubits are entangled, there is a special connection between them: If the individual qubits are measured, the outcome of the measurements could be 0 or 1; but the outcome of the measurement on one qubit will always be correlated to the measurement on the other qubit. And this is always the case, even if the particles are separated from each other by a large distance.
In essence, superposition allows quantum computers to solve some problems exponentially faster than classical computers, while entanglement makes quantum computers significantly more powerful.
Qubits can be created through different methods, such as using superconductivity to create and maintain a quantum state. Superconductivity requires low temperatures, which is why quantum computers need to be kept cold to maintain their stability.
One main problem with qubits is that they are very tricky to manipulate: Any disturbance makes them fall out of their quantum state or ‘decohere’. Significant research is being carried out on identifying ways to overcome this decoherence problem and make qubits co-operate.
While quantum computers can work with classical algorithms, quantum algorithms are obviously more appropriate as they can solve some problems faster. One example of a quantum algorithm is Grover’s algorithm, which can search through an unstructured database or unordered list significantly faster than any classical algorithm.
It is important to note that problems fundamentally unsolvable by classical algorithms (called undecidable class problems) cannot be solved by quantum algorithms either.
Applications of quantum computing
The unprecedented power of quantum computers makes them useful in many scenarios where classical computers would require an impractical amount of time to solve a problem. For example, they could simulate quantum systems, allowing scientists to study in detail the interactions between atoms and molecules. This, in turn, could help in the design of new materials (e.g. electronics, chemical materials) or new medicines. As they are significantly faster than classical computers, quantum computers will also be far more efficient at searching through a space of potential solutions for the best solution to a given problem.
Quantum computers can thus pave the way for unparalleled innovations in medicine and healthcare, allowing for the discovery of new medications to save lives or of new AI methods to diagnose diseases. They can also support the discovery of new materials, the development of enhanced cybersecurity methods, the elaboration of much more efficient traffic control and weather forecasting systems, and more.
Researchers around the world are working on and with quantum technology in various fields. Airbus has launched a quantum computing challenge to encourage the development of quantum solutions in aircraft climb and loading optimisation, as well as wingbox design optimisation. Daimler is working with Google on using quantum computing in the fields of materials science and quantum chemical simulation. The US Department of Energy is funding research projects that could lead to the development of very sensitive sensors (with applications in medicine, national security, and science) and provide insights into cosmic phenomena such as dark matter and black holes.
Google, IBM, Intel, Microsoft, and other major tech companies are allocating significant resources to quantum computing research, in their efforts to pioneer breakthroughs in areas such as AI and machine learning, medicine, materials, chemistry, supply chains and logistics, financial services, astrophysics, and others.
Quantum communication and cryptography
Beyond powerful quantum computers, quantum technology has applications in other areas too, such as quantum cryptography and quantum communication, both of which are closely interlinked.
Quantum cryptography is a method used for the secured, encrypted transfer of information. Unlike other forms of cryptography, it ensures security by the laws of physics; it is not dependent on mathematical algorithms and unsecure exchanges of keys. Quantum communication based on quantum cryptography currently qualifies as highly secure, making it impossible to wiretap or intercept. Here, the most well known application is quantum key distribution (QKD), which relies on the use of quantum mechanical effects to perform cryptographic tasks.
One possible means of quantum communication is quantum teleportation. Although the name can be misleading, quantum teleportation is not a form of the transport of physical objects but a form of communication. This teleportation is the process of transporting a qubit from one location to another without having to transport the physical particle to which that qubit is attached. Even quantum teleportation depends on the traditional communication network, making it impossible to exceed the speed of light.
Quantum computers already exist, but their power is still rather limited and several tech companies are continuously working on improving this power. For instance, in January 2019, IBM announced its first commercial quantum computer that can work outside the research lab, but with a power of only 20 qubits. Later on, in October 2019, the company's engineers announced the development of a 53-qubit computer. In another example, the startup Rigetti Computing developed a 32-qubit computer and is now working on a 128-qubit one too.
In October 2019, Google claimed that it achieved ‘quantum supremacy’ with a 53-qubit quantum computing chip that took 200 seconds to carry out a specific calculation which would have taken a classical computer 10 000 years to complete. IBM soon challenged that claim, arguing that the problem solved by Google’s computer could also be solved in just 2.5 days through a different classical technique. We can expect these and other companies to discover further improvements in processing power, allowing quantum computers to solve problems that classical computers cannot.
While this race is ongoing, the hype around this technology should also be looked at with a degree of caution. As the Massachusetts Institute of Technology (MIT) explains, quantum supremacy is an ‘elusive concept’. First of all, we are still far from quantum computers that can do significant work; Wired magazine estimates that at least thousands of qubits would be required for fully functional quantum computers to solve real-life problems (current quantum computers that operate with less than 100 qubits are far from such a reality).
In addition, quantum computers are prone to many more errors than classical computers and, as already explained, the risk of decoherence makes it very difficult to maintain the quantum nature of qubits. The more qubits a quantum computer has, the more difficult it is to overcome such challenges. Moreover, a quantum computer cannot simply speed up the process of solving any task given to it; scientists explain that, for certain calculations, a quantum computer can be even slower than a classical one. Plus, only a limited number of algorithms have been developed so far where a quantum computer would clearly have supremacy over a classical computer.
Governmental initiatives and policy issues
The promises that quantum computing holds also make it the subject of an ongoing ‘race for supremacy’ not only among tech companies, but among nations too. The USA and China are currently at the forefront, while the EU, Japan, and others are following closely.
In the USA, the National Quantum Initiative Act was adopted in December 2018, setting up a ‘federal programme to accelerate quantum research and development for the economic and national security of the United States’. The Act enables the allocation of over US$1 billion to support the research and development (R&D) of quantum technologies, including quantum computing. In March 2019, the White House Office of Science and Technology Policy created a National Quantum Coordination Office to ‘work with federal agencies in developing and maintaining quantum programmes, connecting with stakeholders, [and] enabling access and use of R&D infrastructure’. And in August 2019, President Trump adopted an executive order establishing the National Quantum Initiative Advisory Committee.
China, on the other hand, is allocating substantial financial resources to university-based quantum research centres and is planning to open a National Laboratory for Quantum Information Science in 2020 (with an investment of around US$1 billion). On the R&D side, researchers have built a satellite that can send quantum-encrypted messages between distant locations, and a terrestrial ultra-secure network between Beijing and Shanghai that allows for the transmission of sensitive data with the help of quantum-encrypted keys.
Beyond this ‘race for supremacy’, progress in quantum computing is also paving the way to new policy issues. For example, one immediate concern is that quantum computers could be used to break encryption systems that are utilised nowadays to secure online banking and shopping, for example. While quantum processors do not yet have such power, the potential is real and governments and companies have started to look into this issue.
It is also likely that regulatory and ethical issues will emerge related to the use of the technology: How to ensure that quantum computing will be used for social good? Similar to the ongoing discussions regarding ethics and AI, will there be a need to implement ethical principles in the development of applications based on quantum computing?


Nothing is more frustrating than watching that circle spinning in the centre of your screen, while you wait for your computer to load a programme or access the data you need.
