Researchers at the National Institute of Standards and Technology (NIST) and Wavsens LLC have developed a method for using radio signals to create real-time images and videos of hidden and moving objects, which could help firefighters find escape routes or victims inside buildings filled with fire and smoke. The technique could also help track hypersonic objects such as missiles and space debris.
The new method, described in Nature Communications, could provide critical information to help reduce deaths and injuries. Locating and tracking first responders indoors is a prime goal for the public safety community. Hundreds of thousands of pieces of orbiting space junk are considered dangerous to humans and spacecraft.
“Our system allows real-time imaging around corners and through walls and tracking of fast-moving objects such as millimeter-sized space debris flying at 10 kilometers per second, more than 20,000 miles per hour, all from standoff distances,” said physicist Fabio da Silva, who led the development of the system while working at NIST.
This demonstration of the m-Widar (micro-Wave image detection, analysis and ranging) system shows, in the video on the left, a person walking and later crouching and lying down in an anechoic chamber. The transmitters and receiver are in a vertical line on the right side of the chamber. The second video on the right shows the instrument’s view of the same scene. About 21 seconds into the video, a wallboard is inserted between the person and the instrument in the anechoic chamber, to show that m-Widar can “see” through walls. Credit: NIST
“Because we use radio signals, they go through almost everything, like concrete, drywall, wood, and glass,” da Silva added. “It’s pretty cool because not only can we look behind walls, but it takes only a few microseconds of data to make an image frame. The sampling happens at the speed of light, as fast as physically possible.”
The NIST imaging method is a variation on radar, which sends an electromagnetic pulse, waits for the reflections, and measures the round-trip time to determine distance to a target. Multisite radar usually has one transmitter and several receivers that receive echoes and triangulate them to locate an object.
“We exploited the multisite radar concept but in our case use lots of transmitters and one receiver,” da Silva said. “That way, anything that reflects anywhere in space, we are able to locate and image.”
Da Silva explains the imaging process like this:
“To image a building, the actual volume of interest is much smaller than the volume of the building itself because it’s mostly empty space with sparse stuff in it. To locate a person, you would divide the building into a matrix of cubes. Ordinarily, you would transmit radio signals to each cube individually and analyze the reflections, which is very time consuming. By contrast, the NIST method probes all cubes at the same time and uses the return echo from, say, 10 out of 100 cubes to calculate where the person is. All transmissions will return an image, with the signals forming a pattern and the empty cubes dropping out.”
Da Silva has applied for a patent, and he recently left NIST to commercialize the system under the name m-Widar (microwave image detection, analysis, and ranging) through a startup company, Wavsens LLC (Westminster, Colorado).
The NIST team demonstrated the technique in an anechoic (non-echoing) chamber, making images of a 3D scene involving a person moving behind drywall. The transmitter power was equivalent to 12 cellphones sending signals simultaneously to create images of the target from a distance of about 10 meters (30 feet) through the wallboard.
Da Silva said the current system has a potential range of up to several kilometers. With some improvements the range could be much farther, limited only by transmitter power and receiver sensitivity, he said.
The basic technique is a form of computational imaging known as transient rendering, which has been around as an image reconstruction tool since 2008. The idea is to use a small sample of signal measurements to reconstruct images based on random patterns and correlations. The technique has previously been used in communications coding and network management, machine learning and some advanced forms of imaging.
Da Silva combined signal processing and modeling techniques from other fields to create a new mathematical formula to reconstruct images. Each transmitter emits different pulse patterns simultaneously, in a specific type of random sequence, which interfere in space and time with the pulses from the other transmitters and produce enough information to build an image.
The transmitting antennas operated at frequencies from 200 megahertz to 10 gigahertz, roughly the upper half of the radio spectrum, which includes microwaves. The receiver consisted of two antennas connected to a signal digitizer. The digitized data were transferred to a laptop computer and uploaded to the graphics processing unit to reconstruct the images.
The NIST team used the method to reconstruct a scene with 1.5 billion samples per second, a corresponding image frame rate of 366 kilohertz (frames per second). By comparison, this is about 100 to 1,000 times more frames per second than a cellphone video camera.
With 12 antennas, the NIST system generated 4096-pixel images, with a resolution of about 10 centimeters across a 10-meter scene. This image resolution can be useful when sensitivity or privacy is a concern. However, the resolution could be improved by upgrading the system using existing technology, including more transmitting antennas and faster random signal generators and digitizers.
In the future, the images could be improved by using quantum entanglement, in which the properties of individual radio signals would become interlinked. Entanglement can improve sensitivity. Radio-frequency quantum illumination schemes could increase reception sensitivity.
The new imaging technique could also be adapted to transmit visible light instead of radio signals — ultrafast lasers could boost image resolution but would lose the capability to penetrate walls — or sound waves used for sonar and ultrasound imaging applications.
In addition to imaging of emergency conditions and space debris, the new method might also be used to measure the velocity of shock waves, a key metric for evaluating explosives, and to monitor vital signs such as heart rate and respiration, da Silva said.
Reference: “Continuous Capture Microwave Imaging” by Fabio C. S. da Silva, Anthony B. Kos, Grace E. Antonucci, Jason B. Coder, Craig W. Nelson and Archita Hati, 25 June 2021, Nature Communications.
This work was funded in part by the Public Safety Trust Fund, which provides funding to organizations across NIST leveraging NIST expertise in communications, cybersecurity, manufacturing and sensors for research on critical, lifesaving technologies for first responders.


Physicists set a new record by linking together a hot soup of 15 trillion atoms in a bizarre phenomenon called quantum entanglement. The finding could be a major breakthrough for creating more accurate sensors to detect ripples in space-time called gravitational waves or even the elusive dark matter thought to pervade the universe.
Entanglement, a quantum phenomena Albert Einstein famously described as "spooky action at a distance," is a process in which two or more particles become linked and any action performed on one instantaneously affects the others regardless of how far apart they are. Entanglement lies at the heart of many emerging technologies, such as quantum computing and cryptography.
Entangled states are infamous for being fragile; their quantum links can be easily broken by the slightest internal vibration or interference from the outside world. For this reason, scientists attempt to reach the coldest temperatures possible in experiments to entangle jittery atoms; the lower the temperature, the less likely atoms are to bounce into each other and break their coherence. For the new study, researchers at the Institute of Photonic Science (ICFO) in Barcelona, Spain, took the opposite approach, heating atoms to millions of times hotter than a typical quantum experiment to see if entanglement could persist in a hot and chaotic environment.
"Entanglement is one of the most remarkable quantum technologies, but it is famously fragile," said Jia Kong, a visiting scientist at ICFO and lead author of the study. "Most entanglement-related quantum technology has to be applied in a low-temperature environment, such as a cold atomic system. This limits the application of entanglement states. [Whether or not] entanglement can survive in a hot and messy environment is an interesting question."
Things get hot and messy
The researchers heated a small glass tube filled with vaporized rubidium and inert nitrogen gas to 350 degrees Fahrenheit (177 degrees Celsius), coincidentally the perfect temperature to bake cookies. At this temperature, the hot cloud of rubidium atoms is in a state of chaos, with thousands of atomic collisions taking place every second. Like billiard balls, the atoms bounce off each other, transferring their energy and spin. But unlike classical billiards, this spin does not represent the physical motion of the atoms.
In quantum mechanics, spin is a fundamental property of particles, just like mass or electric charge, that gives particles an intrinsic angular momentum. In many ways, the spin of a particle is analogous to a spinning planet, having both angular momentum and creating a weak magnetic field, called a magnetic moment. But in the wacky world of quantum mechanics, classical analogies fall apart. The very notion that particles like protons or electrons are rotating solid objects of size and shape doesn't fit the quantum worldview. And when scientists try to measure a particle's spin, they get one of two answers: up or down. There are no in-betweens in quantum mechanics.
Fortunately, the tiny magnetic fields created by a particle's spin allow scientists to measure spin in a number of unique ways. One of those involves polarized light, or electromagnetic waves that oscillate in a single direction.
The researchers shot a beam of polarized light at the tube of rubidium atoms. Because the atoms' spins act like tiny magnets, the polarization of the light rotates as it passes through the gas and interacts with its magnetic field. This light-atom interaction creates large-scale entanglement between the atoms and the gas. When researchers measure the rotation of the light waves that come out the other side of the glass tube, they can determine the total spin of the gas of atoms, which consequently transfers the entanglement onto the atoms and leaves them in an entangled state.
"The [measurement] we used is based on light-atom interaction," Kong said. "With proper conditions, the interaction will produce correlation between light and atoms, and then if we do correct detection, the correlation will be transferred into atoms, therefore creating entanglement between atoms. The surprising thing is that these random collisions didn't destroy entanglement."
In fact, the "hot and messy" environment inside the glass tube was key to the experiment's success. The atoms were in what physicists call a macroscopic spin singlet state, a collection of pairs of entangled particles' total spin sums to zero. The initially entangled atoms pass their entanglement to each other via collisions in a game of quantum tag, exchanging their spins but keeping the total spin at zero, and allowing the collective entanglement state to persist for at least a millisecond. For instance, particle A is entangled with particle B, but when particle B hits particle C, it links both particles with particle C, and so on.
This "means that 1,000 times per second, a new batch of 15 trillion atoms is being entangled," Kong said in a statement. One millisecond "is a very long time for the atoms, long enough for about 50 random collisions to occur. This clearly shows that the entanglement is not destroyed by these random events. This is maybe the most surprising result of the work."
