Could cloud quantum computing be possible? Google wants to make it happen, although there are some who doubt it will happen anytime soon.
Potential benefits of quantum computing
Quantum computing could be a large step above the computers we typically use today. They are made up of quantum bits, or qubits, that not only process information as ones or zeros, but also any state in between.
It uses this mechanism to solve problems and attempt to work more quickly than previously possible. Unfortunately, technology hasn’t given us a fully functional, available quantum computer yet.
However, its future potential means that, according to Jerry Chow, a member of IBM’s experimental quantum computing department, “at 50 qubits, universal quantum computing would reach that inflection point and be able to solve problems existing computers can’t handle.” This future might be closer than we think. IBM has plans to construct and distribute this 50-qubit system within the next few years, while Google is projecting that they’ll complete a 49-qubit system by the end of this year.
One of the real-life uses of quantum computing is for pharmaceutical science. Right now, there’s a struggle when it comes to understanding how each structure bonds together. It takes complex computer simulations to understand the atomic and subatomic motion when creating new drugs. Solving this could result in lower-cost and better drugs.
Scott Crowder from IBM explained that “you don’t even ask those questions on a classical computer because you know you’re going to get it wrong.” Once quantum computing hits its prime, though, medicine can potentially be made much more quickly and at much lower prices.
Another problem solved with quantum computing is one that you wouldn’t expect: fertilizer production, according to computing sciences fellow at Lawrence Berkeley National Laboratory, Jarrod McClean.
Just making “mass-produced fertilizer accounts for one percent to two percent of the world’s energy use per year.” However, there is a much more energy-efficient option.
The problem, according to McClean, is that “it’s been too challenging for classical systems to date” to help researchers create this energy-efficient option in the lab, but he has high hopes for quantum computers’ abilities in the near future to accomplish it.
And, the application doesn’t have to be revolutionary to be helpful. In fact, these computers could help organize delivery routes, especially during particularly busy times like Christmas, by organizing thousands of self-driving cars (assuming they will be commonly used in the future). Quantum computers could also help translation software or other small, but productive uses.
The potential of quantum computing is almost endless, from finance to economy to energy. It is beginning to become available to certain people right now and is expected to become more mainstream soon (although there are debates about when, exactly). But can Google bring it to the cloud?
What is Google doing
While Google has been working on quantum computing for years, they’ve just recently started looking at how to turn it into a business.
In fact, Google has already started offering “science labs and artificial intelligence researchers early access to its quantum machines over the Internet in recent months.” Their motivation, according to Bloomberg, for giving this early access is so the research will build more tools to go with this technology, helping to make their cloud quantum computing service as fast and powerful as possible.
Google is also considering a ProjectQ, or “an open-source effort to get developers to write code for quantum computers.” According to a quantum computing researcher at Stanford University, Google is not trying to hide that “they’re building quantum hardware and they would, at some point in the future, make it a cloud service.”
Additionally, according to scientist Jonathan DuBois at Lawrence Livermore National Laboratory, Google “pledged that government and academic researchers would get free access.”
While there’s still quite a bit of debate about when quantum computers will actually be usable, Google’s efforts could skyrocket them to the top of the ongoing cloud wars. If what many companies are predicting, processing tasks could become millions of times faster.
Offering cloud quantum computing is an extremely smart business decision, considering that these computers are very large and hard to contain so very few companies could have them themselves.
As of right now, Google rents storage by the minute, so if their quantum computers can cut the compute time by such a large percentage, they would have a huge price advantage over the competition. Google’s cloud compute prices are currently higher than Amazon’s and Microsoft’s for most instances.
Unfortunately, though, we may be getting ahead of ourselves. Seth Lloyd, a professor at the Massachusetts Institute of Technology, argued that useful applications won’t arrive until a system has at least 100 qubits, although, other researchers and organizations seem to disagree.
While Google announced their quantum computing efforts back in 2014, they claimed that they would prove their “supremacy” by performing equal to or better than supercomputers by the end of 2017.
Of course, though, Google isn’t the only one going after quantum computers. IBM already offers access to their specialized quantum computing platform and plans to create a 50-qubit quantum system within the next five years. This past May, they added a 17 qubit prototype quantum processor to their service as well, although it’s still in its experimental phase.
The future of cloud quantum computing
Chad Rigetti, founder of Rigetti Computing, which has netted over $69 million from investors for quantum computing software and equipment, believes that quantum computing will become as popular as AI is now, although he isn’t sure exactly when.
“This industry is very much in its infancy,” Rigetti said. “No one has built a quantum computer that works.”
Hopefully, the future of cloud quantum computing will be here sooner rather than later. Scientists believe that its applications are almost endless, from “improving the work of solar panels, creating medicines, and even fertilizers.”
With the numerous applications, faster speeds, and potentially lower prices, cloud quantum computing could revolutionize technology.
Photo credit: Pixabay


We live in a time where the phrase “artificial intelligence” (called AI for short) is trendy and appears in the marketing descriptions of many products and services. But what is precisely AI?
Broadly speaking, AI originated as an idea to create artificial “thinking” along the lines of the human brain.
As of today, however, we can only make assumptions about how the human brain works, primarily based on medical research and observation. From a medical point of view, we know that the brain looks like a complex network of connections in which neurons are the main element and that our thoughts, memory, and creativity are a flow of electrical impulses. This knowledge has given hope to construct an analogous brain in an electronic version, either hardware or software, where neurons are replaced by electronics or software. However, since we are not 100% sure exactly how the brain works, all current models in AI are certain mathematical approximations and simplifications, serving only certain specific uses. Nevertheless, we know from observation that it is possible, for example, to create solutions that mimic the mind quite well – they can recognize the writing, images (objects), music, emotions, and even create art based on previously acquired experiences. However, the results of the latter are sometimes controversial.
What else does AI resemble the human brain in?
Well… it has to learn! AI solutions are based on one fundamental difference from classical algorithms: the initial product is a philosophical “tabula rasa”, or “pure mind”, which must first be taught.
In the case of complex living organisms, knowledge emerges with development: the ability to speak, to move independently, to name objects, and in the case of humans and some animal species, there are elements of learning organized in kindergartens, schools, universities, and during work and independent development. Analogously in most artificial intelligence solutions – the AI model must first receive specific knowledge, most often in the form of examples, to be able to later function effectively as an “adult” algorithm. Some of the solutions learn once, while others improve their knowledge while functioning (Online Learning, or Reinforced Learning). It vividly resembles the human community: some people finish their education and work for the rest of their lives in one company doing one task. Others have to train throughout their lives as their work environment changes dynamically.
Is AI already “smarter” than humans?
As an interesting aside, we can compare the “computing power” of the brain versus the computing power of computers. It, of course, will be a simplification because the nature of the two is quite different.
First, how many neurons does the average human brain have? It was initially estimated to be around 100 billion neurons. However, according to recent research (https://www.verywellmind.com/how-many-neurons-are-in-the-brain-2794889), the number of neurons in the “average” human brain is “slightly” less, by about 14 billion, or 86 billion neuronal cells. For comparison, the brain of a fruit fly is about 100 thousand neurons, a mouse 75 million neurons, a cat 250 million, a chimpanzee 7 billion. An interesting fact is an elephant’s brain (much larger than a human in terms of size), which has … 257 billion neurons, which is definitely more than the brain of a human.
From medical research, we know that for each neuron, there are about 1000 connections with neighboring neurons or so-called synapses, so in the case of humans, the total number of connections is around 86 trillion (86 billion neurons * 1000 connections). Therefore, in simplified terms, we can assume that each synapse performs one “operation”, analogous to one instruction in the processor.
At what speed does the brain work? In total … not much. We can determine it based on BCI type interfaces (Brain-Computer Interface), which not so long ago appeared as a result of the development of medical devices for electroencephalography (EEG), such as armbands produced by Emotiv, thanks to which we can control the computer using brain waves. Of course, they do not integrate directly with the cerebral cortex but measure activity by analyzing electrical signals. Based on this, we can say that the brain works at variable speed (analogous to the Turbo mode in the processor), and it is between 0.5Hz for the so-called delta state (complete rest) and about 100Hz for the gamma state (stress, full tension).
Thus, we can estimate the maximum computational power of the brain as 8.6 billion operations (8.6*10^15) or 8.6 Petaflops! Despite the relatively slow performance of the brain, this is a colossal number thanks to the parallelization of operations. From Wikipedia (https://en.wikipedia.org/wiki/Supercomputer), we learn that supercomputers did not break this limit until the first decade of the 21st century. The situation will change with the advent of quantum computers, which inherently work in parallel, just like the human brain. However, as of today, quantum computing technology for cyber threat hunting is still in its infancy.
In conclusion, at the moment, AI has not yet overtaken the human brain, but it probably will someday. However, we are only talking about learning speed here, leaving aside the whole issue of creativity, “coming up with” ideas, emotions, etc.