Now a team from the Universities of Sheffield and Leeds may have found the answer to faster computing: sound. The research – published in Applied Physics Letters – has shown that certain types of sound waves can move data quickly, using minimal power.
The world’s 2.7 zettabytes (2.7 followed by 21 zeros) of data are mostly held on hard disk drives: magnetic disks that work like miniaturised record players, with the data read by sensors that scan over the disk’s surface as it spins. But because this involves moving parts, there are limits on how fast it can operate.
For computers to run faster, we need to create “solid-state” drives that eliminate the need for moving parts – essentially making the data move, not the device on which it’s stored. Flash-based solid-state disk drives have achieved this, and store information electrically rather than magnetically. However, while they operate much faster than normal hard disks, they last much less time before becoming unreliable, are much more expensive and still run much slower than other parts of a modern computer – limiting total speed.
Creating a magnetic solid-state drive could overcome all of these problems. One solution being developed is ‘racetrack memory’, which uses tiny magnetic wires, each one hundreds of times thinner than a human hair, down which magnetic “bits” of data run like racing cars around a track. Existing research into racetrack memory has focused on using magnetic fields or electric currents to move the data bits down the wires. However, both these options create heat and reduce power efficiency, which will limit battery life, increase energy bills and CO2 emissions.
Dr Tom Hayward from the University of Sheffield and Professor John Cunningham from the University of Leeds have together come up with a completely new solution: passing sound waves across the surface on which the wires are fixed. They also found that the direction of data flow depends on the pitch of the sound generated – in effect they “sang” to the data to move it.
The sound used is in the form of surface acoustic waves – the same as the most destructive wave that can emanate from an earthquake. Although already harnessed for use in electronics and other areas of engineering, this is the first time surface acoustic waves have been applied to a data storage system.
The Latest on: Faster computing
via Google News
The Latest on: Faster computing
- When Is It Better to Restart vs. Shut Down Your Computer?on March 23, 2020 at 8:10 pm
However, since Windows 8, a new feature called Fast Startup has altered this considerably.” How has that changed things, exactly? “Shutting down a Windows computer actually creates a deep hibernation ...
- The New Xbox: Just How Fast Is 12 TeraFLOPS?on March 23, 2020 at 10:16 am
Comparing the Series X to contemporary computer hardware, performance is on par with NVIDIA’s current flagship ... Advances in hardware have come thick and fast, and the roughly half-decade lives of ...
- Coronavirus researchers to get fast-track access to Irish supercomputeron March 23, 2020 at 5:04 am
All content copyright 2002-2020 Silicon Republic Knowledge & Events Management Ltd. Reproduction without explicit permission is prohibited. All rights reserved. Designed by Zero-G and Square1.io The ...
- [email protected] Now Faster Than World’s Top 7 Supercomputers Combined
- Coronavirus or computer virus – both are spreading fast, which one to prevent?on March 23, 2020 at 3:20 am
Both on high priority! So vulnerable we humans are! Today, we stand humbled in front of a tiny microorganism and so are our computers, always at the risk of a virus attack. Exposure to a virus can ...
- Coronavirus And [email protected]; More On How Your Computer Helps Medical Researchon March 22, 2020 at 7:09 am
Can I Make Sure My Computer Only Works on the COVID-19 Problem? No, but you don’t need to since the group is already prioritizing the coronavirus effort. Although the software does offer the option to ...
- Chip-based device opens new doors for augmented reality and quantum computingon March 22, 2020 at 7:00 am
Trapped ion quantum computers are among the most promising practical designs for quantum computing, an emerging technology expected to be significantly faster than traditional computing. The new ...
- Apple's 2020 iPad Pro: Finally, the Laptop Is Dead? Not So Fast.on March 22, 2020 at 5:27 am
Not So Fast. The launch of the 2020 iPad Pro coincides with the imminent release of iPadOS ... s ongoing attempts to fully realize the iPad’s potential as a productivity platform. The iPad Pro is now ...
- Amazon Web Services offers $20 million worth of credits and technical support for customers working on faster COVID-19 testingon March 20, 2020 at 10:03 am
Amazon Web Services will offer $20 million worth of credits and technical support to customers who are working to develop faster COVID-19 testing.
- How Fast Does a Virus Spread? Let’s Do the Mathon March 20, 2020 at 6:00 am
How far and how fast will the Covid-19 pandemic spread ... Here's what that looks like: So, how do you tell if something is exponential? You could use a computer to fit an exponential function to the ...
via Bing News


The definition of the quantum computer is quite simple. It is a computer that exploits the laws of physics and quantum mechanics for data processing using the qubit as a fundamental unit. Unlike electronic calculation, at the base of computers as we have always known them, whose fundamental unit is the bit!
In particular, quantum bits have some properties that derive from the laws of quantum physics such as:
- The superposition of states (they can be 0 and 1 at the same time) due to which parallel rather than sequential calculations can be made as happens today with the computational capacity of "traditional" computers.
- The entanglement that is the correlation (the bond) that exists between one qubit and another, a very important aspect because it has a strong acceleration in the calculation process derives due to the influence that one qubit can produce on another even if they have distance.
- Quantum interference: It is, in fact, the effect of the first principle (the superposition of states); quantum interference allows you to "control" the measurement of qubits based on the wave nature of the particles. The interference represents the superposition of two or more waves that depend on whether there is an overlap or not between grows and bellies. For instance, higher and lower parts of the wave - constructive interference can occur. When crests or bellies coincide and form a wave, which is the sum of the overlapping waves, or destructive interference when overlapping are the crest of a wave and belly of another, in this case, the two waves cancel each other out.
To understand how we got to the quantum computer, we have to go back to the miniaturization of circuits and Moore's Law. From the 1960s onwards, there has been a progressive increase in the computing power of computers, an increase that has gone hand in hand with the miniaturization of the electronic circuits from which it derives the famous Moore's Law. According to this law, “the complexity of a microcircuit, measured with the number of transistors in a chip (processor), and the relative calculation speed doubles every 18 months ".
Following this law - which over time has become a real measurement parameter and also guide of objectives for processor manufacturers - we have come to have integrated microchips, i.e., processors that integrate a CPU, a GPU, and a Digital Signal inside them processing, within our smartphones.
However, a threshold that today has reached the limits of quantum mechanics, making it very complex (almost impossible) to continue the path of miniaturization, together with the increase in the density of transistors. Limit that has actually opened the way to a paradigm shift trying to exploit the laws of physics and quantum mechanics to achieve a computing power higher than that of computers based on electronic calculation without necessarily thinking about the miniaturization of circuits.
The information units that encode two states open and closed (whose values are 1 and 0) of a switch, exploit those that are called qubits. The units of quantum information that are coded not by 1 or 0 but by the quantum state in which a particle or atom is found, which can have both the value 1 and the value 0 at the same time. Moreover, in a variety of combinations that produce different quantum states (a particle can be 70% in state 1 and 30% in state 0, or 40% and 60%, or 15 and 85).
A condition that takes on an incredible meaning when you think of mathematical progression such as 2 qubits can have 4 states simultaneously. For example, a pair of qubits can be in any quantum superposition of 4 states), 3 qubits can be in any 8 state superpositions. And, eight strings of three different bits: 000, 001, 010, 011, 100, 101, 110 and 111), 4 qubits in overlapping 16 states, 8 qubits of 256 states and so on. In a quantum computer, the n qubits can be in any superposition up to 2 to ‘n’ different states.
