Photon Qubit is Made of Two Colors
The discovery of the photon, the quantum particle of light, played a key role in the development of quantum physics. Today, photons are among the most advanced building blocks for quantum technologies, such as quantum computing , secure communication , and precision measurement . These applications typically rely on quantum control of a photon’s polarization or its spatial mode. Surprisingly, the most manifest property of light—its color or frequency—is difficult to manipulate on the quantum level. An experiment now demonstrates a toolbox for creating, manipulating, and detecting single photons in a quantum superposition of two discrete frequencies . The approach requires an interaction between different frequency components of light, which Stéphane Clemmen from Cornell University, New York, and colleagues have achieved by making use of nonlinear processes in optical fibers. Such photonic quantum bits (qubits) could be useful for connecting quantum systems operating at different frequencies in a quantum network.
According to quantum physics, monochromatic light of frequency , such as the light emitted by a laser, is composed of photons of energy , where is the Planck constant. Polychromatic light, such as the light emitted by the Sun, contains photons of many different frequencies. However, each individual photon usually has a well-defined frequency and energy. Interestingly, the superposition principle of quantum physics allows for yet another version of polychromatic light: a single photon in a superposition of two discrete frequencies and . In this case, neither the frequency nor the energy of the photon is well defined. In some sense, such a “bichromatic” photon can be thought of as having two different colors at the same time, only one of which would be revealed if the photon were measured by a spectrometer or detected by eye.
However, the creation and manipulation of bichromatic photons turns out to be challenging. The difficulty is that such processes require an interaction between photons of different frequencies, and in most media, light beams do not interact. The situation changes if light propagates in a nonlinear medium, where the optical properties vary with the intensity of light. The nonlinear response results in an interaction between photons, providing a means with which to convert them to a different frequency. To make this process efficient, however, the medium must be pumped with separate high-power laser beams. These beams are tuned to a different frequency than the weak single-photon signal, but care must be taken to avoid noise processes that generate additional photons in the signal channel. Several experiments have succeeded in creating suitable single-photon nonlinearities. Clemmen et al. build on this work by not only creating bichromatic photons, but also by manipulating them and demonstrating their coherence.
In their experiments, Clemmen et al. realize the efficient frequency conversion of single photons with a process called Bragg-scattering four-wave mixing. The main ingredient in this approach is a 100-m-long fiber, which the authors pump with two laser beams of unequal frequencies to obtain the required nonlinear response. The frequency difference between the beams determines the difference between the frequencies and involved in the final single-photon superposition. The team achieves the necessary low noise level by cooling the fiber in a cryostat. Within this setup, sending a single photon of frequency and quantum state through the fiber converts the photon state into a superposition, , where is the mixing angle and is the relative phase between the frequency components. The angles and can be adjusted by tuning the amplitude and phase of the pump lasers, allowing the researchers to encode one bit of quantum information on the photon (see representation at the top of Fig. 1).
Importantly, Clemmen et al. prove that they have generated a coherent superposition rather than an incoherent mixture in which photons randomly acquire one of the two frequencies. To do so, the researchers perform Ramsey spectroscopy, a technique commonly used to measure coherence in atomic clocks or nuclear magnetic resonance (NMR). The Ramsey sequence is illustrated in Fig. 1. The researchers adjust the pump lasers such that after the photon passes through the fiber it is in an equal superposition of the two photon frequencies, or . In the terminology of NMR, this corresponds to a pulse. Subsequently, they adjust the phase, , by introducing a propagation delay. Finally, they send the photon through the fiber a second time, corresponding to a second pulse, which converts the state to . To analyze the state, they separate the two frequency components and detect each with a single-photon detector. When they vary the propagation delay, and therefore , the probability of detecting the photon oscillates sinusoidally between the two detectors, as expected. The observed contrast of these “Ramsey fringes” reaches up to 65%, proof that a coherent superposition is being generated. Moreover, the researchers show that there is only a small probability of detecting more than one photon at a time, confirming that their superposition state preserves the single-photon character of the initial state.