AI and mobile devices
Artificial intelligence applications require substantial computational power, especially at the so-called learning stage, and pose a significant challenge in integrating them with AR and VR solutions. Unfortunately, AR and VR devices mostly have very limited resources, as they are effectively ARM processor-based mobile platforms comparable in performance to smartphones. As a result, most artificial intelligence models are so computationally (mathematically) complex that they cannot be trained directly on mobile devices. OK – you can, but it will take an incredibly and unacceptably long time. So in most cases, to learn models, we use powerful PCs (clusters) and GPU gas pedals, mainly Nvidia CUDA. This knowledge is then “exported” into a simplified model “implanted” into AR and VR software or mobile hardware.
In our next blog post, you’ll learn how we integrated AI into VR and AR, how we dealt with the limited performance of mobile devices, and what we use AI for in AR and VR.


Many protocols like SSH, OpenPGP, S/MIME, and SSL/TLS rely on RSA encryption where access to data is secured with two keys. The encryption key is public and differs from the decryption key which is kept secret. The cryptosystem’s reliability exploits the fact that factoring large primes takes years to do even for today’s fastest supercomputers, so protocols based on RSA have proven paramount to anything from processing payments to storing classified intelligence. RSA, however, might become obsolete soon as quantum computer system become stabler and more efficient. Using only five atoms, a team of international researchers showed how to factor a prime, albeit a trivial one for demo purposes. The researchers say there aren’t any physical restrictions that might hinder scalability. Theoretically, more atoms could be added in the process and large primes could be solved at lightning speed. That doesn’t make the engineering challenges easy, though.
RSA was first described in 1977 by Ron Rivest, Adi Shamir and Leonard Adleman of the Massachusetts Institute of Technology. In this asymmetric cryptography, two different but mathematically linked keys, one public and one private, are used to decrypt a message. The public key, which anyone can see and use to encrypt a message, is based on the product of two large primes, and an auxiliary exponential. Multiplying two large primes to an integer is easy, but determining the original primes that make the product with no other info is very difficult.
In 1994, Peter Shor, the Morss Professor of Applied Mathematics at MIT, came up with a quantum algorithm that calculates the prime factors of a large number, vastly more efficiently than a classical computer. To actually run the algorithm though a quantum computer would require many qubits or quantum bits.
In conventional computers, operations that transform inputs into outputs work with bits which can be 0s or 1s. Qubits are atomic-scale units that can be 0 and 1 at the same time — a state known as a superposition. What this means is that a quantum computer can essentially carry out two calculations in parallel. A system that works with qubits can be not twice but millions of times faster than a conventional computer.
Previously, scientists designed quantum computers that could factor the number 15 (primes 3 and 5), but these couldn’t be scaled to factor larger numbers. “The difficulty is to implement [the algorithm] in a system that’s sufficiently isolated that it can stay quantum mechanical for long enough that you can actually have a chance to do the whole algorithm,” Isaac Chuang, professor of physics and professor of electrical engineering and computer science at MIT.
Chuang and colleagues at MIT and the University of Innsbruck in Austria claim they not only found a way to make a quantum system scalable, but also more efficient. Typically, it took 12 qubits to factor the number 15. The researchers factored the same number using only five qubits or atoms. These five atoms are held in an ion trap, which removes an electron from each atom thereby charging it. The system is stabilized by holding the atoms in place with a magnetic field.
Logic gates operations are performed using laser pulses on four of the atoms, while the fifth is used to store or extract results. Using the fifth atom to store information was the brilliant part. “Measuring a qubit knocks it out of superposition and thereby destroys the information it holds. Restricting the measurement step to the fifth ion kept the four involved in the computation from being corrupted,” wrote Amy Nordrum in an article for IEEE.
The number 15, albeit trivial to solve, is the smallest that can meaningfully demonstrate Shor’s algorithm. A working system developed at University of Innsbruck factored the number with a confidence exceeding 99 percent, as reported in the journal Science.
“In future generations, we foresee it being straightforwardly scalable, once the apparatus can trap more atoms and more laser beams can control the pulses,” Chuang says. “We see no physical reason why that is not going to be in the cards.”
To decrypt a typical 1024-bit key, the same system would need thousands of qubits or simultaneous laser pulses. This is doable, but highly challenging and it might take a long time before you can use a quantum computer to break RSA.
Moreover, many researchers are already aware of the limitations of current cryptosystem and are preparing for the future: “quantum-resistant public-key algorithms”.
“Continued advances in quantum computing will draw broad attention to the threat it represents to all of today’s widely used public-key cryptosystems – the cryptography that underlies electronic commerce and secure communications on the Internet. The security community will begin planning the migration to new `quantum-resistant’ public-key cryptosystems for which quantum computers provide no computational advantage,” said Brian LaMacchia, Director, Security & Cryptography, Microsoft Research.


The original University of Chicago “uchicago news” article by Louise Lerner can be read here.
Researchers used the U.S. Department of Energy’s Advanced Photon Source (APS) to help them invent an innovative way for different types of quantum technology to “talk” to each other using sound. The study, published in Nature Physics, is an important step in bringing quantum technology closer to reality.
Scientists are eyeing quantum systems, which tap the quirky behavior of the smallest particles, as the key to a fundamentally new generation of atomic-scale electronics for computation and communication. But a persistent challenge has been transferring information between different types of technology, such as quantum memories and quantum processors.
“We approached this question by asking: Can we manipulate and connect quantum states of matter with sound waves?” said senior study author David Awschalom, the Liew Family Professor with the Institute for Molecular Engineering and senior scientist at Argonne National Laboratory.
One way to run a quantum computing operation is to use “spins”—a property of an electron that can be up, down or both. Scientists can use these like zeroes and ones in today’s binary computer programming language. But getting this information elsewhere requires a translator, and scientists thought sound waves could help.
“The object is to couple the sound waves with the spins of electrons in the material,” said graduate student Samuel Whiteley, the co-first author on the Nature Physics paper. “But the first challenge is to get the spins to pay attention.” So they built a system with curved electrodes to concentrate the sound waves, like using a magnifying lens to focus a point of light.
The results were promising, but the researchers from The University of Chicago, and Argonne National Laboratory needed more data. To get a better look at what was happening, they worked with scientists at the Center for Nanoscale Materials (CNM) at Argonne to observe the system in real time. Essentially, they used extremely bright, powerful x-rays from the CNM/X-ray Science Division 26-ID-C x-ray beamline at the Advanced Photon Source as a microscope to peer at the atoms inside the material as the sound waves moved through it at nearly 7,000 kilometers per second. (Both the CNM and the APS are Office of Science user facilities at Argonne.)
“This new method allows us to observe the atomic dynamics and structure in quantum materials at extremely small length scales,” said Awschalom. “This is one of only a few locations worldwide with the instrumentation to directly watch atoms move in a lattice as sound waves passes through them.”
One of the many surprising results, the researchers said, was that the quantum effects of sound waves were more complicated than they’d first imagined. To build a comprehensive theory behind what they were observing at the subatomic level, they turned to Prof. Giulia Galli, the Liew Family Professor at the IME and a senior scientist at Argonne. Modeling the system involves marshalling the interactions of every single particle in the system, which grows exponentially, Awschalom said, “but Professor Galli is a world expert in taking this kind of challenging problem and interpreting the underlying physics, which allowed us to further improve the system.”
It’s normally difficult to send quantum information for more than a few microns, said Whiteley—that’s the width of a single strand of spider silk. This technique could extend control across an entire chip or wafer.
“The results gave us new ways to control our systems, and opens venues of research and technological applications such as quantum sensing,” said postdoctoral researcher Gary Wolfowicz, the other co-first author of the study.
The discovery is another from the University of Chicago’s world-leading program in quantum information science and engineering; Awschalom is currently leading a project to build a quantum “teleportation” network between Argonne and Fermi National Accelerator Laboratory to test principles for a potentially un-hackable communications system.
The scientists pointed to the confluence of expertise, resources and facilities at the University of Chicago, Institute for Molecular Engineering and Argonne as key to fully exploring the technology.
“No one group has the ability to explore these complex quantum systems and solve this class of problems; it takes state-of-the-art facilities, theorists and experimentalists working in close collaboration,” Awschalom said. “The strong connection between Argonne and the University of Chicago enables our students to address some of the most challenging questions in this rapidly moving area of science and technology.”
See: Samuel J. Whiteley1, Gary Wolfowicz1,2, Christopher P. Anderson1, Alexandre Bourassa1, He Ma1, Meng Ye1, Gerwin Koolstra1, Kevin J. Satzinger1,3, Martin V. Holt4, F. Joseph Heremans1,4, Andrew N. Cleland1,4, David I. Schuster1, Giulia Galli1,4, and David D. Awschalom1,4*, “Spin–phonon interactions in silicon carbide addressed by Gaussian acoustics,” Nat. Phys., published on line 11 February 2019. DOI: 10.1038/s41567-019-0420-0
Author affiliations: 1The University of Chicago, 2Tohoku University, 3University of California, Santa Barbara, 4Argonne National Laboratory
The devices and experiments were supported by the Air Force Office of Scientific Research; material for this work was supported by the U.S. Department of Energy (DOE). Use of the Center for Nanoscale Materials, an Office of Science user facility, was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. S.J.W. and K.J.S. were supported by the NSF GRFP, C.P.A. was supported by the Department of Defense through the NDSEG Program, and M.V.H., F.J.H., A.N.C., G.G. and D.D.A. were supported by the U.S. DOE Office of Science-Basic Energy Sciences. This work made use of the UChicago MRSEC (NSF DMR-1420709) and Pritzker Nanofabrication Facility, which receives support from the SHyNE, a node of the NSF’s National Nanotechnology Coordinated Infrastructure (NSF ECCS-1542205). This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.