In fact, atomic and subatomic particles can exist in an overlap of quantum states, a situation that greatly expands the possibilities of encoding information by opening the possibility of exploiting this processing capacity for the resolution of extremely complex problems, such as those underlying the Artificial intelligence.
The critical issues that have so far slowed down the race to develop these systems are related to the controlled manipulation of atoms and particles. It is possible with a few qubits but for complex processing hundreds and thousands of qubits are needed. Their connection and communication, as well as the development of algorithms are suitable for the quantum computer.
The functioning of the quantum computer, as mentioned in the first paragraph of this service) is based on two laws of quantum mechanics:
- The superposition principle from which derives, as we have seen, the possibility for the particles to be simultaneously in several different states. The superposition of states, in quantum physics, represents the simultaneous existence of all possible states of a particle or physical entity before its measurement. Only with the measurement, it is possible to define precisely the property of the qubit, and this is one of the most critical aspects that have not yet made the quantum computer available on a large scale. The particles are unstable, and their measurement is very complex, to which it must be added that the instability of the particles generates heat, which, to date, can only be controlled with advanced cooling systems.
- The quantum correlation (entanglement): It expresses the constraint, the correlation precisely that exists between two particles or two qubits.
According to this principle, it is possible to know the state of a particle (or a qubit) by measuring the other with which it has the constraint.
According to Gartner analysts, applications for quantum computing will be restricted and targeted, as the general-purpose quantum computer - most likely - will fail to be economically accessible on a large scale (at least not in the short term).
However, technology has the potential to revolutionize certain sectors. Quantum calculation could allow discoveries and be applied in many sectors:
- Machine-learning: improved machine learning due to a faster forecasting structure (due to parallel calculation). Examples include quantum Boltzmann machines, semi-supervised learning, unsupervised learning, and deep learning.
- Artificial intelligence: faster calculations could improve the perception, understanding, and diagnosis of circuit faults / binary classifiers.
- Chemistry: New fertilizers, catalysts, battery chemicals will bring enormous improvements in the use of resources;
- Biochemistry: New drugs, customized drugs, personalized medicine.
- Finance: the quantum calculation could allow the so-called faster and more complex "Monte Carlo simulations"; for example in the field of trading, optimization of "trajectories," market instability, price optimization, and hedging strategies.
- Medicine and health: DNA gene sequencing, such as optimization of radiation therapy treatment/brain tumor detection, could be done in seconds rather than hours or weeks.
- Materials: super-resistant materials; anti-corrosive paints, lubricants, semiconductors, the research could be greatly accelerated due to super-fast calculations.


For decades, scientists have used techniques such as X-ray crystallography and nuclear magnetic resonance (NMR) imaging to gain invaluable insight into the atomic structure of molecules. Such efforts have long been hampered by the fact that they demand large quantities of a specific molecule, often in ordered and crystalized form, to be effective — making it all but impossible to peer into the structure of most molecules.
Harvard researchers say those problems may soon be a thing of the past.
A team of scientists, led by Professor of Physics and of Applied Physics Amir Yacoby, has developed a magnetic resonance imaging (MRI) system that can produce nanoscale images, and may one day allow researchers to peer into the atomic structure of individual molecules. Their work is described in a March 23 paper in Nature Nanotechnology.
“What we’ve demonstrated in this new paper is the ability to get very high spatial resolution, and a fully operational MRI technology,” Yacoby said. “This work is directed toward obtaining detailed information on molecular structure. If we can image a single molecule and identify that there is a hydrogen atom here and a carbon there … we can obtain information about the structure of many molecules that cannot be imaged by any other technique today.”
Though not yet precise enough to capture atomic-scale images of a single molecule, the system already has been used to capture images of single electron spins. As the system is refined, Yacoby said he expects it eventually will be precise enough to peer into the structure of molecules.
While the system designed by Yacoby and colleagues operates in much the same way conventional MRIs do, the similarities end there.
“What we’ve done, essentially, is to take a conventional MRI and miniaturize it,” Yacoby said. “Functionally, it operates in the same way, but in doing that, we’ve had to change some of the components, and that has enabled us to achieve far greater resolution than conventional systems.”
Yacoby said that while conventional systems can achieve resolutions of less than a millimeter, they are effectively limited by the magnetic field gradient they can produce. Since those gradients fade dramatically within just feet, conventional systems built around massive magnets are designed to create a field large enough to image an object — like a human — that may be a meter or more in length.
The nanoscale system devised by Yacoby and colleagues, by comparison, uses a magnet that’s just 20 nanometers in diameter — about 300 times smaller than a red blood cell — but is able to generate a magnetic field gradient 100,000 times larger than even the most powerful conventional systems.
The difference, Yacoby explained, is that the nanoscale magnet can be brought incredibly close, within a few billionths of a meter, to the object being imaged.
“By doing that, we can achieve spatial resolution that’s far better than one nanometer,” he said.
The departures from conventional MRI systems, however, didn’t end there.
To construct a sensor that could read how molecules react to that magnetic field gradient, Yacoby and colleagues turned to a field that would appear to be unconnected to imaging — quantum computing.
Using ultra-pure, lab-grown diamonds, the team milled tiny devices, each of which ended in a super-fine tip, and embedded an atomic-scale impurity, called a nitrogen-vacancy (NV) center in each tip, creating a single quantum bit, or qubit — the essential building block of all quantum computers.
In experiments published last year, Yacoby and his collaborators showed that as the tip was scanned across the surface of a diamond crystal, the quantum bit interacted with electron spins near the crystal’s surface. Those interactions could then be used to create an image of individual electron spins. However, while the sensitivity of the quantum bit sensor is sufficient to detect individual electron spins and represents a quantum leap forward from earlier efforts, its spatial resolution is limited by its distance from the object that is being imaged.
To create truly 3-D images, Yacoby and colleagues combined the quantum-bit sensing approach with the large-field gradient by bringing the nanomagnet in close proximity to both the sample of interest and the qubit sensor. By scanning the magnet in 3-D, but very close to the sample, they were able to detect individual electron spins as they reacted to the magnetic field.
“This is really a game of bringing both the magnet very close to generate large gradients, and bringing the detector very close to get larger signals,” Yacoby said. “It’s that combination that gives us both the spatial resolution and the detectability.
“Our current system is already capable of imaging individual electron spins with sub-nm [subnanometer] resolution,” he said. “The goal, eventually, is to put a molecule in proximity to our NV center to try to see the components within that molecule, namely the nuclear spins of the individual atoms composing it. This is by no means an easy task, since the nuclear spin generates a signal that is 1,000 times smaller than that of the electron spin … but that’s where we’re headed.”


Diamonds have always been considered among the rarest of gems. They stand for innocence and elegance. But did you know that diamonds will soon be a good replacement for silicon? It is possible due to the presence of a special atomic impurity in each diamond known as “doping.”
Silicon is a widely used semiconductor. However, it has a very high melting point and sluggish performance. However, the uses of this gem are not restricted to that alone. A new application of the diamond is being discovered in the field of semiconductor manufacturing – the diamond sensor. This unique material can detect impurities and defects in the material used in semiconductors.