It is very appealing to see that a single particle of light can be in a superposition of two different colors. So far, the wavelengths involved are in the infrared near 1280 nm—outside the range of human vision—and differ by about 4 nm. But when translated to the visible spectrum (roughly 380–780 nm), such a wavelength difference could be discriminated with the bare eye. In the future, the methods that Clemmen et al. have demonstrated could be used to interface quantum systems operating at different frequencies, such as solid-state and atomic quantum memories [5–8]. One could envision two physically different quantum memories, each absorbing one part of the single photon in a frequency superposition. This would entangle the quantum memories because they share a single excitation in a coherent way. Such a protocol may be useful for creating quantum networks [9, 10], which could be the basis for quantum communication, computing, and simulation. Another application of the bichromatic qubits could be spectroscopy that requires only small amounts of light: the idea would be to look for spectrally dependent phase changes in the qubits’ states. These applications would benefit from extending the technique demonstrated by Clemmen et al. to larger frequency or wavelength differences.
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KENSINGTON, Australia — Quantum computing could soon become a reality that changes digital technology forever after a milestone achievement by researchers in Australia. The team has proven that virtually error-free computer operations are possible using a silicon-based quantum device. Moreover, scientists found it’s possible to build these lightning-fast computers using current semiconductor manufacturing technology available today.
“Today’s publication shows our operations were 99 percent error-free,” says Professor Andrea Morello from the University of New South Wales-Sydney in a release.
“When the errors are so rare, it becomes possible to detect them and correct them when they occur. This shows that it is possible to build quantum computers that have enough scale, and enough power, to handle meaningful computation.”
Morello, who leads a team of researchers from the United States, Japan, Egypt, and Australia, is building what they call a “universal quantum computer” that is capable of performing more than one application.
“This piece of research is an important milestone on the journey that will get us there,” Prof. Morello adds.
Why are quantum computers so special?
In a nutshell, quantum computers find better and quicker ways to solve problems. Scientists believe quantum technology could solve extremely complex problems in seconds, while traditional supercomputers you see today could need months or even years to crack certain codes.
What makes these next generation supercomputers different from your everyday smartphone and laptop is how they process data. Quantum computers harness the properties of quantum physics to store data and perform their functions. While traditional computers use “bits” to encode information on your devices, quantum technology uses “qubits.”
The main difference between these two are that bits process information in binary fashion — meaning something is either a “0” or “1” or a yes/no answer. They represent this two-choice system through the absence or presence of an electrical signal in the computer.
Qubits, on the other hand, use quantum objects which act as information processors — such as spin (controlling the spin of charged particles in a semiconductor), trapped atoms or ions, photons (particles of light), or semiconducting circuits.
Like a bit, qubits also have two distinctive states representing “0” and “1,” but they are also capable of working in “superposition” states as well. A qubit can account for incompatible measurements (beyond 0 and 1) and even entangle with other qubits. All this makes them incredibly more powerful than the average computer bit.
Cracking the 99 percent threshold
The new study actually features three separate reports which detail the researchers’ breakthrough into super-accurate quantum computing.
Prof. Morello’s team achieved a one-qubit operation fidelity of 99.95 percent, meaning a qubit’s ability to successfully pass a test. They also achieved a two-qubit fidelity of 99.37 percent. The team conducted this test using a three-qubit system consisting of an electron and two phosphorous atoms inside silicon.
Another team in the Netherlands reached the 99 percent accuracy threshold using qubits consisting of electron spins in a stack of silicon and silicon-germanium alloy (Si/SiGe).
Finally, a third team in Japan broke the 99 percent barrier with a two-electron system using Si/SiGe quantum dots.
Scientists are focusing on using qubits in silicon because of their stability and capability to hold quantum information for long periods of time. Prof. Morello’s previous studies demonstrated that he could preserve quantum data in silicon for 35 seconds. That may not sound like a lot to the average person, but it’s nearly a lifetime for quantum computers.
“In the quantum world, 35 seconds is an eternity,” Prof. Morello explains. “To give a comparison, in the famous Google and IBM superconducting quantum computers the lifetime is about a hundred microseconds – nearly a million times shorter.”