Argonne National Laboratory seeks solutions to pressing national problems in science and technology. The nation's first national laboratory, Argonne conducts leading-edge basic and applied scientific research in virtually every scientific discipline. Argonne researchers work closely with researchers from hundreds of companies, universities, and federal, state and municipal agencies to help them solve their specific problems, advance America's scientific leadership and prepare the nation for a better future. With employees from more than 60 nations, Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy's Office of Science.
The U.S. Department of Energy's Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit the Office of Science website.


The 2022 Nobel Prize for Physics was awarded to experimental physicists Alain Aspect, John Clauser, and Anton Zeilinger. The three pioneers conducted groundbreaking research using entangled quantum particles — subatomic particles that behave as if they are linked even when there is nothing between them — a process that Albert Einstein famously called “spooky action at a distance”.
“Quantum information science is a vibrant and rapidly developing field,” said Eva Olsson, a member of the Nobel Committee for Physics. “It has broad potential implications in areas such as secure information transfer, quantum computing, and sensing technology.”
But it wasn’t always like this. In fact, quantum physics itself was a fiercely debated field. In the 1930s, one of the fiercest clashes in physics history erupted between Albert Einstein on one hand and Niels Bohr and Erwin Schrödinger on the other (all three Nobel laureates). Einstein believed that everything had to be concrete and knowledgeable at a fundamental level, whereas the pioneers of quantum mechanics argued that reality can be uncertain and particles don’t possess certain properties until they are measured.
John Clauser initially thought Einstein was right, and in the 1970s, he set out clever experiments to settle the debate. But it didn’t go as planned: in fact, his experiments disproved Einstein and laid the groundwork for a deeper understanding of quantum mechanics — and in particular, quantum entanglement.
Quantum entanglement really is a bizarre process. It’s essentially a phenomenon that can occur when some particles (most commonly photons) are linked together in a way that persists no matter how far apart they are in space and they have a state that cannot be described independently of each other. For instance, physical properties such as position, momentum, spin, and polarization can be perfectly correlated between entangled particles even when they are miles away from each other. Basically, you can study one of the entangled particles and gain information about the linked particles as well — a phenomenon that has no equivalent in classical mechanics.
“I would not call entanglement ‘one,’ but rather ‘the’ trait of quantum mechanics,” Thors Hans Hansson, a member of the Nobel Committee, quoted Schrödinger as writing in 1935. “The experiments performed by Clauser and Aspect opened the eyes of the physics community to the depth of Schrödinger’s statement, and provided tools for creating and manipulating and measuring states of particles that are entangled although they are far away.”
Einstein (and many other physicists) suspected that if the particles are linked, then there must be some ‘hidden variables’ to connect them, or something that would tie them together. Instead, experimental research from the three laureates showed that there is a genuine entanglement that is not owed to other factors.
Ironically, Clauser, who runs his own company in California now, recalls how his advisor thought this field of research was a “waste of time” and advised him to focus on something else and warned him against “ruining” his career. Well, as it turns out, the very opposite happened.
The trio’s experiments were also previously awarded the Wolf Prize, sometimes considered a precursor to the Nobel Prize. In fact, the three have been considered “favorites” for a Nobel Prize for a decade.
However, Zeilinger, who is currently a professor of physics at the University of Vienna, was very eager to point out that the three did not work alone, and dedicated the prize to the young people who helped in doing the work.
“This prize is an encouragement to young people,” said Zeilinger. “It would not be possible without more than 100 young people who worked with me over the years.”
Zeilinger also gave some advice to young researchers, echoing the thoughts of Dennis Sullivan, the 2022 Abel Prize laureate (in mathematics): “Do what you find interesting, and don’t care too much about possible applications.”
But it should also be said that the trio’s Nobel Prize also considered the applications of their experiments.
While the field of quantum mechanics seems ethereal and removed from everyday life, researchers are increasingly finding applications for this technology.
For starters, the quantum computers that have so much promise to solve complex problems are based on quantum processes studied by the three physicists. Another application is quantum communications, a technology with security that promises to be nigh-unbreakable.
For instance, a research group from China managed to beam up entangled pairs of photons to a satellite, proving that entanglement can survive trips of over 1,000 kilometers — that group was spearheaded by one of Zeilinger’s former students. This type of quantum voyage paves the way for securing messages with a “quantum key” that gets destroyed any time someone attempts to eavesdrop and intercept the information. Basically, this could mean essentially unbreakable cryptography.
However, while the field is growing rapidly and it has a lot of potential, there is much we still don’t know about entanglement. In theory, everything could be entangled, but practically, the process seems chaotic and random, and the largest experiments have entangled around a dozen photons. Another project has entangled around a thousand atoms with a single photon.
In 2021, the Nobel Prize for Physics was awarded to three researchers who study complex systems that are particularly important for climate science.
Earlier this week, the Nobel committee awarded the Physiology or Medicine prize to Svante Pääbo for his many contributions “concerning the genomes of extinct hominins and human evolution.” All Nobel Prizes come with a cash reward worth 10 million Swedish krona ($920,000); if there are multiple laureates, the reward is shared.
Andrei's background is in geophysics, and he's been fascinated by it ever since he was a child. Feeling that there is a gap between scientists and the general audience, he started ZME Science -- and the results are what you see today.


The laws of physics, among the greatest discoveries of humankind, have emerged over many centuries in a process often influenced by the prominent thinkers of the time. This process has had a profound influence on the evolution of science and gives the impression that some laws could not have been discovered without the knowledge of earlier ages.
Quantum mechanics, for example, is built on classical mechanics using various mathematical ideas that were prominent at the time.
But perhaps there is another way of discovering the laws of physics that does not depend on the understanding we have already gained about the universe.
Today Raban Iten, Tony Metger, and colleagues at ETH Zurich in Switzerland say they have developed just such a method and used it to discover laws of physics in an entirely novel way. And they say it may be possible to use this method to find wholly new formulations of physical laws.
First, some background. The laws of physics are simple representations that can be interrogated to provide information about more complex scenarios. Imagine setting a pendulum in motion and asking where the base of the pendulum will be at some point in the future. One way to answer this is by measuring the position of the pendulum as it swings. This data can then be used as a kind of look-up table to find the answer. But the laws of motion provide a much easier way of discovering the answer: simply plug values for the various variables into the appropriate equation. That gives the correct answer too. That’s why the equation can be thought of as a compressed representation of reality.
This immediately suggests how neural networks might find these laws. Given some observations from an experiment—a swinging pendulum, for example—the goal is to find some simpler representation of this data.
The idea from Iten, Metger, and co is to feed this data into the machine so it learns how to make an accurate prediction of the position. Once the machine has learned this, it can then predict the position from any initial set of conditions. In other words, it has learned the relevant law of physics.
To find out whether this works, the researchers feed data from a swinging-pendulum experiment into a neural network they call SciNet. They go on to repeat this for experiments that include the collision of two balls, the results of a quantum measurement on a qubit, and even the positions of the planets and sun in the night sky.
The results make for interesting reading. Using the pendulum data, SciNet is able to predict the future frequency of the pendulum with an error of less than 2 percent.
What’s more, Iten, Metger, and co are able to interrogate SciNet to see how it arrives at the answer. This doesn’t reveal the precise equation, unfortunately, but it does show that the network uses only two variables to come up with the solution. That’s exactly the same number as in the relevant laws of motion.
But that isn’t all. SciNet also provides accurate predictions of the angular momentum of two balls after they have collided. That’s only possible using the conservation of momentum, a version of which SciNet appears to have discovered. It also predicts the measurement probabilities when a qubit is interrogated, clearly using some representation of the quantum world.
Perhaps most impressive is that the network learns to predict the future position of Mars and the sun using the initial position as seen from Earth. That’s only possible using a heliocentric model of the solar system, an idea that humans took centuries to hit on.
And indeed, an interrogation of SciNet suggest it is has learned just such a heliocentric representation. “SciNet stores the angles of the Earth and Mars as seen from the Sun in the two latent neurons—that is, it recovers the heliocentric model of the solar system,” say the researchers.
That’s impressive work, but it needs to be placed in perspective. This may be the first demonstration that an artificial neural network can compress data in a way that reveals aspects of the laws of physics. But it is not the first time that a computational approach has derived these laws.
A few years ago, computer scientists at Cornell University used a genetic algorithm that exploits the process of evolution to derive a number of laws of physics from experimental data. These included conservation laws for energy and momentum. The system even spat out the equation itself, not just a hint about how it was calculating, as SciNet does.
Clearly, evolutionary algorithms have the upper hand in the process of discovering the laws of physics using raw experimental data. (Given that evolution is the process that produced biological neural networks in the first place, it is arguable that it will forever be the more powerful approach.)