Quantum diamond is the most innovative diamond sensor. It has numerous benefits in comparison to the current diamond sensor technologies. As a result, it is a very lucrative diamond sensor technology used in many labs worldwide. Let’s check out what a quantum diamond sensor is.
What Is a Quantum Diamond Sensor?
A quantum diamond sensor is a sensor that exists at the atomic level. It is a device that uses diamonds to measure the electrons in a specific material at a particular time. The device works by placing the diamond sensor in a vacuum chamber and applying a static magnetic field.
The name “quantum diamond sensor” is derived from the element diamonds being used, giving off light when electrons within the diamond crystals transition from a higher energy level to a lower one. The sensor is also called a diamond nanocrystal, or DNC.
The sensor is not only embedded with the power to detect, but it is also very sensitive to changes. It is an amazing discovery that can revolutionize the electronics industry. The quantum diamond sensor also identifies the grade of the diamond and the proportion of flaws.
With the technology of the quantum diamond sensor, you can purchase a higher quality diamond at a lower cost than buying a lower quality diamond at a higher price.
Quantum diamonds are created in colder temperatures than lab grown gems using a different process. These diamonds typically have fewer impurities and more vibrant colors than their lab grown cousins.
So what is a lab grown diamond? Lab grown diamonds are man-made diamonds grown in a lab. They are made by melting carbon and turning it into the diamond’s crystal structure through high pressure and heat. These diamonds are mined in a controlled lab setting. This cost-effective, green alternative to mined diamonds is an environmentally friendly option that is irreversible, renewable, and non-polluting.
How does a Quantum Diamond Sensor Work?
A quantum sensor is a type of sensor that can read the vibrations on the pressure plate. It completes a circuit that changes the color of the liquid crystal display. The entire device is about the size of your hand, and the pressure plate is about half the size of the machine.
The quantum diamond sensor works by bouncing a laser beam off the diamond and then measuring the time it takes for the light to bounce back. This determines the size and shape of the diamond.
The whole device is connected to the pressure plate. The device needs to be cooled down at that temperature to reduce the thermal energy of the electrons and atoms. It will allow the spin of the electrons to remain unchanged when the magnetic field is applied. The device consists of a series of diamond layers.
What makes this method so fascinating is that it can reveal the different impurities in the diamond, giving the owner a better idea of what type of diamond they are buying.
What are the Applications of Quantum Diamond Sensors?
Quantum diamond sensors have a wide range of applications in many different scientific disciplines. One use of quantum diamond sensors is detecting trace amounts of toxic gasses. The device uses a modified version of a technology known as Surface Enhanced Raman Spectroscopy (SERS).
It detects trace amounts of gasses without being affected by other environmental gasses, such as oxygen. SERS is a technique that utilizes the surface of a diamond to concentrate and amplify a signal. Such as pressure or force, allowing users to detect atomic or molecular structures.
Another application of quantum diamond sensors is in the detection of radioactive material. Quantum diamond sensors are an excellent material for this purpose due to their atomic structure.
They can distinguish between different atoms and detect the presence of these substances. Diamond sensors can also be used in many other fields, such as studying photovoltaic cells, magnetic field strength, magnetoresistance, and more.
Quantum diamond sensors are used to study diamond films, which are films that are grown on a substrate to create different diamond structures. For example, a diamond film that is just a single diamond layer deposited on top of the substrate or many layers of diamond films stacked on top of one another could be grown.
This diamond film aims to study how diamond films will change with different amounts of pressure and heat. Quantum diamond sensors can measure that response and have already shown promise in predicting the properties of diamond films, which may be useful in developing realistic diamond films.
Quantum diamond sensors can be used to create quantum computers, which would be far more advanced than their digital counterparts. It is also used in astronomy to detect hidden planets. This sensor can also be placed underwater to see changes in the environment.
It is extremely useful when wanting to detect underwater objects or changes in the water. Overall, the applications of this sensor are endless. You can use it in medical, transportation, and many more.
The diamond sensor is one of the most innovative technologies in today’s modern industries. Quantum sensors are more precise than traditional photoelectric sensors. It can detect objects that are smaller than the wavelength of light. They can see various materials, including organic, inorganic, and semi-organic objects.
Quantum diamond sensor is becoming a new standard in the measurement of quantum entanglement. With quantum entanglement, there is a mysterious connection between two quantum objects. When quantum diamonds are entangled, there are many possible uses.
It can help in teleportation, increasing computing power, and even communication between objects that are far away. Also, quantum diamonds are much smaller and can store more information, which will make their use in quantum computing a vital step in the future.


The simple answer to this question is no — within present quantum technology we are unable to build one with sufficient power to replace ordinary computers. In order to build a quantum computer you need particles that can behave like qubits, that is, the quantum analogue of the bits used by a classical computer. Qubits must be able to represent the values 0 and 1, but crucially, they must also be able to exist in a superposition of the two (see this article to find out more about how quantum computers work). The idea is to embody those qubits in particles — photons, electrons, atoms; researchers are working on a host of possibilities. And they have already succeeded in building quantum devices that use only a small number of qubits.
For an example, think of a beam splitter: a half-silvered mirror that will reflect half of a beam of light that's shone on it and let the other half pass through. Since you can think of light as consisting of particles called photons, you can ask what happens to an individual photon as it hits the surface of the half-mirror. A possible answer is that it's got a 50:50 chance of being transmitted through the mirror or reflected — either one or the other. But that's not actually what happens. When the photon hits the mirror it enters a superposition state of simultaneously being reflected and transmitted.
A beam splitter.
In the image on the right, think of a photon as representing a qubit in state 0 if it is travelling in the vertical direction and a qubit in state 1 if it is travelling in the horizontal direction. Similarly, write 0 if it continues along the vertical path after hitting the beam splitter and 1 if it continues along the horizontal path. The beam splitter therefore turns an input qubit that's in the state 0 or 1 into a qubit that is in superposition of 0 and 1. We won't go further into the details here, but using such beam-splitters and photons that impinge on them it's possible to build quantum gates such as the Hadamard gate We mentioned in this article.
Using a combination of just a few quantum gates, contraptions that are able to process one or two qubits, you can implement any quantum algorithm you can come up with — people have been able to prove this fact mathematically. So why can't we just bang together a few of those "universal" quantum gates to create a quantum computer that can perform tasks involving many qubits? That's exactly what happens in ordinary computing, where combinations of individual logic gates can perform all sorts of computations (see this article for an example).
There are many difficulties involved in getting particles to behave and interact in the qubit way, and especially in building systems of many qubits, which are needed to get the computing benefits. But the most important hurdle faced by quantum computing is the fact that superposition states are delicate: they can only survive for any length of time when the quantum system in which they occur is extremely well-isolated from its environment. If it isn't, then its quantum nature sort of "leaks out" and dissipates in a process called decoherence (the system becomes entangled with its environment). It happens very quickly, and all that's left are states we are used to seeing in real life. It's the difficulty in keeping quantum systems isolated that stops us from building quantum computers that can handle more than just a few qubits.