Because the scientists are only able to understand the collective state of the entangled atoms, the application of their research is limited to special uses. Technologies like quantum computers are likely out of the question, since the state of individually entangled particles needs to be known to store and send information.
However, their results may help to develop ultra-sensitive magnetic field detectors, capable of measuring magnetic fields more than 10 billion times weaker than Earth's magnetic field. Such powerful magnetometers have applications in many fields of science. For example, in the study of neuroscience, magnetoencephalography is used to take images of the brain by detecting the ultra-faint magnetic signals given off by brain activity.
"We hope that this kind of giant entangled state will lead to better sensor performance in applications ranging from brain imaging, to self-driving cars, to searches for dark matter," Morgan Mitchell, a professor of physics and the lab's group leader, said in the statement.
Their results were published online May 15 in the journal Nature Communications.
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Originally published on Live Science.
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Quantum computing is to classical computing what space travel is to the horse and cart. Comparisons have even been made to the cognitive awakening of early man. Then again, every generation believes they are in the grip of advanced and unparalleled technology. This may be true, but despite the incredible scientific breakthroughs of past centuries, we still don’t really understand how vast swathes of our world works, including our own bodies. Quantum computing has the potential to address these myriad gaps on a granular level as never before.
Some predict that this novel technology will create whole new families of drugs and diagnostic processes, new industrial and chemical processes, new ways to address climate change, new methods of logistics and transport, advanced space exploration, surveillance and monitoring… the list goes on.
Most estimations put working quantum computers within a future window of between five and 20 years. GlobalData predicts quantum supremacy – when quantum computers surpass classical computers in computational power and accuracy – will be achieved within five years, but this timeframe will deliver intermediate quantum computers that offer an advantage for specific optimisation applications rather than a full spectrum of use cases. The important milestone, however, is that the technology has left the lab and is on the cusp of commercialisation: early adoption and exploration by businesses is already under way.
What is quantum computing?
Today’s classical computers are based on information stored on binary bits, which are transistors represented by either 0s or 1s. The computing power is linear and increases with the number of transistors. This means that the main limitation of classical computing is a finite level of processing power that can be held on a chip. All calculations are deterministic with the same input resulting in the same output, and all processing is carried out in sequential order.
Instead of classic computing’s binary processing, quantum computing uses the properties of quantum physics: the counterintuitive behaviour of subatomic particles that results in the quantum states of superposition and entanglement. Quantum computing bits are called qubits and have the ability to represent 0 and 1 simultaneously. By increasing qubits, the computational power grows exponentially, not linearly.
For example, think about the problem of finding a way out of a complex maze where there are millions of possible exit routes. A classical computer using binary processing would check each escape route one after the other in a linear manner until it found a correct solution. A quantum computer, on the other hand, would test all possible escape routes simultaneously and come up with a solution in a fraction of the time. This means the theoretical limits of quantum computing are endless and its computational power is in order of magnitudes greater than classical computing. According to IBM, if you wanted to find one item in a list of one trillion and each item took one microsecond to check, a classical computer would take a week to complete this task versus only a second for a quantum computer.
How will quantum computing change the business world?
According to Markets and Markets, the quantum computing market is expected to reach $1.77bn by 2026, up from $472m in 2021. This level of investment in such an unproven technology demonstrates a consensus about its potential for disruption. Mass commercial applications could transform everything from drug discovery and disease diagnostics to calculating financial risk and refining industrial processes.
Potential applications include:
- pharmaceutical industry: drug discovery, disease diagnostics and personalised medicine through gene sequencing and analysis
- optimisation problems: supply chain logistics, delivery fleet optimisation, mapping, traffic/air traffic control and transport systems
- climate change: forecasting, climate modelling and carbon capture technologies (the UK Met Office is already investing in quantum computing to help improve weather forecasting)
- financial services: forecasting financial risk with complex financial modelling
- machine learning: the convergence of quantum computing and artificial intelligence (AI) has the potential to be a game changer. The ability to analyse huge quantities of data using quantum computing will provide the information needed for high-performance AI.
Where is quantum computing’s global centre of gravity?
The US and China are locked in a battle for global quantum supremacy. The US launched its National Quantum Initiative in 2019, pledging $1.2bn over five years. In 2020, the White House Office of Science and Technology Policy, together with the National Science Foundation and the Department of Energy, announced a fund of $1bn to establish 12 AI and quantum information science research centres nationwide.
Similarly, in 2016, China’s 13th five-year plan included the aspiration to become the pre-eminent global quantum computing and communication superpower. Indeed, China leads in quantum communications via satellites and long-path optical fibres, launching the world’s first quantum satellite, Micius, in 2016. China is also building a Quantum Information Sciences National Laboratory with initial funding of $1bn.
Patent data from GlobalData demonstrates that the US and China are at the global forefront of the sector’s technology development.
The UK, however, punches above its weight as a pioneer in the quantum computing sector. The National Quantum Technology Programme (NQTP) was established in 2013 with an estimated public and private sector investment of £1bn by 2024, according to the NQTP’s 2020 strategic intent report. Promising start-ups include Cambridge Quantum Computing and Oxford Quantum Circuits, with a major sector hub evolving around Oxford University.
Global quantum computing hubs of note have also developed in Australia, Canada, Germany, Japan, Russia, Singapore and South Korea. All global hubs have the backing of policymakers and have been the beneficiaries of concerted efforts by governments recognising the need to stay abreast of this emerging technology. Public funding of quantum technologies is said to have reached $24.4bn globally by mid-2021 in an estimate by quantum resources and careers company Quereca.
The quantum computing private sector is seeing significant growth, with deal size and numbers increasing. According to Pitchbook, private investment in quantum computing companies reached $1.02bn by September 2021, more than the combined figure for the previous three years. Consolidation is beginning, which indicates a maturing of the sector. Deal activity demonstrates this flood of private investment, with the US at the forefront.
Quantum threats and challenges
For all the potential advantages, the technology behind quantum computers has many hurdles to clear before it becomes ready for market. A quantum computer is still prohibitively expensive for most companies or organisations to own. For now, exploration is taking place in the cloud with shared services the preferred way to access the technology.
Common standards are still being worked out and possible qubit architectures are still in formation (with five main quantum computing architectures in contention). These various methods are being used by start-ups and tech giants alike and most look promising, but none have dominated the market. For example, Google is leading in the area of superconducting qubits, Silicon Valley based start-up PsiQuantum is pioneering photonic qubits and UK start-up Cambridge Quantum Computing uses trapped ion qubits. No matter the qubit architecture, until the high error rates in quantum computing outcomes are fixed, the technology will not be widely used for real-world problems. Quantum computing companies are also struggling to attract and retain talent, and this will be a significant future challenge for the sector.
The greatest risk quantum technology poses is its potential to decode all current cryptography. Businesses need to become alert to the security risks that are likely to ensue with quantum supremacy. However, according to the Global Risk Institute, it will be at least ten years before such attacks are feasible.
While most industry insiders believe quantum supremacy will be achieved within a decade, public perception of the risk timeline is somewhat out of step and therefore businesses are lagging on mitigating potential risks. A survey from the Global Risk Institute assessing the quantum risk timeline found that 90% of respondents indicated the quantum threat timeline was nearer the 20-year mark.
GlobalData’s prediction of five years for quantum supremacy comes with caveats. Even when the hardware and software are available, businesses still need to know how to use quantum computing and understand that it may not be a panacea for business problems. The analyst says the technology will be exceedingly useful but initially for a narrow and well-defined set of problems. For now, IT departments should keep abreast of developments, but it is other parts of the company that will need to be prepared for problems that are amenable to quantum computing solutions. No business needs to have a quantum computer just yet, but early-mover exploration is accessible in the cloud and it is time to start thinking about the possibilities.


Google has just announced that it’s achieved “quantum supremacy” by using a quantum computer it built to perform a test computation that would take even the most powerful non-quantum supercomputers thousands of years to execute. It’s an early yet significant milestone in the development of quantum computers—one which Google refers to as the field’s “hello world” moment—because it stands as a proof-of-concept of the real-world applications of this technology, which will likely one day include everything from creating unbreakable encryption to modeling quantum systems to helping AI program itself.
The idea that Google researchers—who published their breakthrough in a paper in the journal Nature—are dealing with science and technology that’s just barely on the edge of humanity’s understanding isn’t hyperbolic in the least considering the complexity of quantum computers. At their core, quantum computers distinguish themselves from non-quantum computers thanks to their use of quantum bits, or qubits, of information. Unlike binary bits of information, which are made up of either zeroes or ones, qubits are “superpositions” of both zeroes and ones. This means that qubits exist in two equally valid states simultaneously.
It’s difficult to grasp exactly what this means because, as Google itself notes, this kind of dual-state existence runs so counter to our normal day-to-day experiences. A big part of the reason the physical rules that govern the quantum world and quantum computing are so difficult to grasp is because we don’t have any useful references, or even metaphors, for the way the subatomic world works. (How often is your sandwich both there and not there in front of you at lunch?) But on the quantum scale, particles existing in a superposition of multiple states is the norm.