There is an interesting corollary to all this. It has taken humanity centuries to discover the laws of physics, often in ways that have depended crucially on previously discovered laws. For example, quantum mechanics is based in classical mechanics. Could there be better laws that can be derived from experimental data without any prior knowledge of physics?
If so, this machine-learning approach or the one based on evolution should be exactly what’s need to find them.
Ref: arxiv.org/abs/1807.10300 : Discovering physical concepts with neural networks
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Exploring the magnetism of a single atom
An EPFL-led research collaboration has shown for the first time the maximum theoretical limit of energy needed to control the magnetization of a single atom. The fundamental work can have great implications for the future of magnetic research and technology.
Magnetic devices like hard drives, magnetic random access memories (MRAMs), molecular magnets, and quantum computers depend on the manipulation of magnetic properties. In an atom, magnetism arises from the spin and orbital momentum of its electrons. ‘Magnetic anisotropy’ describes how an atom’s magnetic properties depend on the orientation of the electrons’ orbits relative to the structure of a material. It also provides directionality and stability to magnetization. Publishing in Science, researchers led by EPFL combine various experimental and computational methods to measure for the first time the energy needed to change the magnetic anisotropy of a single Cobalt atom. Their methodology and findings can impact a range of fields from fundamental studies of single atom and single molecule magnetism to the design of spintronic device architectures.
Magnetism is used widely in technologies from hard drives to magnetic resonance, and even in quantum computer designs. In theory, every atom or molecule has the potential to be magnetic, since this depends on the movement of its electrons. Electrons move in two ways: Spin, which can loosely be thought as spinning around themselves, and orbit, which refers to an electron’s movement around the nucleus of its atom. The spin and orbital motion gives rise to the magnetization, similar to an electric current circulating in a coil and producing a magnetic field. The spinning direction of the electrons therefore defines the direction of the magnetization in a material.
The magnetic properties of a material have a certain ‘preference’ or ‘stubbornness’ towards a specific direction. This phenomenon is referred to as ‘magnetic anisotropy’, and is described as the “directional dependence” of a material’s magnetism. Changing this ‘preference’ requires a certain amount of energy. The total energy corresponding to a material’s magnetic anisotropy is a fundamental constraint to the downscaling of magnetic devices like MRAMs, computer hard drives and even quantum computers, which use different electron spin states as distinct information units, or ‘qubits’.
The team of Harald Brune at EPFL, working with scientists at the ETH Zurich, Paul Scherrer Institute, and IBM Almaden Research Center, have developed a method to determine the maximum possible magnetic anisotropy for a single Cobalt atom. Cobalt, which is classed as a ‘transition metal’, is widely used in the fabrication of permanent magnets as well as in magnetic recording materials for data storage applications.
The researchers used a technique called inelastic electron tunneling spectroscopy to probe the quantum spin states of a single cobalt atom bound to a magnesium oxide (MgO) layer. The technique uses an atom-sized scanning tip that allows the passage (or ‘tunneling’) of electrons to the bound cobalt atom. When electrons tunneled through, they transferred energy the cobalt atom, inducing changes in its spin properties.
The experiments showed the maximum magnetic anisotropy energy of a single atom (~60 millielectron volts) and the longest spin lifetime for a single transition metal atom. This large anisotropy leads to a remarkable magnetic moment, which has been determined with synchrotron-based measurements at the X-Treme beamline at the Swiss Light Source. Though fundamental, these findings open the way for a better understanding of magnetic anisotropy and present a single-atom model system that can be conceivably used as a future ‘qubit’.
“Quantum computing uses quantum states of matter, and magnetic properties are such a quantum state”, says Harald Brune. “They have a life-time, and you can use the individual suface-adsorbed atoms to make qubits. Our system is a model for such a state. It allows us to optimize the quantum properties, and it is easier than previous ones, because we know exactly where the cobalt atom is in relation to the MgO layer.”
This work represents a collaboration between EPFL’s Laboratory of Nanostructures at Surfaces (LNS), IBM’s Almaden Research Center, ETH Zurich’s Department of Materials, Paul Scherrer Institute’s Swiss Light Source, and Georgetown University’s Department of Physics.
Rau IG, Baumann S, Rusponi S, Donati F, Stepanow S, Gragnaniello L, Dreiser J, Piamonteze C, Nolting F, Gangopadhyay S, Albertini OR, Macfarlane RM, Lutz CP, Jones B, Gambardella P, Heinrich AJ, Harald Brune. Reaching the magnetic anisotropy limit of a 3d metal atom. Science 08 March 2014 DOI:10.1126/science.1252841


Radhika Iyer – 2022 Teddy Rocks Maths Essay Competition Commended Entry
Data transmission is often noisy. Information can get easily garbled, and imperfect information frequently has a cost associated with it. Coding theory is a field of mathematics that deals with trying to make transmission more reliable by using error correcting codes, which are methods of detecting and correcting errors.
Throughout this delve into error correcting codes, we will be considering the transmission of strings of binary (or base 2), as every letter, symbol or pixel from an image can be represented as a string of 0s and 1s. Additionally, when a message of length k is sent, there are 2k possible messages being sent, as there are 2 options for each bit. Error correcting codes send more than k binary digits, with these extra digits, called parity digits, helping to detect and correct codes.
One example of error correction codes is repetition codes, where we send each message multiple times. For example, if we sent 0011 twice, as 00110011, then the second block of four bits could be compared by the receiver against the first block. A recurrent term in coding theory is information rate (R), with it recording how much information is carried on average for every bit that is sent. For repetition codes, the information rate is often really low, here 0.5 and this can be lower if blocks are sent even more times. Therefore, in practice, we would prefer to use other error correction codes.
The weight of a binary sequence is the number of bits in the message that are equal to 1. Parity check codes work by adding a parity check bit at the end that will make a message have even (or odd) weight. For example, consider a scenario where we want an even weight, and the message we are trying to send is 0011. There are 2 (which is an even count) 1s right now, so we add a 0 at the end, so that we have even weight. If we were trying to send 1110, then we would add a 1. This is the equivalent of making the parity digit equal to the sum of all the digits modulo 2. Then, when a message received, if this parity bit does not represent the weight of the message being sent, then a bit might have flipped in transmission. Although we can’t be sure where an error may have occurred, or if an even number of changes occurred, the information rate for this code is 4/5 = 0.8.
If we consider trying to find a single-error-detecting code for a message of length 4 bits, as we need to always add a parity bit, an overall message of length 5 is the shortest possible length, so 0.8 is the highest possible information rate for a message of length 4. In general, parity check codes for messages of length n have information rates of R = n/(1 + n).
So far, what has been described has been focused on trying to get good error detection, but we still need to correct these errors and try and find the original message. I will be referring to codewords, which are the overall transmissions received that combine our original message and the added bits, that help transmit messages with less likelihood of error. Another important definition is that of Hamming distances, which are the total number of different bits when comparing two codewords. For example, 1010 and 1001 have a Hamming distance of 2, as the last two bits are different.
Let’s take a scenario where we are trying to send either a True or a False, with 1 for True and 0 for False. If an error occurred when sending this message, then we would never be able to know if the original message was True or False. Now, let’s say that 11 is True and 00 is False. We still would not be able to know what the original message was for 01, as this has a Hamming distance of 1 from both 00 and 11.
Therefore, let’s say that 111 is True and 000 is False. If we received either 101,110, 011, then we could assume that an error had occurred and what was originally sent was True, as there is only one change. Therefore, if we received 001, 010, or 100, then the original message was False. This is known as majority logic. This code is called the (3,1) repetition code, as three bits are sent, with the message of length 1 (0 or 1) being identified.
There is a link with geometry here – this is where sphere packing comes in. Sphere packing concerns the arranging of non-overlapping spheres within a space, where we are trying to maximise the total volume of all the spheres that fit within this space. If we consider (0,0,0) and (1,1,1) as points in a 3D space, then all our other possible messages, where only one error has occurred, can also be visualised as vertices of a cube. Two spheres of radius 1 can then be centred at (0,0,0) and (1,1,1). The vertices contained within the sphere can then be interpreted as codewords for the point at the centre of the sphere.
In order to fit more original messages being sent, we would like more of these spheres to be packed around points, so that more messages can be transmitted. Sphere packing, where all spheres are disjoint, can be used to find error correcting codes that can always detect the position of errors. Perfect Hamming codes are a well-known class of codes that can correct single bit errors, and these occur when all vertices, in however many dimensions of Euclidean space, can be contained within spheres that have the smallest radius we can make possible. This means that they have attained the Hamming bound, or the sphere-packing bound. Another way of writing this is to say that a perfect code occurs when all vertices are either codewords themselves, or are only one edge (or a Hamming distance of 1) away from a codeword.
The most well-known Hamming code is the (7,4) code which uses a ‘generator matrix’ to create three parity bits added to our four bits that make up the message, and is a code that can detect and correct single errors. It is thought to have a relatively high information rate, as the rate is higher than 0.333 for the (3,1) code; the R for the (7,4) code is 4/7 = 0.571 (3 significant figures).