So when can we expect to have a fully-fledged, uncontroversial and practically useful quantum computer? "There are all sorts of wonderful and exotic things that people try to build quantum computers out of: defects in diamonds, electrons floating on liquid helium, superconducting qubits, and all sorts of amazingly imaginative stuff." says Jozsa. "There is very good progress in all of these things, it's getting better and better all the time. You can't say that [quantum computing] is far off because these developments don't occur incrementally. The transistor for classical computing didn't emerge gradually: one day there wasn't one, but a few weeks later it existed and computing exploded." says Jozsa. He quotes the physicist N. David Mermin, who in his lecture notes on quantum computation states that "Only a rash person would declare that there will be no useful quantum computers by the year 2050, but only a rash person would predict that there will be."
To find out more about quantum computing, read the following articles:
- How does quantum computing work?
- Quantum computing: Some (not so) gruesome details
- What can quantum computers do?
About this article
Marianne Freiberger is Editor of Plus. She would like to thank Richard Jozsa, Leigh Trapnell Professor of Quantum Physics at the University of Cambridge, for his extremely helpful, very patient and generally invaluable explanations.
Thanks for such wonderful insights, I would like to read more about Quantum computing, if you have expanded you research.


phonlamaiphoto - stock.adobe.com
There are some problems that are simply too complex for even the most powerful of today’s computers, and researchers are trying to overcome the limits of traditional computer designs to enable computationally difficult problems to be solved.
The von Neumann architecture, which has defined the layout of computing for the past 75 years, is being pushed in directions that it was never designed to take. This is the classical computing architecture, which effectively defines the way a processor fetches program instructions from memory, runs them and stores values back into memory.
But the stored program architecture described by John von Neumann is less efficient at solving certain complex problem areas, compared with entirely new approaches to computing.
Quantum computing is one of the new approaches to computing that opens up the ability to run calculations that would be impossible to complete on a classical computing architecture. However, quantum computers are currently highly specialised devices. Only recently have scientists been able to demonstrate a device that does not need to be supercooled and kept at near absolute zero (-270°C).
Peter Chapman, CEO and president of IonQ, which recently listed on NYSE, said that when running handwriting recognition, an 11 qubit quantum computer outperformed classical computing and was more accurate in its ability to handle noisy data. “Machine learning is the first application area that will go to quantum computing,” he said. “It is much faster at creating models, and models are better.”
Unlike the classical approach, which needs to be programmed in a way that can compensate for noise in the dataset, “a little bit of noise actually helps”, he said.
What is more, although Moore’s Law has held true for classical computer architectures, where processing power doubles every 18 months to two years, scalability in quantum computing grows exponentially. “We are doubling the number of qubits every 10 months,” said Chapman.
In a machine with n qubits, the computational power is expressed as 2n. In effect, each additional qubit doubles the processing power. To put this into perspective, said Chapman, the number of simultaneous states that a 120-qubit system could handle would be equivalent to the number of atoms in the universe.
Read more about next-generation hardware
- In The Hitchhiker’s Guide to the Galaxy, Deep Thought calculates the answer to the ultimate question of life, the universe and everything as ‘42’. But what of quantum computing?
- In The Terminator, Arnold Schwarzenegger is transported back to 1984. If Arnie had to rely on cloud connectivity, he’d still be walking around naked.
According to Chapman, modelling certain chemical reactions would require the computational power that is only available in the realms of quantum computing. But even in the real world, certain types of optimisations are simply too complicated for classical computing.
There are numerous reports about how the programmers who developed the route optimisation software for logistics firm UPS only used right turns in their calculations. Looking at route optimisation, Chapman said: “What we do today is far from the optimal route as there is a set of cheats that programmers have figured out.”
If an individual driver makes 120 deliveries a day, the number of different permutations of routes is a 200-digit number, he said. Multiply that by the number of drivers and, from a calculations perspective, the problem space quickly become astronomical. “A quantum approach offers a different way to solve the problem,” said Chapman.
IonQ is developing a quantum computer that does not need to be supercooled. According to its roadmap, the company plans to offer a rack-mounted quantum computer by 2023.
Such a system would avoid the latency associated with running quantum computing as a cloud resource, to support applications in high-performance computing that need low-latency connectivity to supercomputers and applications that rely on real-time processing.
It is this idea of taking computing from the cloud towards the edge that is driving Intel’s new-generation Loihi chip architecture for neuromorphic computing. Loihi 2, unveiled at the end of September, is Intel’s second-generation neuromorphic research chip. The company has also released Lava, an open source software framework for developing neuro-inspired applications.
Neuromorphic computing adapts the fundamental properties of neural architectures found in nature to build a new model of computer architecture.
The paper Advanced neuromorphic computing with Loihi describes neuromorphic computing as classes of brain-inspired computation that challenge the von Neumann model. The paper’s authors said one of the most promising application areas of neuromorphic technology is in emulating how the biological brain has evolved to solve the challenges of interacting with dynamic and often unpredictable real-world environments.
Mirroring the biological world, a neuromorphic chip has a neuron, synapses for neuron-to-neuron connectivity and dendrites, which enable the neuron to receive messages from multiple neurons.
According to Intel’s specifications, each Loihi 2 chip consists of microprocessor cores and up to 128 fully asynchronous neuron cores connected by a network-on-chip (NoC). The neuron cores are optimised for neuromorphic workloads, each implementing a group of “spiking” neurons, including all synapses connecting to the neurons.
All communication between neuron cores is in the form of spike messages, which mimics neural networks in a biological brain. Whereas the previous Loihi chip had three microprocessor cores, Intel said it has doubled the number of embedded microprocessor cores in Loihi 2 to six.
Garrick Orchard, a researcher at Intel Labs, said: “We are not trying to directly model biology, but taking some things we think are important.”
On the Loihi chip, to model biological neuron behaviour, one part of the chip functions as the neuron’s core, he said.“We have a bit of code that describes the neuron,” he added. There are also neuromorphic computing versions of biological synapses and dendrites, all built using asynchronous digital complementary metal-oxide semiconductor (CMOS) technology.
Deep neural networks
Given that neuromorphic computing is inspired by biological systems, deep neural networks (DNN) for machine learning is one of the application areas being targeted. Orchard added: “Using neuromorphic computing for a DNN is something people understand, but we need to differentiate. We are not trying to be a DNN accelerated. There’s more to AI than deep learning.”
Where Loihi 2 and neuromorphic computing as a whole seem to have a good fit is in the area of edge computing for processing sensor data at low latency. Orchard said it could be used within a microphone or camera and offer visual and tactile perception similar to biological systems, in systems such as robotics arm controllers that can adapt to the weight of an object it is trying to lift, or within a drone to provide very low latency control.
Within a datacentre environment, a neuromorphic computer could power a recommendation engine or be used in scientific computing to model how forces propagate through a physical structure, said Orchard.
There is something of an overlap in application areas with quantum computing. Orchard said a neuromorphic computer can be applied to solve a certain class of hard optimisation, such as scheduling at train operator Deutsche Bahn, which is currently investigating its use.
But although there may be an overlap in application areas, Orchard said that, unlike a quantum computer, it is much easier to scale up a neuromorphic computer. The Loihi 2 chip can scale simply by wiring chips together. “You can build very large systems,” he added.
With Loihi 2 and Lava, neuromorphic computing is pushing closer to commercialisation, said Orchard.