Scientists discover how to make qubits interact with each other
The biggest breakthrough in the study, researchers say, is overcoming the need to isolate individual qubits in the computing process. Until now, it’s been seemingly impossible for qubits to interact with each other. The team used an electron encompassing two nuclei of phosphorus atoms to overcome this problem.
“If you have two nuclei that are connected to the same electron, you can make them do a quantum operation,” says study author Mateusz Mądzik.
“While you don’t operate the electron, those nuclei safely store their quantum information. But now you have the option of making them talk to each other via the electron, to realize universal quantum operations that can be adapted to any computational problem.”
“This really is an unlocking technology,” adds Dr. Serwan Asaad. “The nuclear spins are the core quantum processor. If you entangle them with the electron, then the electron can then be moved to another place and entangled with other qubit nuclei further afield, opening the way to making large arrays of qubits capable of robust and useful computations.”
With this breakthrough, study authors say semiconductor spin qubits in silicon could soon become the platform of choice as scientists build the next wave of reliable quantum computers.
“Until now, however, the challenge has been performing quantum logic operations with sufficiently high accuracy,” Prof. Morello concludes. “Each of the three papers published today shows how this challenge can be overcome to such a degree that errors can be corrected faster than they appear.”
The findings are published in the journal Nature.


As our demand for powerful processors rises, our need for a solution outside classical computing mounts. Quantum computing could help solve some of the more complex problems plaguing us. With quantum computers, we could map complex climate systems, solve impossibly complex encryption puzzles, and simulate advanced chemical processes. And this is just the tip of the iceberg.
As a result, there is an international race to arrive at a scalable, commercial quantum computer. Researchers all over the world are working diligently to find the perfect material to harness the power of a quantum bit, or qubit.
How does a quantum bit differ from a normal bit? In a classical computer, bits have discrete states. These bits are either 0 or 1. A pulse of energy, either in the form of 0 -- which is not the same as a complete lack of electrical impulse -- or a 1, is sent through transistors. These strings of zeroes and ones are simply instructions to the hardware. A string of these commands make up a byte. A string of bytes make up kilobytes, megabytes, gigabytes, terabytes and so on and so forth.
The software your computer uses is equipped to translate the commands supplied by a stream of bits. So, when a programmer creates a program in C++ or Java, for example, the words and phrases and mathematical equations she uses can all be reduced to zeroes and ones.
As you have probably already surmised, for the most part, a bit can be either one or the other: 1 or 0. There are two choices and no more. This is why coding in ones and zeroes is often referred to as binary code, and where the name bit, which stands for binary digit, comes from.
In quantum mechanics, there is something called superposition. Many are familiar with the famous Schrödinger's Cat thought experiment, in which an unseen cat is both alive and dead at the same time. In our everyday physical world, this idea does not make too much sense. A cat is either alive or dead. In other words, organic life is binary. Classical physics and classical computers follow this logic. They live in a binary world.
Qubits, however, are not bound to binary. Qubits can be either 0 or 1, or a combination of those states. This is the principle of superposition at work.
“A qubit can be thought of like an imaginary sphere,” writes Abigail Beall for Wired. “Whereas a classical bit can be in two states - at either of the two poles of the sphere - a qubit can be any point on the sphere.”
What’s so intriguing about qubits is their interactions with each other. In quantum computing, the sum is much larger than its individual parts. “Every time I add a quantum bit to a quantum computer, I double the computational power,” explains Michelle Simmons, the lead quantum researcher at the University of New South Wales, in a recent talk. “It’s predicted that… a 30-qubit computer... will be more powerful than the world’s most powerful supercomputer.” These quantum computers would be powerful enough to run sophisticated AI programs that could disrupt finance, medicine, and engineering industries.
To put this 30-qubit figure in context, IBM is, at present, leading the charge with a 16-qubit chip. “IBM Q has successfully built and tested two of its most powerful universal quantum computing processors to date,” IBM boasts. “16 qubits for public use and a 17-qubit prototype commercial processor.”