What is interesting to note is that (7,4) is the first perfect Hamming code after (3,1). After this, the next perfect code is (15,11). A pattern you may have spotted is that the first number in all of these brackets is one less than a power of two. In order to explain this, let us consider the (7,4) scenario. If we consider code words that are on a 7-dimensional hypercube, every codeword would have 7 edges exiting this point, and so, including itself, there are 8 vertices involved for every message. Now, as we are using messages of length 4, there are 16 possible messages. 16 x 8 is 128 which is 27. On a hypercube in n-dimensions, the total number of vertices is always 2n. This makes having an overall message length of 2n-1 a perfect scenario, as every single vertex is involved with a codeword (2n for each possible message), so there is a most effective use of spheres or space.
Perfect Hamming codes are a method of efficiently correcting single bit errors, but it is important to note that these processes are not going to be able to correct all of the bits that may be corrupted in a data transmission error. There are also many other famous codes that I have not delved into, including Reed-Muller codes, the famous Golay (23,12) code that can correct up to three errors, and the Leech lattice in 24-dimensional space. There are also many links between error correcting and probability that have not been mentioned.
With an increasing focus on quantum computing and how powerful this field can be, especially when we think about their impact on cryptography, it is interesting to think about qubits, which also transmit information. Qubits can be in any superposition of the states 0 and 1, and will also have errors, but recent research has shown that space-time could be involved in building error correcting code for qubits and quantum computers. Perhaps this will be the area that coding theory focuses on next.
Thompson, Thomas M. (2014) – From Error-Correcting Codes Through Sphere Packings to Simple (Chapter 1) doi: 10.5948/UPO9781614440215.002


Topological insulators are one of the most puzzling quantum materials – a class of materials whose electrons cooperate in surprising ways to produce unexpected properties. The edges of a TI are electron superhighways where electrons flow with no loss, ignoring any impurities or other obstacles in their path, while the bulk of the material blocks electron flow.
Scientists have studied these puzzling materials since their discovery just over a decade ago with an eye to harnessing them for things like quantum computing and information processing.
Now researchers at the Department of Energy’s SLAC National Accelerator Laboratory and Stanford University have invented a new, hands-off way to probe the fastest and most ephemeral phenomena within a TI and clearly distinguish what its electrons are doing on the superhighway edges from what they’re doing everywhere else.
The technique takes advantage of a phenomenon called high harmonic generation, or HHG, which shifts laser light to higher energies and higher frequencies – much like pressing a guitar string produces a higher note – by shining it through a material. By varying the polarization of laser light going into a TI and analyzing the shifted light coming out, researchers got strong and separate signals that told them what was happening in each of the material’s two contrasting domains.
“What we found out is that the light coming out gives us information about the properties of the superhighway surfaces,” said Shambhu Ghimire, a principal investigator with the Stanford PULSE Institute at SLAC, where the work was carried out. “This signal is quite remarkable, and its dependence on the polarization of the laser light is dramatically different from what we see in conventional materials. We think we have a potentially novel approach for initiating and probing quantum behaviors that are supposed to be present in a broad range of quantum materials.”
The research team reported the results today in Physical Review A.
Light in, light out
Starting in 2010, a series of experiments led by Ghimire and PULSE Director David Reis showed HHG can be produced in ways that were previously thought unlikely or even impossible: by beaming laser light into a crystal, a frozen argon gas or an atomically thin semiconductor material. Another study described how to use HHG to generate attosecond laser pulses, which can be used to observe and control the movements of electrons, by shining a laser through ordinary glass.
In 2018, Denitsa Baykusheva, a Swiss National Science Foundation Fellow with a background in HHG research, joined the PULSE group as a postdoctoral researcher. Her goal was to study the potential for generating HHG in topological insulators – the first such study in a quantum material. “We wanted to see what happens to the intense laser pulse used to generate HHG,” she said. “No one had actually focused such a strong laser light on these materials before.”
But midway through those experiments, the COVID-19 pandemic hit and the lab shut down in March 2020 for all but essential research. So the team had to think of other ways to make progress, Baykusheva said.
“In a new area of research like this one, theory and experiment have to go hand in hand,” she explained. “Theory is essential for explaining experimental results and also predicting the most promising avenues for future experiments. So we all turned ourselves into theorists” – first working with pen and paper and then writing code and doing calculations to feed into computer models.
An illuminating result
To their surprise, the results predicted that circularly polarized laser light, whose waves spiral around the beam like a corkscrew, could be used to trigger HHG in topological insulators.
“One of the interesting things we observed is that circularly polarized laser light is very efficient at generating harmonics from the superhighway surfaces of the topological insulator, but not from the rest of it,” Baykusheva said. “This is something very unique and specific to this type of material. It can be used to get information about electrons that travel the superhighways and those that don't, and it can also be used to explore other types of materials that can’t be probed with linearly polarized light.”
The results lay out a recipe for continuing to explore HHG in quantum materials, said Reis, who is a co-author of the study.
“It’s remarkable that a technique that generates strong and potentially disruptive fields, which takes electrons in the material and jostles them around and uses them to probe the properties of the material itself, can give you such a clear and robust signal about the material’s topological states,” he said.
“The fact that we can see anything at all is amazing, not to mention the fact that we could potentially use that same light to change the material’s topological properties.”
Experiments at SLAC have resumed on a limited basis, Reis added, and the results of the theoretical work have given the team new confidence that they know exactly what they are looking for.
Researchers from the Max Planck POSTECH/KOREA Research Initiative also contributed to this report. Major funding for the study came from the DOE Office of Science and the Swiss National Science Foundation.
Citation: Denitsa Baykusheva et al., Physical Review A, 2 February 2020 (10.1103/PhysRevA.103.023101)
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Ultra-thin designer materials unlock quantum phenomena
A team of theoretical and experimental physicists have designed a new ultra-thin material that they have used to create elusive quantum states. Called one-dimensional Majorana zero energy modes, these quantum states could have a huge impact for quantum computing.
At the core of a quantum computer is a qubit, which is used to make high-speed calculations. The qubits that Google, for example, in its Sycamore processor unveiled last year, and others are currently using are very sensitive to noise and interference from the computer’s surroundings, which introduces errors into the calculations. A new type of qubit, called a topological qubit, could solve this issue, and 1D Majorana zero energy modes may be the key to making them.
‘A topological quantum computer is based on topological qubits, which are supposed to be much more noise tolerant than other qubits. However, topological qubits have not been produced in the lab yet,’ explains Professor Peter Liljeroth, the lead researcher on the project.
What are MZMs?
MZMs are groups of electrons bound together in a specific way so they behave like a particle called a Majorana fermion, a semi-mythical particle first proposed by semi-mythical physicist Ettore Majorana in the 1930s. If Majorana’s theoretical particles could be bound together, they would work as a topological qubit. One catch: no evidence for their existence has ever been seen, either in the lab or in astronomy. Instead of attempting to make a particle that no one has ever seen anywhere in the universe, researchers instead try to make regular electrons behave like them.
To make MZMs, researchers need incredibly small materials, an area in which Professor Liljeroth’s group at Aalto University specialises. MZMs are formed by giving a group of electrons a very specific amount of energy, and then trapping them together so they can’t escape. To achieve this, the materials need to be 2-dimensional, and as thin as physically possible. To create 1D MZMs, the team needed to make an entirely new type of 2D material: a topological superconductor.
Topological superconductivity is the property that occurs at the boundary of a magnetic electrical insulator and a superconductor. To create 1D MZMs, Professor Liljeroth’s team needed to be able to trap electrons together in a topological superconductor, however it’s not as simple as sticking any magnet to any superconductor.
‘If you put most magnets on top of a superconductor, you stop it from being a superconductor,’ explains Dr. Shawulienu Kezilebieke, the first author of the study. ‘The interactions between the materials disrupt their properties, but to make MZMs, you need the materials to interact just a little bit. The trick is to use 2D materials: they interact with each other just enough to make the properties you need for MZMs, but not so much that they disrupt each other.’
The property in question is the spin. In a magnetic material, the spin is aligned all in the same direction, whereas in a superconductor the spin is anti-aligned with alternating directions. Bringing a magnet and a superconductor together usually destroys the alignment and anti-alignment of the spins. However, in 2D layered materials the interactions between the materials are just enough to “tilt” the spins of the atoms enough that they create the specific spin state, called Rashba spin-orbit coupling, needed to make the MZMs.
Finding the MZMs
The topological superconductor in this study is made of a layer of chromium bromide, a material which is still magnetic when only one-atom-thick. Professor Liljeroth’s team grew one-atom-thick islands of chromium bromide on top of a superconducting crystal of niobium diselenide, and measured their electrical properties using a scanning tunneling microscope. At this point, they turned to the computer modelling expertise of Professor Adam Foster at Aalto University and Professor Teemu Ojanen, now at Tampere University, to understand what they had made.
‘There was a lot of simulation work needed to prove that the signal we’re seeing was caused by MZMs, and not other effects,’ says Professor Foster. ‘We needed to show that all the pieces fitted together to prove that we had produced MZMs.’