While this feature of the quantum world is totally counterintuitive to our everyday lives, it does make the existence of qubits possible, which are useful because their dual nature as both zeroes and ones means they can be used to perform much more complex calculations much faster than normal computers. In other words quantum computers can have exponentially more computational power relative to normal computers thanks to the fact that they have access to far more computations far more quickly.
Excited about what quantum computing means for the future – it gives us another way to speak the language of the universe and better understand the world, not just in 1s and 0s but in all of its states: beautiful, complex, and with limitless possibility. https://t.co/P6YX4KguMX
— Sundar Pichai (@sundarpichai) October 23, 2019
For instance, if you have two bits of information, they can exist in four possible states (00,11,01,10) whereas two qubits of information would allow you to put those four possible states in superposition, meaning those four states, plus any variation of all four states, all exist simultaneously. In practice, this would mean performing calculations much faster thanks to the availability of exponentially more information, which is exactly what’s happened with this landmark quantum supremacy computation.
In the case of Google’s quantum computer, a 54-qubit processor named “Sycamore” has been deployed, which can simultaneously be in 2^54 possible computational states. Or—wait for it—18,014,398,509,481,984 simultaneous computational states. Yes, this means quadrillions of simultaneous computational states.
The technology giant’s #Sycamore quantum processor was able to perform a specific task in 200 seconds that would take the world’s best supercomputers 10,000 years to complete. pic.twitter.com/kYGXI4QiWW
— Nicholas Stevenson (@NSR_Stevenson) October 23, 2019
Looking forward, there is an enormous amount of research to be done and an equally enormous number of breakthroughs to be made before quantum processors like Google’s Sycamore become widely available. No one source found while researching this article gave a concrete estimate in terms of when quantum computing will become commonplace, but the general consensus seems to be that it will take decades.
But once quantum computers like these do come online, Google says they’ll be able to help with everything from solving complex climate change problems to helping to find cures for diseases to coming up with more efficient battery designs to “simulating the world on a molecular level.” Which means this “hello world” moment is going to mean us saying hello back to worlds that we literally can’t yet imagine, but one day, with quantum computing’s help, will be able to.
What do you think of Google’s “quantum supremacy” moment? And where do you think quantum computing is going to take us in the decades to come? Let us know in the comments!


Technology Research News
Give an electron two paths to get to one
location and it will usually take both. This fact of quantum physics plays
a leading role in a computer architecture that could replace today's chip
technology when it reaches its limits in a decade or so.
According to the laws of quantum physics, electrons are waves as well
as particles. Like ocean waves, where two crests meet they reinforce each
other and where a crest and trough meet they cancel each other out. Researchers
at University of Missouri at Rolla have devised a scheme for using electron
wave interference to represent the ones and zeros of digital
Traditional electronic computers use combinations of transistors, which
are tiny electronic switches, as the logic units that perform the binary
arithmetic at the heart of digital computing. Electron wave computers
would use networks of microscopic wire rings that form the two paths for
the electron waves to follow, said Cheng-Hsiao Wu, a professor of electrical
and computer engineering at the University of Missouri at Rolla.
"You do not need transistors to control the flow of charge if all the
devices involved are very small and at low temperature," said Wu.
The researchers' proposal involves using modified forms of Aharonov-Bohm
rings, which are used in basic physics research, to form the logic gates
of computers. Aharonov-Bohm rings are circles of extremely thin wire and
are commonly made several times smaller than a red blood cell. Due to
their wave nature, electrons entering the Aharonov-Bohm rings travel in
both directions at once, meeting -- and reinforcing each other -- at the
Using a magnetic field perpendicular to the ring, researchers can speed
up or slow down the electron wave traveling in one side of the ring, throwing
the waves in the two sides out of sync and causing the waves to cancel
each other out when they meet at the other end. The reinforced waves and
the canceled waves could represent the ones and zeros of computing, according
Aharonov-Bohm rings have an input and an output terminal. The researchers'
scheme calls for making three- and four-terminal Aharonov-Bohm rings.
Their work shows that three-terminal rings could be combined to form IF-THEN,
XOR, OR, AND and INVERTER logic units. These logic units could, in turn,
be combined to form half adders and full adders. A half adder adds two
binary numbers but cannot carry, and a full adder includes the carry function.
A single, four-terminal Aharonov-Bohm ring could also be used as a half
adder, said Wu. "It replaces eight transistors for the same function."
And two connected four-terminal Aharonov-Bohm rings could serve as a full
adder. "This replaces about two dozen transistors in traditional microelectronic
circuits," he said.
In addition to the potential for making smaller, and therefore faster,
computer circuits, electron wave computers could solve certain problems
faster than even the fastest ordinary computer by examining all of the
possible solutions to a problem at once, according to Wu.
Electron wave interference could be used to make massively parallel processing
computers, he said. "Millions of inputs enter a large network [of rings]
simultaneously with desirable outputs when the waves arrive at the output
terminals. This is similar to optical computing."
Optical computers use light waves that reinforce and cancel each other
out. Last year, researchers at the University of Rochester demonstrated
an optical computer running a quantum search algorithm.
The electron wave scheme is an idea worth trying, said Ian Walmsley, a
professor of experimental physics at the University of Oxford and a professor
of optics at the University of Rochester. "The nice thing about electrons
is that [their] wavelengths are inherently smaller than optical wavelengths,
so the whole machine can be smaller. At present I see the advance as a
technical one rather than a fundamental one," he added.
"It's a very neat idea but... completely theoretical," said Mike Lea,
a professor of physics at the University of London. "I'd be quite skeptical
about claims without at least some analysis of the likely practicalities
based on real experiments," he said.
The researchers are working out the physics for larger networks of Aharonov-Bohm
rings, said Wu. "I would like to convince experimentalists elsewhere to
simply extend the original Aharonov-Bohm effect to three or four terminals.
I promise nice results will come out of such a simple extension," he said.
Given that today's semiconductor technology is likely to reach its limits
by the year 2015, researchers and engineers should have a good idea of
how to build devices smaller than 10 nanometers by then, said Wu. At that
point, electron wave computing could be a contender for the next generation
computer architecture, he said.
Wu's research colleague was Diwakar Ramamurthy. They published the research
in the February 15, 2002 issue of the journal Physical Review B. The research
was funded by the university.
Timeline: 13 years
TRN Categories: Quantum Computing and Communications; Integrated
Story Type: News
Related Elements: Technical paper, "Logic Functions from
Three-Terminal Quantum Resistor Networks for Electron Wave Computing,"
Physical Review B, February 15, 2002
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emits linked photons
Technology Research News
The way lasers work can only be explained
by quantum physics, the realm of atoms and subatomic particles. Lasers
stimulate already-energized atoms, causing them to emit energy in the
form of photons, the particles of light.
A team of researchers at the University of Oxford in England is taking
the technology deeper into the bizarre regions of quantum physics with
the development of a rudimentary laser that produces linked pairs of photons.
The work promises to make perfectly secure communications devices more
practical and advance long-term efforts to build ultra-powerful quantum
The device makes it easier to produce linked, or entangled, sets of two
or even four photons. The researchers have demonstrated "laser-like operation"
for entangled photons, said Antia Lamas-Linares, a graduate student at
the University of Oxford.
When two or more quantum particles become entangled, one or more of their
properties march in lockstep. For example, two photons can have their
polarizations, or electric field orientations, entangled.
But when photons are entangled they exist in an unmeasurable netherworld
of quantum mechanics where they are in some mixture of all possible polarizations
until one of the pair is observed or otherwise comes into contact with
the environment. When this happens, both photons are knocked out of entanglement
and into the same definite polarization, regardless of the physical distance
The usual way of producing pairs of entangled photons is shining ultraviolet
laser light into a crystal, which transforms a tiny percentage of the
ultraviolet photons into entangled pairs of infrared photons. The Oxford
device bounces the entangled photon pairs back into the crystal while
the laser is still shining on it. For each pair sent back into the crystal,
four new pairs are generated.
The laser action produces more pairs of entangled photons for the same
amount of power as non-lasing schemes, "and, perhaps more importantly,
higher-number entangled photon states," she said.
Ordinary conversion produces about 5,000 detectable photon pairs per second,
said Lamas-Linares. "Our source in its current form would produce four
times more pairs, and the number would grow exponentially with the number
of passes." In addition, the device entangles groups of four photons.
"Current sources produce about one 4-photon state per minute, while our
source will amplify this by a factor of 16, making it feasible to perform
experiments on them," she said.
The Oxford device currently passes the light through the crystal only
twice. Ordinary lasers use a reflective chamber, or cavity, to bounce
light back and forth through a gas hundreds of times, each pass causing
the gas atoms to emit more photons.
The researchers' next step is to add a reflective cavity to their device,
making it more like a true laser and multiplying further the number of
entangled photons it could produce. "We are working on building a cavity
system... to obtain a more conventional lasing action," said Lamas-Linares.
The goal is to produce a device that can generate useful numbers of pairs
of entangled photons. "Entanglements are the main resource in quantum
information," said Lamas-Linares. "One of the main problems in the field
currently is to produce entanglement in a controllable and reliable way."
Current sources of entangled photons are not bright enough for some proposed
quantum information processing experiments and a brighter source would
make them possible, said Paul Kwiat, a professor of physics at the University
of Illinois. A true entangled-photon laser "would be a very bright source
of entanglement," he said.
The Oxford source of entangled photons could be used for quantum cryptography
in five years and is currently being used as a tool by physicists
to explore the fundamentals of quantum mechanics, said Lamas-Linares.