IBM, Microsoft, and Google are making these qubit chips by submerging superconductors in subzero temperatures. Simmons and her team are simply imprinting atoms in silicon. Other researchers have taken a more rigorous approach. They applied the theory of time crystals, an idea proposed in 2012 by Nobel laureate Frank Wilczek, to build their quantum technologies.
The University of Maryland and Harvard University synthesized time crystals in their own research labs, using disparate approaches. In University of Maryland’s system, they use an ion trap system to form patterns in time. Harvard exploited flaws in diamonds, a spatial crystal, to synthesize a discrete time crystal of their own.
What could these sci-fi-sounding time crystals be used for? “Time crystals,” Wilczek said in a recent presentation to university students, “are just what the doctor ordered for this technology.” Indeed, quantum computers are sensitive and require a very precise global clock, a potential use for the new type of matter. Further, time crystals could be used for information tasks and quantum memory.
Qubits, superconductors, and time crystals. As far-flung as the future of quantum computing may appear to be, it is likely closer than you think. We need more computationally complex machines to power our most pressing problems. Quantum computers could lead us to some very compelling solutions.
About the author: Josh Althauser is an entrepreneur with a background in design and M&A. He's also a developer, open source advocate, and designer. You may connect with him on Twitter.
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You may have been reading about breakthroughs in the area of quantum computing, including Google’s announcement that it had created a “time crystal” — a new form of matter — in a quantum computer. The truth, however, is that today’s quantum computers have limitations. Which is why Norman Yao, a molecular physicist at the University of California at Berkeley, states, “Time crystals are like a rest stop on the road to building a [better (i.e., more functional)] quantum computer.” Journalist Dalvin Brown (@dalvin_brown) reports, “Time crystals are scientific oddities made of atoms arranged in a repeating pattern in space. This design enables them to shift shape over time without losing energy or overheating. Since time crystals continuously evolve and don’t seem to require much energy input, they may be useful for quantum computers, which rely on extremely fragile qubits that are prone to decay. Quantum computing is weighed down by hard-to-control qubits, which are error prone and often die. Time crystals might introduce a better method for sustaining quantum computing, according to Yao, who published a blueprint for making time crystals in 2017.” Even though scientists remain in the early stages of developing a more functional quantum computer, futurists are already thinking about how useful they will be in the supply chain sector.
What are Quantum Computers?
Unlike traditional computers where a bit is either a one or a zero, a quantum bit (or qubit) can simultaneously be both a one and a zero. Like many things found at the quantum level, qubits defy logic. The weirdness continues with the fact that a quantum particle can simultaneously appear to be in two places at once. This phenomenon — called entanglement — involves a pair of quantum particles linked together in a such a way that when one particle is altered its twin is instantaneously altered in exactly the same way regardless of how far apart the entangled particles may be. Professor Albert Einstein famously called entanglement “spooky action at a distance.” Because a qubit can simultaneously be both a 0 and a 1, quantum machines compute differently than traditional machines. James Norman explains, “Quantum computers can be game changers because they can solve important problems no existing computer can. While conventional computing scales linearly, QC scales exponentially when adding new bits. Exponential scaling always wins, and it’s never close.” To learn more about quantum computers, watch the following video.
Eric Limer insists, “Someday, somehow, quantum computing is going to change the world as we know it. Even the lamest quantum computer is orders of magnitude more powerful than anything we could ever make today. But figuring out how to program one is ridiculously hard.”
How Can Quantum Computers Improve Logistics?
Almost weekly, scientists are making breakthroughs that are paving the way to more functional quantum computers — including how to program them. As a result, more people are beginning to consider how a quantum computer could be used to improve logistics. Robert Liscouski, President and CEO of Quantum Computing Inc., explains, “World events ranging from the Suez Canal blockage to the global COVID-19 pandemic have shown how susceptible our supply chain management and logistics systems are to changes in consumer and business demand, raw materials availability, shipping, and distribution. The field of constrained optimization is well matched to address these needs, yet today’s classical computers can hit a wall amidst growing volumes of data and unpredictable disruptions. New software solutions will combine the power of classical and quantum computing to help planners stay ahead.” If you are not familiar with “constrained optimization,” Liscouski explains that constrained optimization is a field of mathematics that addresses problems in which “optimizing a function’s variables (e.g., trucks, SKUs, people)” are important, but optimized solutions must take “into account their constraints (e.g., cost, volume, time), for better business decision-making and efficiency.”