Now the team is sure that they can make 1D MZMs in 2-dimensional materials, the next step will be to attempt to make them into topological qubits. This step has so far eluded teams who have already made 0-dimensional MZMs, and the Aalto team are unwilling to speculate on if the process will be any easier with 1-dimensional MZMs, however they are optimistic about the future of 1D MZMs.
‘The cool part of this paper is that we’ve made MZMs in 2D materials,’ said Professor Liljeroth ‘In principle these are easier to make and easier to customise the properties of, and ultimately make into a usable device.’
The research collaboration included researchers from Tampere University in Finland, and M.Curie-Sklodowska University in Poland.
Published at Thu, 17 Dec 2020 18:53:38 +0000


Please read this guest post about the quantum Internet by Stephanie Wehner, Professor at the University of Technology in Delft, The Netherlands.
In March 2017, we invited Stephanie Wehner, Professor at QuTech at the Delft University of Technology to give a guest-lecture to RIPE NCC staff about the Quantum Internet project. We were curious to learn about this new technology, its consequences for the "traditional" Internet, and how we can make the connection between cutting-edge research and the RIPE community.
The Technical Basics of Quantum Computing
The goal of the quantum Internet is to enable transmission of quantum bits (qubits) between any two points on earth in order to solve problems that are intractable classically. Qubits are very different from classical bits in that they can be “0” and “1” at the same time, and cannot be copied.
Currently, it is possible to make a transmission over 100km, and run a single application known as quantum key distribution. The next challenge is to go long distance, and to connect small quantum processors to enable a larger range of applications. Thankfully, these quantum processors do not need to be large quantum computers: a handful of qubits are already enough to outperform classical communication. The reason why quantum Internet nodes do not need many qubits to be useful (unlike quantum computers) is that a quantum Internet derives its advantages from quantum entanglement for which even a single qubit can be enough. In contrast, a quantum computer always needs more qubits than can be simulated on a classical supercomputer to be useful.
Use-cases for quantum networking currently include:
- Secure communication with the help of quantum key distribution
- Clock synchronisation
- Combining distant telescopes to form one much more powerful telescope
- Advantages for classic problems in distributed systems such as achieving consensus and agreement about data distributed in the cloud
- Sending exponentially fewer qubits than classical bits to solve some distributed computing problems
- Secure access to a powerful quantum computer using only very simple “desktop” quantum devices
- Combining small quantum computers to form a larger quantum computing cluster
In general, quantum networking exploits two essential features of quantum entanglement: first, quantum entanglement is inherently private – if two network nodes are maximally entangled, then this entanglement is completely shielded from anything else in the universe according to the laws of quantum mechanics. Second, quantum entanglement allows maximal coordination – measuring two qubits that are entangled always results in the same outcome no matter how far they are apart. It is this feature of perfect coordination that gives advantages in, for example, clock synchronisation or even winning online bridge more often using quantum entanglement.
Dutch Test-bed Network
QuTech at the Delft University of Technology and TNO, in collaboration with the European Quantum Internet Alliance, is leading with the efforts to establish a quantum Internet, and aims to have a demonstration network in 2020 connecting four cities in the Netherlands. This network may be the first of its kind in 2020, and will allow the end to end transmission of qubits between any two network nodes consisting of few qubit processors.
The quantum network in The Netherlands
Transmitting Qubits Over Long Distances
One may wonder why it is difficult to send qubits over long distances. Roughly speaking, one qubit corresponds to just one photon which is easily lost over distance. The technology needed to transmit qubits over long distances is called a quantum repeater. A quantum repeater works very differently than a classical repeater, exploiting the fact that qubits can be transmitted using quantum teleportation. Quantum teleportation works by first creating two entangled qubits between two network nodes. Once the entangled link is created, the qubit to be transmitted can be sent over it.
Imagine two network nodes that are 200kms apart – too far for direct transmission. A quantum repeater in the middle works as follows: first two entangled qubits are created between the first endpoint and the repeater. This is possible since this endpoint and the repeater are only 100kms apart. Second, two entangled qubits are created between the repeater and the second endpoint. The repeater then uses quantum teleportation to transfer the qubit that is entangled with the first endpoint to the second endpoint. The end result is end-to-end entanglement between the two endpoints. Qubit data can now be transmitted using this entangled link.
The concept of a quantum repeater
Involvement with the RIPE Community
After this research project is accomplished, industry partners from the RIPE community are needed to take over in order to scale, increase the speed and make this new technology added to the "traditional" Internet, as a parallel service. A quantum Internet also needs significant protocol development to define a networking stack adapted to the transmission of qubits, and the management of entanglement. This requires the help of the RIPE community at large to develop a classical protocol stack to control a quantum Internet and implement protocols to route qubits.
Join us at the Open Day at QuTech
On 22 June 2017, QuTech is organising a presentation and a tour at the lab at QuTech in Delft, the Netherlands. Here is the programme for the day:
10:00 Presentation - Stephanie
11:00 Start lab tours
12:00 Light lunch & meet & greet - Stephanie
Please note that participation is limited to 25 people. Please register HERE if you are interested in participating.
If you are interested to learn more, please join us at one of the events listed above, or get in touch with Stephanie and her team. You can also leave a comment below.


Wormhole A wormhole, also known as an Einstein–Rosen bridge, is a hypothetical topological feature of spacetime that would fundamentally be a "shortcut" through spacetime. A wormhole is much like a tunnel with two ends each in separate points in spacetime. For a simplified notion of a wormhole, visualize space as a two-dimensional (2D) surface. Learning and training: statistics and myths How Effective is Training? Laurie Bassi measured how well employees are trained and developed (Delahoussaye, et al., 2002). She writes that organizations that make large investments in people typically have lower employee turnover, which is associated with higher customer satisfaction, which in turn is a driver of profitability (p22). A second driver is manager proficiency — good managers determine if people stay or go, and this is also influenced by training and development. She further writes that the education and training variable is the most significant predictor of an organization's success as compared to price-to-earning ratios, price-to-book statistics, and measures of risk and volatility. Bassi puts her theories to the test — her and a fellow partner launched an investment firm that buys stocks in companies that invest heavily in employee training.
New Wormhole Theory Uses Space Photon Energy “Fluid” A new theory expands on other theories and adds photon energy “fluid” as a way to support wormholes. The introduction to the paper states the following. Wormholes are hypothetical geometrical structures connecting two universes or two distant parts of the same universe. For a simple visual explanation of a wormhole, consider spacetime visualized as a two-dimensional (2D) surface. If this surface is folded along a third dimension, it allows one to picture a wormhole “bridge”.  “A possible cause of the late-time cosmic acceleration is an exotic ﬂuid with an equation of state lying within the phantom regime, i.e., w = p/ρ < −1.
How secure is my password? Entries are 100% secure and not stored in any way or shared with anyone. Period. As Seen On New data confirms: Neutrinos are still traveling faster than light "It is worth pointing out, however, that the latest arXiv preprint lists 179 authors, while the original lists 174. Would you ever classify five people as "most of" 15? To make things more confusing . . . "four new people" have decided not to sign, according to Science. Now, none of the above numbers may match up . . .." The original 174 include a duplicate " F. World Economic Forum. 8 digital skills we must teach our children The social and economic impact of technology is widespread and accelerating. The speed and volume of information have increased exponentially. Experts are predicting that 90% of the entire population will be connected to the internet within 10 years. With the internet of things, the digital and physical worlds will soon be merged. These changes herald exciting possibilities. But they also create uncertainty.
Gravitational-wave finding causes 'spring cleaning' in physics Detlev van Ravenswaay/Science Photo Library Artist's rendering of 'bubble universes' within a greater multiverse — an idea that some experts say was bolstered with this week's discovery of gravitational waves. On 17 March, astronomer John Kovac of the Harvard-Smithsonian Center for Astrophysics presented long-awaited evidence of gravitational waves — ripples in the fabric of space — that originated from the Big Bang during a period of dramatic expansion known as inflation. By the time the Sun set that day in Cambridge, Massachusetts, the first paper detailing some of the discovery’s consequences had already been posted online1, by cosmologist David Marsh of the Perimeter Institute for Theoretical Physics in Waterloo, Canada, and his colleagues. Cosmologist Marc Kamionkowski of Johns Hopkins University in Baltimore, Maryland, agrees that some axion models no longer work, “because they require inflation to operate at a lower energy scale than the one indicated by BICEP2”.
Quantum world record smashed 14-Nov-2013 [ Print | E-mail ] Share [ Close Window ] Contact: University of Oxford Press email@example.com 44-186-528-3877University of Oxford A normally fragile quantum state has been shown to survive at room temperature for a world record 39 minutes, overcoming a key barrier towards building ultrafast quantum computers. An international team including Stephanie Simmons of Oxford University, UK, report in this week's Science a test performed by Mike Thewalt of Simon Fraser University, Canada, and colleagues.