"That is really our main interest," she said.
Lamas-Linares' research colleagues were John C. Howell and Dik Bouwmeester
of the University of Oxford. They published the research in the August
30, 2001 issue of the journal Nature. The research was funded by the UK
Engineering and Physical Sciences Research Council (EPSRC), the UK Defense
Evaluation and Research Agency and the European Union (EU).
Timeline: 5 years
TRN Categories: Quantum Computing
Story Type: News
Related Elements: Technical paper, "Stimulated Emission
of Polarization-Entangled Photons," Nature, August 30, 2001
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Back in February 2020, scientists from the U.S. Department of Energy's Argonne National Laboratory and the University of Chicago revealed that they had achieved a quantum entanglement — in which the behavior of a pair two tiny particles becomes linked, so that their states are identical — over a 52-mile (83.7 kilometer) quantum-loop network in the Chicago suburbs.
You may be wondering what all the fuss is about, if you're not a scientist familiar with quantum mechanics — that is, the behavior of matter and energy at the smallest scale of reality, which is peculiarly different from the world we can see around us.
But the researchers' feat could be an important step in the development of a new, vastly more powerful version of the internet in the next few decades. Instead of the bits that today's network uses, which can only express a value of either 0 or 1, the future quantum internet would utilize qubits of quantum information, which can take on an infinite number of values. (A quibit is the unit of information for a quantum computer; it's like a bit in an ordinary computer).
That would give the quantum internet way more bandwidth, which would make it possible to connect super-powerful quantum computers and other devices and run massive applications that simply aren't possible with the internet we have now.
"A quantum internet will be the platform of a quantum ecosystem, where computers, networks, and sensors exchange information in a fundamentally new manner where sensing, communication, and computing literally work together as one entity, " explains David Awschalom via email. He's a spintronics and quantum information professor in the Pritzker School of Molecular Engineering at the University of Chicago and a senior scientist at Argonne, who led the quantum-loop project.
Explaining the Quantum Internet
So why do we need this and what does it do? For starters, the quantum internet is not a replacement of the regular internet we now have. Rather it would be a complement to it or a branch of it. It would be able to take care of some of the problems that plague the current internet. For instance, a quantum internet would offer much greater protection from hackers and cybercriminals. Right now, if Alice in New York sends a message to Bob in California over the internet, that message travels in more or less a straight line from one coast to the other. Along the way, the signals that transmit the message degrade; repeaters read the signals, amplify and correct the errors. But this process allows hackers to "break in" and intercept the message.
However, a quantum message wouldn't have that problem. Quantum networks use particles of light photons to send messages which are not vulnerable to cyberattacks. Instead of encrypting a message using mathematical complexity, says Ray Newell, a researcher at Los Alamos National Laboratory, we would rely upon the peculiar rules of quantum physics. With quantum information, "you can't copy it or cut it in half, and you can't even look at it without changing it." In fact, just trying to intercept a message destroys the message, as Wired magazine noted. That would enable encryption that would be vastly more secure than anything available today.
"The easiest way to understand the concept of the quantum internet is through the concept of quantum teleportation," Sumeet Khatri, a researcher at Louisiana State University in Baton Rouge, says in an email. He and colleagues have written a paper about the feasibility of a space-based quantum internet, in which satellites would continually broadcast entangled photons down to Earth's surface, as this Technology Review article describes.
"Quantum teleportation is unlike what a non-scientist's mind might conjure up in terms of what they see in sci-fi movies, " Khatri says. "In quantum teleportation, two people who want to communicate share a pair of quantum particles that are entangled. Then, through a sequence of operations, the sender can send any quantum information to the receiver (although it can't be done faster than light speed, a common misconception). This collection of shared entanglement between pairs of people all over the world essentially constitutes the quantum internet. The central research question is how best to distribute these entangled pairs to people distributed all over the world. "
Once it's possible to do that on a large scale, the quantum internet would be so astonishingly fast that far-flung clocks could be synchronized about a thousand times more precisely than the best atomic clocks available today, as Cosmos magazine details. That would make GPS navigation vastly more precise than it is today, and map Earth's gravitational field in such detail that scientists could spot the ripple of gravitational waves. It also could make it possible to teleport photons from distant visible-light telescopes all over Earth and link them into a giant virtual observatory.
"You could potentially see planets around other stars, " says Nicholas Peters, group leader of the Quantum Information Science Group at Oak Ridge National Laboratory.
It also would be possible for networks of super-powerful quantum computers across the globe to work together and create incredibly complex simulations. That might enable researchers to better understand the behavior of molecules and proteins, for example, and to develop and test new medications.
It also might help physicists to solve some of the longstanding mysteries of reality. "We don't have a complete picture of how the universe works," says Newell. "We have a very good understanding of how quantum mechanics works, but not a very clear picture of the implications. The picture is blurry where quantum mechanics intersects with our lived experience."
Challenges of Building the Quantum Internet
But before any of that can happen, researchers have to figure out how to build a quantum internet, and given the weirdness of quantum mechanics, that's not going to be easy. "In the classical world you can encode information and save it and it doesn't decay, " Peters says. "In the quantum world, you encode information and it starts to decay almost immediately. "
Another problem is that because the amount of energy that corresponds to quantum information is really low, it's difficult to keep it from interacting with the outside world. Today, "in many cases, quantum systems only work at very low temperatures," Newell says. "Another alternative is to work in a vacuum and pump all the air out. "
In order to make a quantum internet function, Newell says, we'll need all sorts of hardware that hasn't been developed yet. So it's hard to say at this point exactly when a quantum internet would be up and running, though one Chinese scientist has envisioned that it could happen as soon as 2030.
Originally Published: Mar 30, 2020


Artificial Intelligence has been around since the 1950's. Alan Turing envisioned a machine that could think. He devised a test, aptly named the Turing Test, published in an article titled Computing Machinery and Intelligence. He proposed the notion that a computational machine could answer a series of questions from a panel of judges. The responses would be rational, thoughtful, and indistinguishable to another human. Prior to that, Turing spent a decade creating the blueprint for "machine intelligence".
John McCarthy, a distinguished professor at MIT coined the term Artificial Intelligence and organized an international conference dedicated to the pursuit of AI. There he met Marvin Minsky and together they worked to advance the theories and concepts of bringing AI to life. They used the LISP language as their programming language of choice.
They ran into some obstacles, included limited processing power, limited storage capacity, high costs, lack of funding and the underlying complexity surrounding the concepts involved.
In the mid-1960s, an MIT professor, Joseph Weizenbaum, created a computer program named ELIZA. It simulated a virtual "doctor" and was able to interpret natural language input with a somewhat intelligent set of responses. Although limited in functionality, it gets credited for being one of the first AI programs in existence.
In the mid 1980's, a concept known as Backpropagation was created. This technology leveraged complex algorithms to process information based on a known set of data. The program received a set of input data, flowed through a series of Neurons, which performed a calculation, produced a number between 0 and 1, and based on the configuration, it either fired a signal to another connected neuron, similar to the synapses in the human brain. It then flowed into a series of "hidden" neurons which also had complex calculations. Finally, the data flowed to the end and produced a final response. That response was compared to the known data and if differences appeared, the set of data was flowed backwards through the neural network to recalculate the weights on each of the neurons. Soon, multi layered neural networks were created to increase the capacity and accuracy.
In 1984, the entire field of Artificial Intelligence slowed down. At a conference of American Association of Artificial Intelligence, the term AI Winter was used to define this stagnation in the field. Basically, the hype surrounding AI was under scrutiny by the funding groups such as government bureaucrats and venture capitalists. This pessimism pushed the AI field into obscurity for some time.
In the early to mid-1990s, AI was becoming known in the business world. Two of the main reasons were: the increase in compute power; and isolating specific problems within specific domains.
“An Intelligent Agent is a system that perceives its environment and takes action which maximize its chances of success.”
In 1997, IBM's Deep Blue knocked off the world chess champion Garry Kasparov.
Strong vs. Weak Artificial Intelligence
In the pursuit of Artificial Intelligence, there are basically two camps.
Weak or Narrow Artificial Intelligence
Personal Assistants typically reside on computers and smart phones. They can learn the behavior of the individual using the application. Preferences, places of travel, history of searches and browser trails, the application is able to inform its users to alter a course of action based on given parameters in real time. These assistants are becoming more precise as they are embedded into everyday applications.
Strong or Artificial General Intelligence
The second type of AI is known as Strong AI or Artificial General Intelligence. Strong AI or AGI is the intelligence of a machine that match or surpass the abilities of a human being in performing tasks. Some of its characteristics are having the ability to learn, reason, communicate in natural language, possess creativity, have morals and be self-aware.
When computers become aware of themselves, they will be able to recursively build machines that are more intelligent than themselves. This will lead to an exponential increase in the pace of progress, eventually moving the intelligence of machines beyond the comprehension of human beings. This hypothetical event is commonly referred to as the Singularity.
Artificial Neural Networks
Artificial neural networks (ANN) are about function approximation. Basically, you have a Neural Network. The Neural Network takes in Input. That input is interpreted by Neurons. These neurons have approximated weights. Based on the results of the calculation, if they exceed a specified threshold, it fires a 1 or 0 as output, which is sent downstream to other Neurons. It's possible to over-train a model, in which the output has to contort itself drastically, which sends the results into a tailspin and results become nonsense.