In a subsequent article, Liscouski notes, “The goal of a supply chain organization is to meet customer requirements while minimizing total supply chain costs. Businesses must be flexible enough to respond quickly when disruptions occur.” In the area of logistics, he notes, being agile isn’t easy. This is especially true, he states, when it comes to last mile logistics. He explains, “The last mile grows even more complex. The last mile has always been the most expensive, long-bemoaned challenge of the supply chain. With the ‘new normal’ of changing consumption habits and channels creating unpredictable demand, forecasts have become meaningless. This makes agility and speed to optimization that much more important to meet customers’ growing expectations for instant availability and near-immediate delivery.” To demonstrate why quantum computers could help logistics optimization, Liscouski provides an example:
“Many of us have heard of the traveling salesman problem, which can be compared to truck routing and how to optimize the routes, as well as the trucks. The challenge is that traveling salesman problems like this grow in complexity by n! (n factorial). Routing problems are more constrained and complex for every variable (truck, route, driver, etc.) that you add. For example, a traveling salesman problem that has 10 stops results in 3,628,800 route options, 40 stops will result in approximately 40! = 815,915,283,2 00,000,000,000,000,000,000,000,000,000,000,000,000 options. Routing multiple trucks and packages is even more complex.”
As a result, Liscouski writes, “A classical computer would struggle under the weight and scale of a vast set of possibilities. This is where quantum computers promise to take on the task to quickly produce options to choose from to make the best decision based on your goals. Complicated scenarios meant to solve for multiple variables are not achievable by a classical computing algorithm in a short span of time. However, algorithms using quantum computing techniques can quickly achieve this simulation using a classical system applying quantum techniques, or a hybrid solution that employs both quantum and classical, today.”
Liscouski notes that both Accenture and IDC insist quantum computing will benefit supply chain optimization efforts. He reports that Accenture concludes, “Route-optimization algorithms are helping reduce mileage and improve on-time delivery rates. In logistics, quantum routing uses cloud-based, quantum computing to calculate the fastest route for all vehicles, taking into account millions of real-time data points about traffic congestion.” And IDC research concludes, “The ability to ingest broad and deep data sets to inform better decision making will be the single largest differentiator of supply chain performance in the future.” Although it comes as no surprise that Liscouski, whose company offers quantum software, is sanguine about quantum computing’s future in the supply chain, I agree with his conclusion: “Quantum computing is one of the most promising technological innovations likely to shape, streamline and optimize the future of the supply chain. It offers better insights to make better decisions. That’s why there’s so much excitement about it.”
 To learn more, read Stephen DeAngelis, “Google and the Quantum Time Crystal,” Enterra Insights, 13 August 2021.
 Dalvin Brown, “Google’s new ‘time crystals’ could be a breakthrough for long-awaited quantum computers,” The Washington Post, 12 August 2021.
 James Norman, “Quantum Computing Will Revolutionize Data Analysis. Maybe Soon,” Seeking Alpha, 14 March 2018.
 Eric Limer, “Why Programming a Quantum Computer Is So Damn Hard,” Gizmodo, 23 August 2013]
 Robert Liscouski, “Quantum Computing: A New Solution for Supply Chain and Logistics Optimization,” Material Handling & Logistics, 4 August 2021.
 Robert Liscouski, “How Quantum Computing Will Power the Future of Logistics,” SupplyChainBrain, 8 August 2021.


Three scientists who have made seminal contributions to the experimental study of quantum entanglement and its applications share the Nobel Prize in Physics in 2022. Scientists John Clauser of the United States and Alain Aspect of France devised a method to definitively detect entanglement between photons. Quantum communication relies on entanglement, which was first successfully transmitted by Anton Zeilinger of the University of Vienna.