New Experiments to Pit Quantum Mechanics Against General Relativity It starts like a textbook physics experiment, with a ball attached to a spring. If a photon strikes the ball, the impact sets it oscillating very gently. But there’s a catch. Before reaching the ball, the photon encounters a half-silvered mirror, which reflects half of the light that strikes it and allows the other half to pass through. What happens next depends on which of two extremely well-tested but conflicting theories is correct: quantum mechanics or Einstein’s theory of general relativity; these describe the small- and large-scale properties of the universe, respectively. In a strange quantum mechanical effect called “superposition,” the photon simultaneously passes through and reflects backward off the mirror; it then both strikes and doesn’t strike the ball.
Most students don't know when news is fake Preteens and teens may appear dazzlingly fluent, flitting among social-media sites, uploading selfies and texting friends. But they’re often clueless about evaluating the accuracy and trustworthiness of what they find. Some 82% of middle-schoolers couldn’t distinguish between an ad labeled “sponsored content” and a real news story on a website, according to a Stanford University study of 7,804 students from middle school through college. The study, set for release Tuesday, is the biggest so far on how teens evaluate information they find online. Many students judged the credibility of newsy tweets based on how much detail they contained or whether a large photo was attached, rather than on the source. More than two out of three middle-schoolers couldn’t see any valid reason to mistrust a post written by a bank executive arguing that young adults need more financial-planning help.
Carver Mead's Spectator Interview From American Spectator, Sep/Oct2001, Vol. 34 Issue 7, p68 Carver Mead The Spectator Interview Once upon a time, Nobel Laureate leader of the last great generation of physicists, threw down the gauntlet to anyone rash enough to doubt the fundamental weirdness, the quark-boson-muon-strewn amusement park landscape of late 20th-century quantum physics. "Things on a very small scale behave like nothing you have direct experience about. Visual learning Visual thinking is a learning style where the learner better understands and retains information when ideas, words and concepts are associated with images. Research tells us that the majority of students in a regular classroom need to see information in order to learn it. Some common visual learning strategies include creating graphic organizers, diagramming, mind mapping, outlining and more.
New qubit control bodes well for future of quantum computing (Phys.org)—Yale University scientists have found a way to observe quantum information while preserving its integrity, an achievement that offers researchers greater control in the volatile realm of quantum mechanics and greatly improves the prospects of quantum computing. Quantum computers would be exponentially faster than the most powerful computers of today. "Our experiment is a dress rehearsal for a type of process essential for quantum computing," said Michel Devoret, the Frederick William Beinecke Professor of Applied Physics & Physics at Yale and principal investigator of research published Jan. 11 in the journal Science. "What this experiment really allows is an active understanding of quantum mechanics.
Education Home of everything Gamification Education -- research, community, case studies and more -- as part of the Gamification.org family of wikis. Want to help us create this website? Contact us! Introduction Education affects everyone.


-By Glenn Roberts Jr.
A team led by physicists at Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley has successfully observed the scrambling of quantum information, which is thought to underlie the behavior of black holes, using qutrits: information-storing quantum units that can represent three separate states at the same time. Their efforts also pave the way for building a quantum information processor based upon qutrits.
The black hole information paradox
The new study, recently published in the journal Physical Review X, makes use of a quantum circuit that is inspired by the longstanding physics question: What happens to information when it enters a black hole?
Beyond the connection to cosmology and fundamental physics, the team’s technical milestones that made the experiment possible represent important progress toward using more complex quantum processors for quantum computing, cryptography, and error detection, among other applications.
While black holes are considered one of the most destructive forces in the universe – matter and light cannot escape their pull, and are quickly and thoroughly scrambled once they enter – there has been considerable debate about whether and how information is lost after passing into a black hole.
The late physicist Stephen Hawking showed that black holes emit radiation – now known as Hawking radiation – as they slowly evaporate over time. In principle, this radiation could carry information about what’s inside the black hole – even allowing the reconstruction of information that passes into the black hole.
And by using a quantum property known as entanglement, it is possible to perform this reconstruction significantly more rapidly, as was shown in earlier work.
Quantum entanglement defies the rules of classical physics, allowing particles to remain correlated even when separated by large distances so that the state of one particle will inform you about the state of its entangled partner. If you had two entangled coins, for example, knowing that one coin came up heads when you looked at it would automatically tell you that the other entangled coin was tails, for example.
Most efforts in quantum computing seek to tap into this phenomenon by encoding information as entangled quantum bits, known as qubits (pronounced CUE-bits). Like a traditional computer bit, which can hold the value of zero or one, a qubit can also be either a zero or one. But in addition, a qubit can exist in a superposition that is both one and zero at the same time. In the case of a coin, it’s like a coin flip that can represent either heads or tails, as well as the superposition of both heads and tails at the same time.
The power of 3: Introducing qutrits
Each qubit you add to a quantum computer doubles its computing power, and that exponential increase soars when you use quantum bits capable of storing more values, like qutrits (pronounced CUE-trits). Because of this, it takes far fewer qubits and even fewer qutrits or qudits – which describes quantum units with three or more states – to perform complex algorithms capable of demonstrating the ability to solve problems that cannot be solved using conventional computers.
That said, there are a number of technical hurdles to building quantum computers with a large number of quantum bits that can operate reliably and efficiently in solving problems in a truly quantum way.
In this latest study, researchers detail how they developed a quantum processor capable of encoding and transmitting information using a series of five qutrits, which can each simultaneously represent three states. And despite the typically noisy, imperfect, and error-prone environment of quantum circuity, they found that their platform proved surprisingly resilient and robust.
Qutrits can have a value of zero, one, or two, holding all of these states in superposition. In the coin analogy, it’s like a coin that has the possibility of coming up as heads, tails, or in landing on its thin edge.
“A black hole is an extremely good encoder of information,” said Norman Yao, a faculty scientist in Berkeley Lab’s Materials Sciences Division and an assistant professor of physics at UC Berkeley who helped to lead the planning and design of the experiment. “It smears it out very quickly, so that any local noise has an extremely hard time destroying this information.”
But, he added, “The encoder is so darn good that it’s also very hard to decode this information.”
Creating an experiment to mimic quantum scrambling
The team set out to replicate the type of rapid quantum information smearing, or scrambling, in an experiment that used tiny devices called nonlinear harmonic oscillators as qutrits. These nonlinear harmonic oscillators are essentially sub-micron-sized weights on springs that can be driven at several distinct frequencies when subjected to microwave pulses.
A common problem in making these oscillators work as qutrits, though, is that their quantum nature tends to break down very quickly via a mechanism called decoherence, so it is difficult to distinguish whether the information scrambling is truly quantum or is due to this decoherence or other interference, noted Irfan Siddiqi, the study’s lead author.
Siddiqi is director of Berkeley Lab’s Advanced Quantum Testbed, a faculty scientist in the Lab’s Computational Research and Materials Sciences divisions, and a professor of physics at UC Berkeley.
The testbed, which began accepting proposals from the quantum science community in 2020, is a collaborative research laboratory that provides open, free access to users who want to explore how superconducting quantum processors can be used to advance scientific research. The demonstration of scrambling is one of the first results from the testbed’s user program.
“In principle, an isolated black hole exhibits scrambling,” Siddiqi said, “but any experimental system also exhibits loss from decoherence. In a laboratory, how do you distinguish between the two?”
A key to the study was in preserving the coherence, or orderly patterning, of the signal carried by the oscillators for long enough to confirm that quantum scrambling was occurring via the teleportation of a qutrit. While teleportation may conjure up sci-fi imagery of “beaming up” people or objects from a planet’s surface onto a spaceship, in this case there is only the transmission of information – not matter – from one location to another via quantum entanglement.
Another essential piece was the creation of customized logic gates that enable the realization of “universal quantum circuits,” which can be used to run arbitrary algorithms. These logic gates allow pairs of qutrits to interact with each other and were designed to handle three different levels of signals produced by the microwave pulses.
One of the five qutrits in the experiment served as the input, and the other four qutrits were in entangled pairs. Because of the nature of the qutrits’ entanglement, a joint measurement of one of the pairs of qutrits after the scrambling circuit ensured that the state of the input qutrit was teleported to another qutrit.
Mirrored black holes and wormholes
The researchers used a technique known as quantum process tomography to verify that the logic gates were working and that the information was properly scrambled, so that it was equally likely to appear in any given part of the quantum circuit.
Siddiqi said that one way to think about how the entangled qutrits transmit information is to compare it to a black hole. It’s as if there is a black hole and a mirrored version of that black hole, so that information passing in one side of the mirrored black hole is transmitted to the other side via entanglement.
Looking forward, Siddiqi and Yao are particularly interested in tapping into the power of qutrits for studies related to traversable wormholes, which are theoretical passages connecting separate locations in the universe, for example.
A scientist from the Perimeter Institute for Theoretical Physics in Canada also participated in the study, which received supported from the U.S. Department of Energy’s Office of Advanced Scientific Computing Research and Office of High Energy Physics; and from the National Science Foundation’s Graduate Research Fellowship.