Due to the complexity of the human brain, researchers have not been able to reproduce it at this point in time. Artificial Neural Networks attempt to simulate that complexity, with some level of accuracy. There are pre-canned packages you can purchase which do all the heavy lifting for you, exposing this technology to more people.
Artificial Quantum Neural Networks
Researchers are combining Quantum Physics with computers. Although this research is in the early stages, these computers are known as Quantum Turing Machines. In classical computing, everything is based off of the concept of “binary” or being in the state 0 or 1. In the Quantum world, the binary unit is replaced with a unit named Quantum Bit or Qubit for short. When Quantum Mechanic principles are applied, the neural unit can result in more than two states, 0, 1 or both. This has great implications as these new systems can perform calculations extremely fast and can actually solve some problems deemed impossible in the classical binary approach. One example is the ability for quantum computers to decrypt public keys, which are the foundation for internet security today. The underlying rules that make up Quantum physics are quite complex. One company leading the charge with Quantum computers is D-Wave. They define Quantum Computation as follows:
“Rather than store information as 0s or 1s as conventional computers do, a quantum computer uses qubits – which can be a 1 or a 0 or both at the same time. This “quantum superposition”, along with the quantum effects of entanglement and quantum tunneling, enable quantum computers to consider and manipulate all combinations of bits simultaneously, making quantum computation powerful and fast.“
This cutting edge technology is making strides in problem solving and could potentially be used to advance the world of Artificial Intelligence by leaps and bounds.
Morals and Ethics
With the rise of intelilgent machines, some thought needs to be spent on the Ethical consequences. How will AI and Robots behave amongst humans? What will determine the moral blueprints for acceptable behavior? What if an AI kills a human? Would it go to machine prison? Could it get married? Or buy insurance? Who owns it? Will machines have funerals? Can a machine be sold? What if it steals? Should they be entitled to vote? What if your machine gets stolen or abducted? Can they reproduce? If so, are they responsible for the care of their youth until they graduate from High School? Can you euthanize a robot? Do robots get paid for services rendered?
Every day we get closer to the reality of Artificial General Intelligence. At some point in the future, machines will be integrated into our society. It's up to us, now, to determine the roles, rights, duties and responsibilities assigned to our new intelligent beings.
Current AI Organizations
One organization dedicated to the pursuit of Artificial Intelligence was created by Paul Allen, one of the original founders of Microsoft, called Allen Institute for Artificial Intelligence with the moto:
“Our mission is to contribute to humanity through high-impact AI research and engineering.
“OpenAI is a non-profit artificial intelligence research company. Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.”
“We’re committed to advancing the field of machine intelligence and developing technologies that give people better ways to communicate. In the long term, we seek to understand intelligence and make intelligent machines.”
Microsoft Research has an Artificial Intelligence (AI) Group.
“The Artificial Intelligence (AI) group consists of an elite team of researchers who have strong expertise in artificial intelligence, machine learning, game theory, and information retrieval. The group is devoted to the following research directions: large-scale distributed machine Learning, cloud computing, robot, game-theoretic machine learning, and deep learning techniques for text mining.”
IBM has a research team, going back to the 1950’s when AI was first introduced. IBM is known for their Cognitive Machine called Watson. Another leading tech company, Baidu, has a research facility in Silicon Valley called Silicon Valley AI Lab.
Intelligent Machines have grown since the early days of the mid 1950's. With the increases in storage capacity, compute power, accessibe software, shared knowledge and technogology advances over the past 60 years, we've witness the rise of Artificial Intelligence into smart applications called Personal Assistants. How soon until we make another leap into the world of Artificial General Intelligence, where machines can learn and interact in real time and pass the Turning test? How soon will machines work side by side with humans to solve complex problems, reduce costs and make the world a better place. True artificial intelligence could be integrated into mainstream society sooner than we think.


Mapping quantum structures with light to unlock their capabilities
Rather than installing new “2D” semiconductors in devices to see what they can do, this new method puts them through their paces with lasers and light detectors.
A new tool that uses light to map out the electronic structures of crystals could reveal the capabilities of emerging quantum materials and pave the way for advanced energy technologies and quantum computers, according to researchers at the University of Michigan, the University of Regensburg and the University of Marburg.
A paper on the work is published in Science.
Applications include LED lights, solar cells and artificial photosynthesis.
“Quantum materials could have an impact way beyond quantum computing,” said Mackillo Kira, a professor of electrical engineering and computer science at the University of Michigan, who led the theory side of the new study. “If you optimize quantum properties right, you can get 100% efficiency for light absorption.”
Silicon-based solar cells are already becoming the cheapest form of electricity, although their sunlight-to-electricity conversion efficiency is rather low, about 30%. Emerging “2D” semiconductors, which consist of a single layer of crystal, could do that much better—potentially using up to 100% of the sunlight. They could also elevate quantum computing to room temperature from the near-absolute-zero machines demonstrated so far.
“New quantum materials are now being discovered at a faster pace than ever,” said Rupert Huber, a professor of physics at the University of Regensburg in Germany, who led the experimental work. “By simply stacking such layers one on top of the other under variable twist angles, and with a wide selection of materials, scientists can now create artificial solids with truly unprecedented properties.”
The ability to map these properties down to the atoms could help streamline the process of designing materials with the right quantum structures. But these ultrathin materials are much smaller and messier than earlier crystals, and the old analysis methods don’t work. Now, 2D materials can be measured with the new laser-based method at room temperature and pressure.
The measurable operations include processes that are key to solar cells, lasers and optically driven quantum computing. Essentially, electrons pop between a “ground state,” in which they cannot travel, and states in the semiconductor’s “conduction band,” in which they are free to move through space. They do this by absorbing and emitting light.
By simply stacking such layers one on top of the other under variable twist angles, and with a wide selection of materials, scientists can now create artificial solids with truly unprecedented properties.”Rupert Huber, University of Regensburg professor of physics
The quantum mapping method uses a 100 femtosecond (100 quadrillionths of a second) pulse of red laser light to pop electrons out of the ground state and into the conduction band. Next the electrons are hit with a second pulse of infrared light. This pushes them so that they oscillate up and down an energy “valley” in the conduction band, a little like skateboarders in a halfpipe.
The team uses the dual wave/particle nature of electrons to create a standing wave pattern that looks like a comb. They discovered that when the peak of this electron comb overlaps with the material’s band structure—its quantum structure—electrons emit light intensely. That powerful light emission along, with the narrow width of the comb lines, helped create a picture so sharp that researchers call it super-resolution.
By combining that precise location information with the frequency of the light, the team was able to map out the band structure of the 2D semiconductor tungsten diselenide. Not only that, but they could also get a read on each electron’s orbital angular momentum through the way the front of the light wave twisted in space. Manipulating an electron’s orbital angular momentum, known also as a pseudospin, is a promising avenue for storing and processing quantum information.
In tungsten diselenide, the orbital angular momentum identifies which of two different “valleys” an electron occupies. The messages that the electrons send out can show researchers not only which valley the electron was in but also what the landscape of that valley looks like and how far apart the valleys are, which are the key elements needed to design new semiconductor-based quantum devices.
For instance, when the team used the laser to push electrons up the side of one valley until they fell into the other, the electrons emitted light at that drop point too. That light gives clues about the depths of the valleys and the height of the ridge between them. With this kind of information, researchers can figure out how the material would fare for a variety of purposes.
The paper is titled, “Super-resolution lightwave tomography of electronic bands in quantum materials.” This research was funded by the Army Research Office, the German Research Foundation and the U-M College of Engineering Blue Sky Research Program. The Army Research Office is an element of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory.


Chips measure electron spin
Technology Research News
Practical quantum computers are at least a decade away, and some researchers are betting that they will never be built.
This is because controlling individual particles like atoms, electrons and photons is extraordinarily challenging. Information carried in particles always comes in shades of gray and can be corrupted or wiped out by the slightest wisp of energy from the environment.
A pair of experiments has brightened prospects for quantum computing, however, by making it more likely that a practical means of reading electron-based quantum bits, or qubits, can be developed. Research teams from the University of California at Los Angeles and from Delft University of Technology in the Netherlands have developed electronic methods of detecting the spins of individual electrons.
Spin is a property of electrons that is akin to the rotation of a top. The two spin directions, spin up and spin down, are magnetically opposite, like the two poles of a kitchen magnet. The spins can represent the 1s and 0s and digital information.
Particles that are isolated from their environment are in the weird quantum state of superposition, meaning they are in some mix of the two spin directions. This means a qubit can be in some mix of 1 and 0, which allows a string of qubits to represent every binary number at once.
This gives a quantum computer the ability to check every possible answer to a problem with a single set of operations, promising speedy solutions to problems that classical computers have to churn through one answer at a time. These include factoring large numbers, a problem whose difficulty is the foundation of most of today's security codes.
Electronic equipment has become sensitive enough that it is no longer difficult to detect the presence of a single electron. But detecting an electron's spin orientation is another matter.
In recent years, researchers have succeeded in detecting electron spin optically using specialized laser setups. The key to using electron spin in quantum computers whose architecture is similar to today's computer chips is being able to detect the spin orientation electronically.
The UCLA team's method of electron spin detection uses devices that are already mass-produced. The researchers flipped a single electron spin in a commercial transistor chip, and detected the spin flip by measuring changes in current flowing through the device.