Both Intel and IonQ are looking at putting next-generation computing nearer to the edge. Intel’s approach with Loihi is effectively about designing a semiconductor chip to behave in a similar way to a brain neuron, and then use biologically inspired algorithms to run on this new architecture. Quantum computing is built on a foundation of quantum physics.
Although they are very different, both approaches offer an insight into how computationally complex problems could be tackled in the future.


Have you ever seen weird tunnels running through space-time? Well, that just might be a wormhole. We’ve all heard about fancy portals that allow us to travel to far-flung locations in a matter of seconds. It sucks and spits you out somewhere else. Then you ask yourself, “How did it happen?” That one person suddenly produces a sheet of paper, folds it in half, and punches a hole through the fold. There you go, Wormhole! A fold that connects two far-flung places.
What are wormholes?
A wormhole is a specific solution to Einstein’s general relativity equations that creates a tunnel between two distant places in space or time. The length of this tunnel should ideally be less than the distance between those two places, effectively making the wormhole a shortcut. Wormholes are, as far as we know, only hypothetical.
They are a staple of science fiction and have caught the popular imagination. Although they are valid general relativity solutions, scientists have never been able to create a stable wormhole in the real world.
However, the stability factor prohibits it from existing. White holes have been explained in a vacuum so far. White holes vanish as soon as we add mass to the system. As a result, the wormholes become unstable.
Another possibility is that a wormhole has two black holes at its endpoints. A wormhole cannot simply be ‘built’ in any event. Before a wormhole can be produced quantum entanglement is used.
Types of wormholes
Lorentzian wormholes (general relativity) and Euclidean wormholes are the two main forms of wormholes studied by physicists (particle physics).
⦁ Lorentzian wormholes
Traversable wormholes would allow rapid travel in both directions from one section of the universe to another inside the same universe, as well as transit across universes. These wormholes are essentially time and space shortcuts.
The good thing about these wormholes is that we still can’t show evidence after ten years of research. The bad thing is that these odd objects require a lot of negative mass to keep them open and prevent them from collapsing if they exist at all. If Lorentzian wormholes exist, it appears to be quite simple to convert them into time machines.
⦁ Euclidean wormholes
Even stranger are Euclidean wormholes, which exist in “imaginary time” and are fundamentally virtual quantum mechanical processes. These things can’t be beautifully explained in terms of a well-behaved classical gravitational field. You’ll need tons of quantum physics knowledge to understand even their most basic qualities.
How can you find wormholes?
Do you want to know how to spot a wormhole? Such paths could connect one part of our universe to another part of our universe at a different time and/or place, or even to another universe entirely.
The strategy focuses on detecting a wormhole near Sagittarius A*, which is assumed to be a supermassive black hole at the galaxy’s center. While there’s no indication of a wormhole there, it’s a good spot to look because wormholes are thought to require high gravitational circumstances like those seen in supermassive black holes.
Where to find wormholes?
⦁ Center of the Milky Way
In the year 2015, Italian astronomers proposed that a wormhole could exist 27,000 light-years away in the Milky Way’s center. Normally, exotic matter would be required to keep wormholes open, however, scientists believe dark matter is capable of doing so.
⦁ Quantum foam
Even though space isn’t empty—at the atomic level, it’s a cauldron of seething energy that comes and goes. In the ‘quantum foam,’ temporary black holes are constantly being formed. However, if we wanted to make one permanent, we’d require a lot more energy.
⦁ Inside a black hole
Some researchers believe we’d find a wormhole instead of a singularity at the center, as predicted by general relativity. The jury is yet out on whether it is large enough for a human to pass through.
How to identify a wormhole?
⦁ Echo of gravitational waves
Gravitational waves from merging black holes fade fast, but two colliding wormholes would produce an echo that may be detected in future studies.
Microlensing occurs when a wormhole passes in front of a distant star, bending the star’s light slightly. This method has already been used to locate rogue planets.
⦁ Approaching one
Some physicists believe that wormholes are black holes in disguise. It’s a risky venture, but sending something into one would confirm whether or not wormholes exist.
Are wormholes dangerous for humans?
Moving at a quicker rate than light is one way to travel around the cosmos in a single lifetime, yet we could be able to achieve it in a single second by using a physical wormhole to travel vast distances at once.
And it turns out that humans may be able to make the trek but there is a catch to it. There are disadvantages to this strategy, including the fact that such wormholes would be minuscule, meaning that even the most rigorous workout routine would not be enough to make humans slim enough for the journey.
From one side to the other, the wormhole traveler would only take around a second. However, anyone not accompanying them would witness hundreds of years pass by. It’s not as if you can just shove them in.
This is significant for several reasons, the most important of which is the ever-increasing intersection of physical risks to human survival.
From this post, we can conclude that wormholes may connect not only two different sections of the universe but perhaps two entire universes. Similarly, some scientists believe that time travel may be possible if one of the wormholes mouths is manipulated in a precise way.
Even if wormholes could be discovered, today’s technology is insufficient to enlarge or stabilize them. However, scientists are continuing to research the concept as a means of space travel in the hopes that technology may be able to use it in the future.


Scientists have reached a significant milestone in the advancement of quantum computing. Quantum computing is a new technology that promises a paradigm shift in computing and faster solutions to a wide range of problems. However, quantum devices are still in their infancy, with most having only a few qubits. This necessitates the use of simulation to develop quantum algorithms and test these devices. While there are many algorithms for simulating quantum circuits, there are (at the time of writing) no tools that use OpenCL to parallelize this simulation, allowing it to take advantage of devices such as GPUs while remaining portable.
Quantum computers have the potential to revolutionize science by enabling computations that were previously thought to be impossible. However, there is a long way to go and many difficult tests to pass before quantum computers become a commonplace reality.
One of the experiments involves using quantum computers to simulate material properties for next-generation quantum technologies. Quantum computing has the potential to solve some of our planet’s most pressing problems, including those in the environment, agriculture, health, energy, climate, materials science, and others we haven’t yet encountered. Classical computing is becoming increasingly difficult to solve for some of these problems as the system grows in size.
We want to learn how to use new and emerging computational technologies. Developing robust strategies early in the history of quantum computing is an important first step toward understanding how to use these machines efficiently in the future.Giulia Galli
Researchers from the U.S. Department of Energy’s (DOE) Argonne National Laboratory and the University of Chicago conducted quantum simulations of spin defects, which are specific impurities in materials that could provide a promising foundation for new quantum technologies, in a new study. By correcting for noise introduced by quantum hardware, the researchers improved the accuracy of calculations on quantum computers.
“We want to learn how to use new and emerging computational technologies. Developing robust strategies early in the history of quantum computing is an important first step toward understanding how to use these machines efficiently in the future.” Giulia Galli, Argonne National Laboratory and University of Chicago.
The research was conducted as part of the Midwest Integrated Center for Computational Materials (MICCoM), a DOE computational materials science program headquartered at Argonne, as well as Q-NEXT, a DOE National Quantum Information Science Research Center.
“We do these kinds of simulations to gain a fundamental understanding of material properties and also to tell experimentalists how to eventually better design materials for new technologies,” said Giulia Galli, a professor at the University of Chicago’s Pritzker School of Molecular Engineering and Department of Chemistry, senior scientist at Argonne National Laboratory, Q-NEXT collaborator, and director of MICCoM. “The experimental results for quantum systems are frequently complicated and difficult to interpret. A simulation is necessary to aid in the interpretation of experimental results and the formulation of new predictions.”