Founded in 1931 on the belief that the biggest scientific challenges are best addressed by teams, Lawrence Berkeley National Laboratory and its scientists have been recognized with 14 Nobel Prizes. Today, Berkeley Lab researchers develop sustainable energy and environmental solutions, create useful new materials, advance the frontiers of computing, and probe the mysteries of life, matter, and the universe. Scientists from around the world rely on the Lab’s facilities for their own discovery science. Berkeley Lab is a multiprogram national laboratory, managed by the University of California for the U.S. Department of Energy’s Office of Science.
DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.


A Chinese satellite has split pairs of "entangled photons" and transmitted them to separate ground stations 745 miles (1,200 kilometers) apart, smashing the previous distance record for such a feat and opening new possibilities in quantum communication.
In quantum physics, when particles interact with each other in certain ways they become "entangled." This essentially means they remain connected even when separated by large distances, so that an action performed on one affects the other.
In a new study published online today (June 15) in the journal Science, researchers report the successful distribution of entangled photon pairs to two locations on Earth separated by 747.5 miles (1,203 km). [The 18 Biggest Unsolved Mysteries in Physics]
Quantum entanglement has interesting applications for testing the fundamental laws of physics, but also for creating exceptionally secure communication systems, scientists have said. That's because quantum mechanics states that measuring a quantum system inevitably disturbs it, so any attempt to eavesdrop is impossible to hide.
But, it's hard to distribute entangled particles — normally photons — over large distances. When traveling through air or over fiber-optic cables, the environment interferes with the particles, so with greater distances, the signal decays and becomes too weak to be useful.
In 2003, Pan Jianwei, a professor of quantum physics at the University of Science and Technology of China, started work on a satellite-based system designed to beam entangled photon pairs down to ground stations. The idea was that because most of the particle's journey would be through the vacuum of space, this system would introduce considerably less environmental interference.
"Many people then thought it [was] a crazy idea, because it was very challenging already doing the sophisticated quantum-optics experiments inside a well-shielded optical table," Pan told Live Science. "So how can you do similar experiments at thousand-kilometers distance scale and with the optical elements vibrating and moving at a speed of 8 kilometers per second [5 miles per second]?"
In the new study, researchers used China's Micius satellite, which was launched last year, to transmit the entangled photon pairs. The satellite features an ultrabright entangled photon source and a high-precision acquiring, pointing and tracking (APT) system that uses beacon lasers on the satellite and at three ground stations to line up the transmitter and receivers.
Once the photons reached the ground stations, the scientists carried out tests and confirmed that the particles were still entangled despite having traveled between 994 miles and 1,490 miles (1,600 and 2,400 km), depending on what stage of its orbit the satellite was positioned at.
Only the lowest 6 miles (10 km) of Earth's atmosphere are thick enough to cause significant interference with the photons, the scientists said. This means the overall efficiency of their link was vastly higher than previous methods for distributing entangled photons via fiber-optic cables, according to the scientists. [Twisted Physics: 7 Mind-Blowing Findings]
"We have already achieved a two-photon entanglement distribution efficiency a trillion times more efficient than using the best telecommunication fibers," Pan said. "We have done something that was absolutely impossible without the satellite."
Apart from carrying out experiments, one of the potential uses for this kind of system is for "quantum key distribution," in which quantum communication systems are used to share an encryption key between two parties that is impossible to intercept without alerting the users. When combined with the correct encryption algorithm, this system is uncrackable even if encrypted messages are sent over normal communication channels, experts have said.
Artur Ekert, a professor of quantum physics at the University of Oxford in the United Kingdom, was the first to describe how entangled photons could be used to transmit an encryption key.
"The Chinese experiment is quite a remarkable technological achievement," Ekert told Live Science. "When I proposed the entangled-based quantum key distribution back in 1991 when I was a student in Oxford, I did not expect it to be elevated to such heights!"
The current satellite is not quite ready for use in practical quantum communication systems, though, according to Pan. For one, its relatively low orbit means each ground station has coverage for only about 5 minutes each day, and the wavelength of photons used means it can only operate at night, he said.
Boosting coverage times and areas will mean launching new satellites with higher orbits, Pan said, but this will require bigger telescopes, more precise tracking and higher link efficiency. Daytime operation will require the use of photons in the telecommunications wavelengths, he added.
But while developing future quantum communication networks will require considerable work, Thomas Jennewein, an associate professor at the University of Waterloo's Institute for Quantum Computing in Canada, said Pan's group has demonstrated one of the key building blocks.
"I have worked in this line of research since 2000 and researched on similar implementations of quantum- entanglement experiments from space, and I can therefore very much attest to the boldness, dedication and skills that this Chinese group has shown," he told Live Science.
Original article on Live Science.


StJohns Field is a massive helium reservoir and immense carbon storage basin located on 152,000 acres in Apache County, Arizona. Extensive third-party geological studies performed on the property indicate reserves of up to 33 billion cubic feet of helium in shallow, easily accessible reservoirs. Capable of producing one billion cubic feet of helium per year, it will be among the most prolific helium production sites in the world.
While most helium is extracted from natural gas deposits, the helium produced at St Johns is highly unusual in that it does not contain any hydrocarbons. The gas deposit is composed almost entirely of carbon dioxide, and as the helium is extracted in the production process, all of the excess CO2 will be reinjected into isolated geological formations and safely sequestered deep underground for millennia. As a result, the helium produced at St Johns is exceptionally clean and environmentally friendly, with a net zero carbon footprint.
Helium is the only element on the planet that is a completely non-renewable resource. It is both scarce and finite, with no commercially viable industrial process to replicate it. Helium is formed by the natural radioactive decay process of Uranium, and can be trapped underground if a halite or anhydrite cap exists above it. If helium is not trapped in this way, it escapes to the atmosphere and rises into space.
Helium is the coldest element, with a boiling point of only 4° Kelvin, and has unique superfluid properties. It has many applications as a high-tech coolant, and is a critical component for nearly all modern technology systems.
For example, liquid helium is used to cool the magnets in MRI systems, helping to optimize their function. It is also used to control the temperature of silicon in the semiconductor manufacturing process. Because Helium is inert and non-flammable, it is used in space and satellite systems as a purge gas in hydrogen systems, and as a pressurizing agent for ground and flight fluid systems. Both NASA and SpaceX are major consumers of helium.
Data centers use helium to encapsulate hard drives, which reduces friction and energy consumption - Google, Amazon, and Netflix are all major consumers. Quantum computing systems also use liquid helium in dilution refrigerators, providing temperatures as low as 2 mK.
Inaddition to its immense helium reserves, the geological characteristics of St Johns make it an ideal storage basin for carbon dioxide. With the ability to inject 22 million metric tons of CO2 per year and a total storage capacity of over 1 billion metric tons, St Johns is set to become one of the largest carbon capture sites in the world. Strategically located in the fast-growing American Southwest near several coal-fired power plants, Proton Green is well positioned to become a critical carbon sequestration hub in the region. The exceptionally well-suited geological storage structure, with its remote location, pipeline infrastructure, right of way, and Class VI storage permits (once granted) will be significant barriers to entry for competitors.
Hydrogen is steadily emerging as one of the most effective fossil fuel replacements and could become a lucrative opportunity for Proton Green as the global movement toward decarbonization and a net zero economy continues. Our processing plants are capable of producing large volumes of industrial-grade hydrogen while simultaneously sequestering the excess CO2 in underground storage basins, thereby qualifying as blue hydrogen. The hydrogen we produce can then be sold into the California markets and will be eligible for Low Carbon Fuel Standard (LCFS) credits as we help drive the transition toward a sustainable fuel and energy source.
Proton Green will partner with government agencies, NGOs, research institutions, and startup companies to create a cutting-edge incubator and innovation center for emerging carbon-neutral technologies and processes like blue hydrogen, CO2-enhanced geothermal energy, biomass energy, and carbon fiber materials. The research center will be located in a designated Opportunity Zone in the extreme southwest corner of the property, and Proton Green will provide CO2 to support research and development activities. We are currently pursuing an opportunity to develop a bioenergy plant that will convert forest-wood waste into biofuel.
A seasoned independent oil and gas producer since 1982, Mr. Looper has extensive experience drilling and operating wells in Colorado, Kentucky, Louisiana, New Mexico, Oklahoma, Texas and Wyoming. He also has project management in Botswana, Canada, South Africa and Zimbabwe. Since 1993, Mr. Looper has been focused on the development of large resource plays in West Texas at Riata Energy, Inc. and most recently in the Barnett Shale trend, where his capital providers achieved>100% rates of return. Mr. Looper is an alumni of West Texas State University, T. Boone Pickens School of Business and participated in the Harvard Business School, Executive Management Program 2003-2007.
Mr. Coates is a highly experienced oil and gas professional with a career emphasis on large-scale, unconventional resource development. He is currently involved in Helium development, carbon capture, oil and gas, and geothermal projects. His educational background in geology, geochemistry and engineering led to an initial career with Advanced Resources International, a domestic and international technical consulting firm at the forefront of unconventional resource development and Carbon Capture technology. He subsequently joined MCN Corp (now DTE Energy) in a senior management role to successfully develop a multi TCF natural gas reserve base in the US. He also co-founded an E&P company Patrick Energy with the funding of a family office that has led to a series of privately funded ($200MM capital) E&P companies built and sold over the past twenty years.