Several proposed quantum computer architectures call for circuits that can be manufactured using today's chipmaking techniques. "The transistor structure used for our experiment [closely] resembles some proposed spin-based qubit architectures," said Hong-Wen Jiang, a professor of physics at the University of California at Los Angeles. "We believe that our read-out scheme can be readily adapted in a scalable quantum information processor," he said.
Electrons travel through a transistor via a semiconductor channel that is electrically insulated. The transistor is controlled by a gate electrode, which produces an electric field that penetrates the insulator and increases the conductivity of the channel, allowing electrons to flow. Occasionally defects occur, producing one or more spots in the insulator that can draw individual electrons from the channel and trap them.
The researchers sought out transistors that contained single defect traps, set the gate voltage so that the trap had an equal chance of attracting an electron or not, and applied a large magnetic field to the trap.
A high magnetic field causes electrons in the spin-down state to have slightly more energy than spin-up electrons. The researchers flipped the electron's spin with a microwave pulse. An electron that is spin-up fills the trap but a higher-energy spin-down electron leaves room, electrically speaking, for a second, spin-up electron from the channel to join it in the trap.
The difference between having one and having two electrons in the trap is measurable as a change in the current flowing through the transistor. Two electrons decrease the amount of current. The researchers can observe a microwave pulse flipping the spin of an electron in the trap by measuring the current.
In its present form, the UCLA device uses a randomly-positioned defect as its electron trap, and electrons cycle through the trap rapidly enough that the spin measurement is an average of a few thousand electrons. The researchers are conducting similar experiments in specially designed semiconductor structures that promise greater control over electron spin, the ability to entangle two spins, and to eventually build a scalable quantum processor, said Jiang.
Properties of entangled particles, including spin, remain in lockstep regardless of the distance between them. Entanglement is a basic requirement of quantum algorithms, and entangled electrons would enable information to be teleported between circuits within a quantum computer.
Meanwhile, the Delft team devised a way to measure the spin of an electron trapped in a quantum dot -- a tiny semiconductor device that produces electric fields capable of confining one or a few electrons. "The technique works fully electrically, and is therefore... suitable for integration with existing solid-state technologies," said Jeroen Elzerman, a researcher at Delft University of Technology.
The researchers applied a large magnetic field to the trapped electron, which caused the spin-down state to have slightly more energy than the spin-up state. They tuned the quantum dot's electric field so that the energy of a spin-down electron was just high enough for it to escape, but the energy of a spin-up electron was below the threshold. Therefore, if an electron is present it is spin-up, and if the quantum dot is empty, the electron that escapes is spin-down.
The researchers next step is to to use pulsed microwaves to control the exact quantum superposition of the spin, said Elzerman. They then plan to entangle two spins. "When this is done, all the basic ingredients for a quantum computer are in place," he said.
Coupling many spins and controlling their interactions accurately
enough to perform a quantum algorithm is a matter of improving control
over the fabrication process, said Elzerman. "We need cleaner and purer
materials and more reproducible electron beam lithography so that all
dots on a single chip are really identical," he said.
Jiang's research colleagues were Ming Xiao and Eli Yablonovitch
of UCLA, and Ivar Martin of Los Alamos National Laboratory. They published
the research in the July 22, 2004 issue of Nature. The research
was funded by the Defense Advanced Research Projects Agency (DARPA) and
the Defense Microelectronics Activity (DMEA).
Elzerman's research colleagues were Ronald Hanson, Laurens Willems
van Beveren, Benoit Witkamp, Lieven Vandersypen and Leo Kouwenhoven. They
published the research in the July 22, 2004 issue of Nature. The
research was funded by DARPA, the Office of Naval Research, the European
Union and the Dutch Organization for Fundamental Research on Matter (FOM).
Timeline: 10 years; 10-20 years
TRN Categories: Physics; Quantum Computing and Communications
Story Type: News
Related Elements: Technical papers, "Electrical detection of the spin resonance of a single electron in a silicon field-effect transistor," Nature, July 22, 2004; "Single-shot read-out of an individual electron spin in a quantum dot," Nature, July 22, 2004
August 11/18, 2004
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D-Wave implements quantum annealing, while Google has digitized adiabatic quantum computation.
D-Wave advertises their line of quantum computers as having thousands of qubits, though these systems are designed specifically for quadratic unconstrained binary optimization. More information about D-Wave's manufacturing process.
It is D-Wave's claim that: "It is best suited to tackling complex optimization problems that exist across many domains such as":
Sampling / Monte Carlo
Pattern recognition and anomaly detection
Software / hardware verification and validation
Bioinformatics / cancer research
D-Wave's QPU uses quantum annealing (QA), a metaheuristic for finding the global minimum of a given objective function over a given set of candidate solutions (candidate states), by a process using quantum fluctuations. Quantum annealing is used mainly for problems where the search space is discrete (combinatorial optimization problems) with many local minima; such as finding the ground state of a spin glass.
D-Wave's architecture differs from traditional quantum computers. It is not known to be polynomially equivalent to a universal quantum computer and, in particular, cannot execute Shor's algorithm because Shor's Algorithm is not a hillclimbing process. Shor's Algorithm requires a universal quantum computer. D-wave claims only to do quantum annealing.
Experimental quantum annealing: case study involving the graph isomorphism problem
Defects in Quantum Computers
Google's claim is: "The goal of the Google Quantum AI lab is to build a quantum computer that can be used to solve real-world problems. Our strategy is to explore near-term applications using systems that are forward compatible to a large-scale universal error-corrected quantum computer using linear array technology".
"State preservation by repetitive error detection in a superconducting quantum circuit"
"Digitized adiabatic quantum computing with a superconducting circuit"
Inaccurate layperson' explanation:
A Graphic Card has more Cores than a CPU.
GPUs are optimized for taking huge batches of data and performing the same operation over and over very quickly, unlike PC microprocessors, which tend to skip all over the place.
Architecturally, the CPU is composed of just few cores with lots of cache memory that can handle a few software threads at a time. In contrast, a GPU is composed of hundreds of cores that can handle thousands of threads simultaneously.
Technical, but not overly complicated, layperson's explanation:
Why is Google's new device newsworthy then? Is it better than D-Wave's machine in some respects? If so, how?
There are "Annealing QPUs" and "Universal QPUs" as explained above, an incomplete list is offered on Wikipedia's page: "List of Quantum Processors".
In quantum annealing, the strength of transverse field determines the quantum-mechanical probability to change the amplitudes of all states in parallel.
In the case of annealing a purely mathematical objective function, one may consider the variables in the problem to be classical degrees of freedom, and the cost functions to be the potential energy function (classical Hamiltonian). Moreover, it may be able to do this without the tight error controls needed to harness the quantum entanglement used in more traditional quantum algorithms.
That makes it easier to provide more qubits, but the kinds of problems they are able to solve is more limited than the qubits provided in a universal QPU.
In general the ground state of a Hamiltonian can be used to encode a wider variety of problems than NP (know QMA-complete problems), and so decision to focus on NP optimization problems has led to restrictions which prevent the device from being used for general purpose quantum computing (even if noise was not an issue).
There is an interesting subtlety as regards noise: If you add noise to the adiabatic algorithm, it degrades gracefully into one of the best classical algorithms for the same problem.
The adiabatic model can encode universal quantum computation, however the limitations of DWave's implementation means that specific machine cannot.
Google's universal QPU can solve a wider range of problems than D-Wave's QPU (in it's current implementation) if they can solve their decoherence problem.
In the case of Google's Bristlecone caution is warranted. Bristlecone is a scaled up version of a 9-qubit Google design that has failed to yield acceptable error rates for a commercially viable quantum system. In real-world settings, quantum processors must have a two-qubit error rate of less than 0.5 percent. According to Google, its best result has been a 0.6 percent error rate using its much smaller 9-qubit hardware.
The commercial success of quantum computing will require more than high qubit numbers. It will depend on quality qubits with low error rates and long-lasting circuit connectivity in a system with the ability to outperform classic computers in complex problem solving, i.e., “quantum supremacy”.
Google will use it's record number of more useful qubits to correct the error rate of those error prone qubits.
More qubits are needed to solve bigger problems and longer living (coherent) qubits to are needed to hold the information long enough for the quantum algorithm to run. IBM describes the problem as: "Quantum Volume: preferring fewer errors per qubit over more qubits", see also: What is the leading edge technology for creating a quantum computer with the fewest errors? .
Google plans to use Surface Codes to resolve this problem, for more info and a comparison to spin glass models see: "Quantum Computation with Topological Codes: from qubit to topological fault-tolerance".
IBM has a video titled: "A Beginner’s Guide to Quantum Computing" which explains quantum computing for laypersons in under 20 minutes.
Microsoft intends to take the wind from everyone's sails with the integration of Q# (Q sharp) into Visual Studio and some information about their Majorana fermion based qubits, and a great reduction in the error rate, in the months to come. See: "Majorana-based fermionic quantum computation". The will enable a system that uses less than 25% as many better qubits to accomplish the same amount of work as Google's qubits.
The website "The Next Platform" describes the current situation as: "Quantum Computing Enters 2018 Like it's 1968".


Nowadays, the very abstract ideas underlying the quantum physics are being translated into reality thanks to new technological capabilities in the field of nanotechnology and optical interactions. One of these ideas, the idea of a quantum internet and a quantum computer, will be discussed further in this article. While the subject is very broad, we’ll try to summarize the basic ideas behind these technologies.