While quantum simulations have for a long time been done on traditional computers, quantum computers might be able to solve problems that even the most powerful traditional computers today can’t tackle. Reaching that target remains to be seen, as researchers around the work continue the effort to build and use quantum computers.
“We want to learn how to use new computational technologies that are emerging,” said Galli, the paper’s lead author. “Developing robust strategies in the early days of quantum computing is a critical first step toward understanding how to use these machines efficiently in the future.”
Examining spin defects provides a real-world system for validating quantum computer capabilities.
“The vast majority of quantum computer calculations these days are on model systems,” Galli explained. “These models are interesting in theory, but simulating a real material of experimental interest is more valuable to the scientific community as a whole.”
Calculating the properties of materials and molecules on quantum computers encounters a problem that classical computers do not: hardware noise. Noisy calculations produce slightly different results each time a calculation is performed; for example, a noisy addition operation might produce values slightly different from 4 each time the question “What is 2 plus 2?” is asked.
“The uncertainty in the measurement is dependent on the quantum hardware,” said Argonne scientist Marco Govoni, co-lead author of the study. “One of our accomplishments was that we were able to correct our simulations to compensate for the noise that we encountered on the hardware.”
Understanding how to handle noise in quantum computers for realistic simulations is a significant result, according to the study’s first author, University of Chicago graduate student Benchen Huang.
“We can expect noiseless quantum computing in the future; learning how to eliminate or cancel noise in our simulation will also teach us whether quantum advantage will become a reality and for which problems in materials science.”
Finally, the groundbreaking potential of quantum computers, according to Galli, will motivate more work in this area. “We’ve only just begun,” she explained. “The road ahead appears to be full of exciting challenges.”


Cryptography is the art of encoding sensitive information so that only authorized users can decode it. Existing coding methods convert information using a shared key (a sequence of bits) that specifies the conversion details. When communicating partners wish to generate a key for secure communication, they exchange information over a public channel, but in a form difficult for an eavesdropper attempting to extract the key. Current key generation protocols rely on mathematical complexity to achieve this capability. With an accelerating pace of development in quantum computing, such encryption methods are a risk -- quantum computers solve mathematically complex problems much faster compared to conventional computers.
For example, the ubiquitous RSA encryption scheme is rendered insecure by employing a quantum-factorization algorithm. Consequently, data that requires long-term security needs to be encrypted in a quantum-secure manner so that they cannot be intercepted today and decrypted tomorrow by a future quantum computer. Quantum Key Distribution and Post-Quantum Cryptography provide schemes that are resilient against this threat posed by quantum computers.
Currently, a popular encryption method called the Advanced Encryption Standard-Galois Counter Mode (AES-GCM) is the standard proposed by NIST for two parties to code and decode messages using a shared secret key (i.e. the key is symmetric). To establish this key, the parties follow a key exchange protocol e.g. the Transport Layer Security (TLS) handshake. This process uses an asymmetric key pair, consisting of mathematically-linked private and public keys.
One party “signs-off” her transmission with her private key, while the other party mathematically verifies the signature using the public key. Security is based on the difficulty of solving mathematical problems, e.g. factorizing large prime numbers in the RSA protocol. However, a quantum computer will break all existing public key exchange methods -- an adversary deploying Shor’s quantum-factorization algorithm will solve this type of mathematical problem exponentially faster than a classical computer.
AES-GCM variants, operating with key sizes at less than 128-bits or less, will also be compromised by the quantum Grover’s search algorithm, which provides a quadratic speed up when searching through all possible keys for deciphering an encrypted message. Fortunately, this threat can be countered by extending the key length to 256 bits, increasing the search time to an impractical extent, even for a quantum computer.
Similarly, hash functions producing 256-bit outputs, widely-used for fingerprinting data, are not expected to be broken by this attack. However, one has to assume that a quantum-attack more efficient than Grover’s search does not exist.
In response to the quantum computing threat posed to existing cryptographic techniques, two approaches have been developed: Post Quantum Cryptography (PQC) and Quantum Key Distribution (QKD). PQC are mathematically complex algorithms resistant to quantum computing attacks.
A suitable PQC public key exchange standard has yet to be established. Potential candidates are currently being reviewed by the National Institute of Standards and Technology (NIST). Quantum Key Distribution has now begun to see commercial adoption. The security of the key material is based on the laws of quantum physics, rather than mathematical complexity, and is therefore quantum-safe.
|Cryptographic Algorithm||Type||Purpose||Quantum Safe?||Available Now?|
|RSA, ECDSA||Asymmetric||Key Establishment, Signatures||No||Yes|
|AES-GCM||Symmetric||Encryption||Larger Key Sizes Needed||Yes|
|SHA-3||-||Hash Function||Larger Output Needed||Yes|
|Post Quantum Cryptography||Public||Encryption, Key Establishment, Signatures||Yes||No|
|Quantum Key Distribution||Symmetric||Key Generation||Yes||Yes|
Quantum Key Distribution is the generation and distribution of cryptographic keys secured by quantum physics. Information required to generate the keys are encoded in the properties of photons, which can be distributed over long distances via an optical link.
Quantum Key Distribution security leverages on quantum physics, which specifies that an unknown photon state cannot be measured or copied without altering the original state -- an eavesdropper inadvertently reveals her presence as she introduces a detectable, irreversible error.
S-Fifteen Instruments Quantum Key Distribution system implements the BBM92 protocol which exhibits fewer vulnerabilities compared to systems running the more common BB84 protocol. We use entangled photon pairs for distributing quantum states -- a single photon of the pair for each party across an optical link. Although each photon of the pair is correlated through quantum entanglement, their individual states are inherently random. This inherent randomness is achieved without active optical components commonly found in prepare-and-measure protocols.
The inclusion of active elements, e.g. phase modulators, has been shown to potentially leak information and require countermeasures whose implementation increases system complexity, and requires additional security verification. Our implementation uses exclusively passive components, which simplifies auditing our system for vulnerabilities. Overall, our BBM92 system is intrinsically immune to attacks targeting the following security issues -- addressing these in a BB84 system typically require additional countermeasures: Trojan-horse, multi-photon emissions, phase-correlation between signal pulses.
A notable aspect of the BBM92 protocol we have adopted is the direct use of quantum randomness. We do not need to rely on a separate random number generator for controlling the active elements in our hardware -- such devices typically require their own security certification. We rely instead on the intrinsic unpredictability of the polarization of photons when prepared in an entangled state, and the path chosen when passing through a 50:50 beam-splitter, for sources of quantum randomness.
Quantum randomness has the advantage of being intrinsically unpredictable and fundamentally inaccessible to any external party -- our system derives randomness directly from the photon source used for communication, rather than from an additional source.
Any cryptographic system needs to prove its resilience against attacks. We actively investigate potential vulnerabilities in our implementation and develop countermeasures to improve security. In the past we have looked into the timing information exchanged between communicating parties as a side channel from which the attacker could collect a large amount of information about the key.