Quantum Internet allows to send quantum data (quantum bits or qubits) from one quantum computer to another. The media here is either a fiber optic cable or a free space connection with a clear line of sight between the starting and the destination point of a signal. Classical computers work with conventional bits that can be either zero or one. Quantum mechanics, however, allows qubits to be in a superposition state that can be 1 and 0 at the same time. Therefore, we can encode more information in qubits than in conventional bits. The amount of information that can be stored and processed using qubits is 2n, where n is the number of qubits. So, in two qubit systems, we need four numbers (bits) to determine the state of the system. To define the state of the three qubits system we need 8 numbers. If we have 300 qubits, the equivalent of classical bit information is 2300.
Quantum computer is a computer where the number of operations grows exponentially. However, the improvement is not in the speed of an individual operation but rather a total number of operations to write the result. Therefore, quantum computers are not generally faster, they are faster only for specific types of calculations . We can easily grasp this concept by playing the light switch game provided by D-Wave . The game explains why a quantum computer is faster than a conventional computer in a process of finding the best combination of switches when a number of the switches is large. As stated, “The quantum computer begins with the bits in superposition (the switch can be in both ON and OFF states), ends with them behaving as regular classical bits, and finds the answer along the way”. However, with only 500 switches, there is not enough time in the universe to check all the configurations when conventional processors are used.
So far, only a small number of quantum algorithms have been found.
Here are some of the most famous ones:
Shor’s Algorithm (factorization)
Grover’s Algorithm (quick search in an unordered database)
Deutsch–Jozsa Algorithm (produces an answer; the function is either constant or balanced)
Let’s review the Shor’s algorithm in a bit more detail. It allows to solve any of the two mathematically equivalent problems below:
- Finding the period of a complex periodic function or
- Decomposing a very large number into the prime factors
The second of these tasks is of significant practical importance since it is used in cryptography. When encrypting and decrypting secret messages (public key encryption) large numbers are used for which their factorization is known. It is clear that such numbers are easy to obtain: it is enough to multiply a large number of prime numbers, and we get a very large number for which the factorization is known. The recipient of the encoded secret message can decode it because the decoding procedure uses factorization of a long number, and he/she knows this decomposition.
If a third party could factor this number into the prime factors, he/she would also be able to decode the message. However, this decomposition takes a lot of time. Therefore, from a practical point of view, it is impossible to decode such messages. But if the third party would’ve had a quantum computer, then he/she could decompose long numbers into simple factors quite fast and therefore could easily decipher such messages. The common cryptography method used today would stop working. This is one of the arguments that make the creation of a quantum computer important.
On the other hand, quantum networking provides another secure communication benefit. Quantum Key Distribution (QKD) enables secure communication whose security relays on quantum mechanics. For instance, the spin of an electron can be used as a qubit since it can undergo transitions between the spin-up and spin-down quantum states, represented classically by 0 and 1. In other words, qubits are based on physicals properties of the particles such as electron spins or polarization of photon. However, if we would want to measure the electron’s spin, some of its properties would change. If we were to apply the temperature near the absolute zero (-273 Celsius), the electron would be spin down ↓. If we were to write the information to a qubit we would put the electron into a spin-up state ↑ by hitting it with a pulse of microwaves with specific frequency. We would not know the spin of an electron until we measure it. And when we measure it, the qubit’s physical properties are changed. Therefore, it is also impossible to make exact copies of qubits or to clone it. This is known as a quantum no-cloning theorem. Qubits perfectly suit for secure communication. If Bob and Alice exchange an encryption key using qubits and Eve intercepts communication, both Alice and Bob know that someone messed with qubits as the physicals properties of the qubits changed. Therefore, extracting quantum information without leaving a trace is impossible. The presence of Eve’s eavesdropping communication, can be easily detected.
Nowadays, we can send qubits to short distances over telecommunication fibers up to 200 kilometers. The reason for that is Decoherence – a situation where the system being measured loses its specific quantum properties. In other words, the pure state quickly turns into a mixture when the quantum system interacts with the environment. So, the real challenge in building quantum Internet is to send qubits further than a few hundred kilometers. The single photon sent over a fiber optic cable can be lost. As we know, qubits cannot be copied or amplified so they cannot be resent without a notice. To solve this issue, the box called a quantum repeater is placed in the middle of the communication line and the pair of photons is exchanged between the repeater and the quantum computer on the left side of the line. Similarly, another pair of photons is exchanged between the repeater and a quantum computer located to the right of the communication line. Quantum repeaters are crucial for entanglement over long distances using fiber optic cables. The vision is to build a long-range quantum internet that will operate in parallel to the Internet we know today.
We have already mentioned, that transmission of quantum signals over long distances is prevented by fiber attenuation and the no-cloning theorem. Therefore, one of the realistic scenarios is that the future Quantum Internet will consist of a global network of quantum repeaters that are developed and used in order to extend the range of communication. However, there is also another approach to this problem which is based on the deployment of satellite technology. China launched world’s first quantum communication satellite Miciusin 2016, and has since been busy testing and extending the limitations of sending entangled photons from space to ground stations on Earth and back again . Chinese and European researchers have tested the system by creating secure video conference between Europe and China.
There are certain issues associated with quantum computing besides decoherence, such as the search for new algorithms as well as new methods of error correction. All of these problems however can be described in one phrase – scalability issues.
Quantum computers are the “holy grail” of modern physics and informatics. The idea of a quantum computer and a quantum network looks unrealistic at first. A regular classical computer was probably perceived the same way at the time of Charles Babbage, the invention of which happened only a hundred years later. QCs on two or three qubits already exist, but they require the use of high technologies (pure substances, precise implantation of individual atoms, a highly accurate measurement system, etc.). However, as mentioned earlier, the main challenge is not the technological one but the fundamental one of scalability.
It is unlikely that quantum computers will replace the classical computers in the near future. We can only speculate that the QCs would be put into clouds to offer unique services whereas personal computers would transmit or access the quantum-encrypted information through the cloud-based QCs.
Hopefully, the scientific and technical progress of our time is fast enough, and we will not have to wait too long for quantum computing to become a common reality.
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As early as 1959 the American physicist and Nobel laureate Richard Feynman noted that, as electronic components begin to reach microscopic scales, effects predicted by quantum mechanics occur—which, he suggested, might be exploited in the design of more powerful computers. In particular, quantum researchers hope to harness a phenomenon known as superposition. In the quantum mechanical world, objects do not necessarily have clearly defined states, as demonstrated by the famous experiment in which a single photon of light passing through a screen with two small slits will produce a wavelike interference pattern, or superposition of all available paths. (Seewave-particle duality.) However, when one slit is closed—or a detector is used to determine which slit the photon passed through—the interference pattern disappears. In consequence, a quantum system “exists” in all possible states before a measurement “collapses” the system into one state. Harnessing this phenomenon in a computer promises to expand computational power greatly. A traditional digital computer employs binary digits, or bits, that can be in one of two states, represented as 0 and 1; thus, for example, a 4-bit computer register can hold any one of 16 (24) possible numbers. In contrast, a quantum bit (qubit) exists in a wavelike superposition of values from 0 to 1; thus, for example, a 4-qubit computer register can hold 16 different numbers simultaneously. In theory, a quantum computer can therefore operate on a great many values in parallel, so that a 30-qubit quantum computer would be comparable to a digital computer capable of performing 10 trillion floating-point operations per second (TFLOPS)—comparable to the speed of the fastest supercomputers.
During the 1980s and ’90s the theory of quantum computers advanced considerably beyond Feynman’s early speculations. In 1985 David Deutsch of the University of Oxford described the construction of quantum logic gates for a universal quantum computer, and in 1994 Peter Shor of AT&T devised an algorithm to factor numbers with a quantum computer that would require as few as six qubits (although many more qubits would be necessary for factoring large numbers in a reasonable time). When a practical quantum computer is built, it will break current encryption schemes based on multiplying two large primes; in compensation, quantum mechanical effects offer a new method of secure communication known as quantum encryption. However, actually building a useful quantum computer has proved difficult. Although the potential of quantum computers is enormous, the requirements are equally stringent. A quantum computer must maintain coherence between its qubits (known as quantum entanglement) long enough to perform an algorithm; because of nearly inevitable interactions with the environment (decoherence), practical methods of detecting and correcting errors need to be devised; and, finally, since measuring a quantum system disturbs its state, reliable methods of extracting information must be developed.
Plans for building quantum computers have been proposed; although several demonstrate the fundamental principles, none is beyond the experimental stage. Three of the most promising approaches are presented below: nuclear magnetic resonance (NMR), ion traps, and quantum dots.
In 1998 Isaac Chuang of the Los Alamos National Laboratory, Neil Gershenfeld of the Massachusetts Institute of Technology (MIT), and Mark Kubinec of the University of California at Berkeley created the first quantum computer (2-qubit) that could be loaded with data and output a solution. Although their system was coherent for only a few nanoseconds and trivial from the perspective of solving meaningful problems, it demonstrated the principles of quantum computation. Rather than trying to isolate a few subatomic particles, they dissolved a large number of chloroform molecules (CHCL3) in water at room temperature and applied a magnetic field to orient the spins of the carbon and hydrogen nuclei in the chloroform. (Because ordinary carbon has no magnetic spin, their solution used an isotope, carbon-13.) A spin parallel to the external magnetic field could then be interpreted as a 1 and an antiparallel spin as 0, and the hydrogen nuclei and carbon-13 nuclei could be treated collectively as a 2-qubit system. In addition to the external magnetic field, radio frequency pulses were applied to cause spin states to “flip,” thereby creating superimposed parallel and antiparallel states. Further pulses were applied to execute a simple algorithm and to examine the system’s final state. This type of quantum computer can be extended by using molecules with more individually addressable nuclei. In fact, in March 2000 Emanuel Knill, Raymond Laflamme, and Rudy Martinez of Los Alamos and Ching-Hua Tseng of MIT announced that they had created a 7-qubit quantum computer using trans-crotonic acid. However, many researchers are skeptical about extending magnetic techniques much beyond 10 to 15 qubits because of diminishing coherence among the nuclei.
Just one week before the announcement of a 7-qubit quantum computer, physicist David Wineland and colleagues at the U.S. National Institute for Standards and Technology (NIST) announced that they had created a 4-qubit quantum computer by entangling four ionized beryllium atoms using an electromagnetic “trap.” After confining the ions in a linear arrangement, a laser cooled the particles almost to absolute zero and synchronized their spin states. Finally, a laser was used to entangle the particles, creating a superposition of both spin-up and spin-down states simultaneously for all four ions. Again, this approach demonstrated basic principles of quantum computing, but scaling up the technique to practical dimensions remains problematic.
Quantum computers based on semiconductor technology are yet another possibility. In a common approach a discrete number of free electrons (qubits) reside within extremely small regions, known as quantum dots, and in one of two spin states, interpreted as 0 and 1. Although prone to decoherence, such quantum computers build on well-established, solid-state techniques and offer the prospect of readily applying integrated circuit “scaling” technology. In addition, large ensembles of identical quantum dots could potentially be manufactured on a single siliconchip. The chip operates in an external magnetic field that controls electron spin states, while neighbouring electrons are weakly coupled (entangled) through quantum mechanical effects. An array of superimposed wire electrodes allows individual quantum dots to be addressed, algorithms executed, and results deduced. Such a system necessarily must be operated at temperatures near absolute zero to minimize environmental decoherence, but it has the potential to incorporate very large numbers of qubits.


Flat solar panels still face big limitations when it comes to making the most of the available sunlight each day. A new spherical solar cell design aims to boost solar power harvesting potential from nearly every angle without requiring expensive moving parts to keep tracking the sun’s apparent movement across the sky.
The spherical solar cell prototype designed by Saudi researchers is a tiny blue sphere that a person can easily hold in one hand like a ping pong ball. Indoor experiments with a solar simulator lamp have already shown that it can achieve between 15 percent and 100 percent more power output compared with a flat solar cell with the same ground area, depending on the background materials reflecting sunlight into the spherical solar cell. The research group hopes its nature-inspired design can fare similarly well in future field tests in many different locations around the world.
“The placement and shape of the housefly’s eyes increase their angular field of view so they can see roughly 270 degrees around them in the horizontal field,” says Nazek El-Atab, a postdoctoral researcher in microsystems engineering at the King Abdullah University of Science and Technology (KAUST). “Similarly, the spherical architecture increases the ‘angular field of view’ of the solar cell, which means it can harvest sunlight from more directions.”
To create the spherical solar cell design, El-Atab and her colleagues built upon their previous work, which demonstrated how to create thinner and more flexible solar cell designs based on a corrugated groove technique. The new work is detailed in a paper that has been submitted for review to the journal MRS Communications.
Measurement setup of the spherical solar cell under a solar simulator in air and using a regular a white paper as the reflective background material. Photo: Nazek El-Atab/KAUST
Testing with the solar simulator lamp showed that the spherical solar cell provided 24 percent more power output over a traditional flat solar cell upon immediate exposure to sunlight. That power advantage jumped to 39 percent after both types of solar cells had begun to heat up and suffered some loss in power efficiency—an indication that the spherical shape may have some advantages in dissipating heat.
The spherical solar cell also delivered about 60 percent more power output than its flat counterpart when both could collect only scattered sunlight under a simulated roof rather than receiving direct sunlight. Additional experiments with different reflective backgrounds—including an aluminum cup, aluminum paper, white paper, and sand—showed that the hexagonal aluminum cup background helped the spherical solar cell outperform the flat solar cell by 100 percent in terms of power output.
The Saudi team created the spherical solar cell using the monocrystalline silicon solar cells that currently account for almost 90 percent of the world’s solar power production. That choice sprang from the goal of helping to maximize the light-harvesting potential of such solar cells, along with the aim of potentially making it easier to scale up production if the design proves cost efficient.
“What surprises me is the authors have demonstrated the ultra-flexibility that can be achieved with rigid silicon solar cells using the corrugation technique in a series of articles,” says Zhe Liu, a postdoctoral researcher in solar engineering at MIT, who was not involved in the study. “I’m more excited about the ability to make spherical cells, which means you can have industrial IBC-type (interdigitated back contact) silicon solar cells cover any shapes and ‘solarize’ everywhere.”
Previous solar cell designs have fabricated tiny microscale spherical cells—sometimes made with nanowires or quantum dot cells—on top of a flat surface to help better collect both direct and scattered sunlight, says Rabab Bahabry, an assistant professor of physics at the University of Jeddah in Saudi Arabia. But the larger spherical solar cell may offer improved efficiency and coverage compared with the microsphere arrays when it comes to collecting sunlight reflected from background surfaces.
Creating the large spherical solar cell required the researchers to etch alternating grooves in 15 percent of a flat solar cell to make a pattern resembling a band of elliptical shapes connected at the middle. A CO2 laser created the appropriate pattern in a polymeric hard mask covering the solar cell and allowed a deep reactive ion etching tool to create grooves in the exposed areas of the silicon solar cell. The flex and bend in those groove areas allowed the researchers to subsequently fold the solar cell into a spherical shape.
Dust accumulation on a spherical solar cell is limited to the silicon area with a small tilt angle. Image: Rabab Bahabry/University of Jeddah and KAUST
The loss of solar cell material in the areas that have been etched out reduces the overall potential solar power output. But the researchers see cost over time favoring spherical solar cells over flat solar cells in certain parts of the world because the spherical design is less prone to dust accumulation and may help dissipate heat that might otherwise reduce the solar cell’s efficiency. In addition, the spherical solar cells don’t require additional costly moving parts to continually track the sun.
Still, the spherical solar cells may not replace traditional solar cell technology at utility-scale solar power plants, says Liu at MIT. In his view, this particular spherical solar cell design could find use in more niche market applications. He noted that one of his colleagues is currently searching for a solar cell design to cover a golf ball so that it can power a tracker inside the ball. But Liu sees much promise in such ultra-flexible solar cell designs being installed in buildings, cars, or even mobile devices.
“The application of spherical design may seem very limited, but the ability to make commercial silicon solar cells into any shapes would enable broad adaption of photovoltaic in autonomous devices, such as IoT (Internet of Things) sensors, and autonomous vehicles,” Liu says. “If we can fully power these autonomous devices with shaped photovoltaic panels, this could be a game changer.”
For future testing, Liu says he would like to see how the spherical solar cell performs in a wide array of both outdoor and indoor lighting environments at different times of day. He also wants to see how well the spherical solar cells can be integrated into certain applications that they might power. And he is curious about seeing a “quantified cost” summary of all the processing steps required to make such spherical solar cells in order to better understand the technology’s commercialization potential.
The Saudi researchers had to manually fold and form their spherical solar cells in their latest demonstration, but they have already begun designing and developing ways to automate the process using “robotic hands” to mimic the manual folding, says Muhammad Mustafa Hussain, a professor of electrical and computer engineering at KAUST who was one of the study’s coauthors.
Eventually, Hussain and his colleagues envision building and testing large arrays of the spherical solar cells. And they’re already working on new shapes that resemble tents or umbrellas to see if those offer any advantages. They are also integrating solar cells with the surfaces of drones that have unusual shapes.
The COVID-19 pandemic that forced the closure of research labs has delayed the Saudi group’s initial plans for outdoor testing. But Hussain says the group still plans to move forward with field trials before the end of 2020. He expects help from the KAUST alumni network in eventually testing the spherical solar cells in California, along with countries such as Bangladesh, China, India, South Korea, Germany, Spain, Brazil, Colombia, Mexico, South Africa, Australia, and New Zealand.
“We will be creating arrays of spherical cells for 100-square-foot to 1,000-square-foot areas, and will compare functionality over cost benefit with that of traditional cells,” Hussain says. “Next, we will deploy it in different geographic locations throughout the year to understand its performance and reliability.”
Editor’s note: A correction to this article was made on 16 June 2020. The sentence on indoor experiments was revised to correct an inaccurate interpretation of the power output comparison between the spherical solar cell and flat solar cell in the submitted paper.
Jeremy Hsu has been working as a science and technology journalist in New York City since 2008. He has written on subjects as diverse as supercomputing and wearable electronics for IEEE Spectrum. When he’s not trying to wrap his head around the latest quantum computing news for Spectrum, he also contributes to a variety of publications such as Scientific American, Discover, Popular Science, and others. He is a graduate of New York University’s Science, Health & Environmental Reporting Program.