This vulnerability is neutralized in our current QKD implementation by randomizing photon emission times using a free-running entangled photon source. Currently, we are investigating detector-blinding attacks as part of a comprehensive vulnerability study.
Work with us to make your organization quantum-safe.


Atom Arrays for Superresolution Imaging
A touchstone for superresolution optical imaging techniques for cold atomic gases is the precision with which they can resolve individual atoms, which are far smaller than the wavelength of the light used for imaging (see Viewpoint: Zooming in on Ultracold Matter). Now, a team based in the US and Germany has turned this problem on its head. Instead of using light to probe atoms, they use atoms to probe an electromagnetic field, taking advantage of the atoms’ tiny size to image the field with high resolution . Using a one-dimensional array of rubidium atoms trapped in optical tweezers, Emma Deist, at the University of California, Berkeley, and colleagues scanned a light field in a cavity with a spatial resolution below the wavelengths of both the cavity field and of the tweezer light. The work may usher in a new generation of metrology and sensing schemes based on individually controlled neutral atoms. It also introduces, for the first time, large arrays of individual atoms into the cavity quantum-electrodynamics (cavity QED) toolbox.
Techniques developed over the last few years have allowed researchers to create large, defect-free atomic arrays that can now approach 1000 atoms in two dimensions . These advances have made atomic arrays a prominent platform for quantum information science, with applications ranging from quantum simulation to quantum computing to precision optical metrology . Deist and colleagues extend the use of atom arrays in the latter category, employing a comparatively modest, one-dimensional array of up to ten atoms to probe the intensity of a standing-wave field inside an optical cavity.
The row of atoms used by the researchers extends along the radial direction of the cavity mode (i.e., perpendicular to the cavity axis). They scan this array along the cavity axis, thereby encompassing the two-dimensional plane defined by the cavity’s radial and axial directions (Fig. 1). The cavity field shifts the resonance frequency of an atomic transition, and, by measuring the rate of the fluorescence induced by a calibrated probe beam, the team determines the intensity of the field at any position in the wave. The result is a beautiful map of the profile of the field, revealing a Gaussian field distribution with a radius of , consistent with predictions based on the cavity geometry and on the cavity field’s wavelength of 1560 nm.
Since the tweezer array localizing the atoms is positioned with electrically actuated adaptive optical elements, the positions of the atoms can be controlled with a precision much greater than the optical wavelength of the light used to generate the tweezers and detect the atoms. For example, the acousto-optic deflector (AOD) used to generate the tweezers provides a mapping between the radio frequency in the AOD and the position of the tweezer in the focal plane , with a typical conversion of . Thus, radio-frequency precision at the kHz level can, in principle, position a tweezer with a spatial precision of despite the size of the tweezer itself being .
Deist and colleagues employ this capability to probe the cavity field on length scales shorter than the field’s 1560-nm wavelength and the 780-nm wavelength of the fluoresced light used to perform the measurement. The ultimate limit on the spatial precision of their approach is set by the accuracy with which the atoms can be localized within their respective tweezers. For atoms cooled to the motional ground state of their optical tweezers, the atomic wave function can be as small as (assuming a trap depth of and a tweezer waist radius of ). In their experiment, Deist and colleagues have slightly hotter atoms which, when combined with other technical factors, limit the spatial resolution to . However, further cooling  and other technical upgrades are readily available, offering significant improvements to the possible resolution.
The researchers also introduce a second field into the cavity with a wavelength of 781 nm—slightly larger than half that of the first field. This short-wavelength field induces forces sufficiently large to displace the atoms in the tweezers, thereby distorting the measurements of the long-wavelength cavity field made using the method described above. In this way, the team uses the atom array as a field-sensitive force sensor—an atomic-force supermicroscope.
The techniques demonstrated by Deist and colleagues could be helpful for diagnosing optical fields in myriad applications—not only in optical cavity systems such as that used in their experiment but also in optical lattice systems, where, for example, the investigation of Hubbard models requires ever-improving control of optical potentials . Beyond optical metrology, marrying a scalable array of single atoms to an optical cavity with strong atom-photon coupling is an enabling breakthrough for quantum information technologies. Systems with atoms coupled to optical cavities—described by cavity QED—offer the ability to entangle atomic spins with photons. Such entanglement could then be used to generate atom-atom entanglement within the cavity, perform nondemolition measurements of atomic spins, and generate remote entanglement between separated systems via a photonic quantum bus—essential operations for quantum computing and communication hardware built on this platform. Cavity QED with single atoms or with atomic ensembles has been studied for the past few decades, but only recently has it been applied to a pair of individual atoms  and to an ordered array of atomic ensembles . Deist and colleagues are the first to demonstrate a cavity QED system combined with a scalable array of individual atoms.
Another field of application involves schemes with highly excited “Rydberg” states , which have recently become the dominant approach to entanglement of neutral atoms. Entanglement “fidelities” using this approach now exceed 0.99 —beyond what can readily be achieved using photon-mediated interactions in a cavity . However, as Rydberg atoms interact with each other via their electric dipole moments, Rydberg interactions are inherently short range, which complicates the development of many-body entangled states such as logically encoded qubits. Photon-mediated interactions in optical cavities could solve this problem [9, 10] because they act on an infinitely long range. Unfortunately, stray electric fields generated by the dielectric surfaces of the mirrors cause a transient Stark shift in the Rydberg states that adversely affects the ability to reliably perform Rydberg-based entangling operations. Deist and colleagues avoid this problem by employing a near-concentric cavity with a large (1 cm) mirror spacing. Near-concentric cavities provide small mode volumes (and thus strong atom-photon coupling) even with such large mirror separation. Since the mirrors in this setup are sufficiently far away from the atoms, the marriage of cavity QED with strong coupling and Rydberg atom arrays is made possible for the first time.
- E. Deist et al., “Superresolution microscopy of optical fields using tweezer-trapped single atoms,” Phys. Rev. Lett. 128, 083201 (2022).
- S. Ebadi et al., “Quantum phases of matter on a 256-atom programmable quantum simulator,” Nature 595, 227 (2021).
- A. M. Kaufman and K.-K. Ni, “Quantum science with optical tweezer arrays of ultracold atoms and molecules,” Nat. Phys. 17, 1324 (2021).
- P. Zupancic et al., “Ultra-precise holographic beam shaping for microscopic quantum control,” Opt. Express 24, 13881 (2016).
- S. Welte et al., “Photon-mediated quantum gate between two neutral atoms in an optical cavity,” Phys. Rev. X 8, 011018 (2018).
- Avikar Periwal et al., “Programmable interactions and emergent geometry in an array of atom clouds,” Nature 600, 630 (2021).
- M. Saffman et al., “Quantum information with Rydberg atoms,” Rev. Mod. Phys. 82, 2313 (2010).
- I. S. Madjarov et al., “High-fidelity entanglement and detection of alkaline-earth Rydberg atoms,” Nat. Phys. 16, 857 (2020).
- W. Huie et al., “Multiplexed telecommunication-band quantum networking with atom arrays in optical cavities,” Phys. Rev. Res. 3, 043154 (2021).
- J. Ramette et al., “Any-to-any connected cavity-mediated architecture for quantum computing with trapped ions or Rydberg arrays,” arXiv:2109.11551 .