The technologies of the future include quantum computing and quantum communication. Because they allow for rapid resolution of difficult problems and the use of “unbreakable” encrypted data. Particles like photons, ions, and atoms act under quantum physical phenomena like superposition and entanglement. Due to these occurrences, quantum computers can process vast amounts of data in a short amount of time, and quantum signals can be “teleported” almost instantly.
The mystery of “spooky action at distance”
Quantum entanglement has been described as “spooky action at a distance” by Albert Einstein and as the most crucial aspect of quantum physics by Erwin Schrödinger. Up until the measurement of the state of one of the entangled particles, the other remains in a superposition state, not knowing which of the two it is. Only then does the second one decide on its state simultaneously.
All current quantum technologies are reliant on the observation of quantum entanglement.
One analogy for quantum entanglement is that of two balls, one white and one black, whose superposition in midair renders them gray. The ultimate color of each ball is revealed only when one of them is captured. Simultaneously, it becomes obvious that the second ball is the opposite color. However, this raises the issue of how the balls determine which color they need to take on. Are their colors coincidental or do they potentially contain information that foretells the color they’ll show up in advance?
Physicist John Stewart Bell suggested a theoretical potential in the 1960s for empirically clarifying this issue. According to this, a real entanglement without hidden variables would have to exhibit a specific degree of correlation when the measurements are repeated numerous times. But how to assess this in a realistic manner remained uncertain.
John Clauser and Alain Aspect: The Bell test becomes practical
The first prize winner of the 2022 Nobel Prize in Physics was the American physicist John Clauser for his work in this area. For the first time, he devised an experiment to prove that quantum entanglement is really possible and that Bell’s inequality could be broken. The scientist accomplished this by generating polarization-entangled pairs of photons. Clauser found out how frequently each combination happened by passing these photons through various polarization filters.
As a result, it was clear that the entangled photons did disprove Bell’s inequality. There was no way to predict or account for the strength of the relationships. Instead, it was a “spooky action at distance” effect in which the measurement of one particle determines the state of another, nullifying the superposition.
Clauser and his team’s experiment was exceedingly inefficient, however, since only a tiny percentage of the created photons were traceable through the filters and hence measurable. French physicist Alain Aspect, who came in second for the 2022 Physics Nobel Prize, decided to interfere here. He refined the experiment by separating the entangled photons and measuring them after they passed through two polarizers.
Anton Zeilinger: Quantum teleportation and quantum amplification
When sending optical information over long distances, for example via a fiber-optic cable, the light signal degrades, limiting the range; this is the issue that Anton Zeilinger of the University of Vienna addressed, and it is strongly connected to quantum entanglement. Over a distance of 6 miles (10 kilometers), about one photon is lost per second. Standard optical transmissions include intermediate amplifiers that account for this.
Unfortunately, this cannot be done with entangled photons; the amplifier’s need to read out the signal before boosting it would destroy the quantum signal by canceling the entanglement. In 1998, Zeilinger and his group solved the problem using quantum teleportation. This stems from the discovery that one entangled pair of photons may impart that entanglement to another.
As a result, all a quantum amplifier has to do to transport the entanglement and the quantum information it carries from one pair of photons to another is to guarantee that the two pairs make contact with each other under the correct conditions. This finding paves the way for the use of fiber optic cables to carry quantum communications across significant distances. Photons from the sun have also been “entangled” by scientists.
Early adopters of quantum technology
The three physicists who shared the 2022 Nobel Prize in Physics have thereby provided the groundwork for the eventual practicality of quantum technology. Their research on entangled states is groundbreaking. The Nobel Foundation explains that this is because “their results have cleared the way for new technology based upon quantum information.”


Quantum computing (QC) leverages quantum mechanics to enable a vastly different mode of computation than computers based on classical physics, including conventional von Neumann systems. A quantum bit (qubit), like a classical bit, takes a binary 0 or 1 value when measured, usually at the end of a quantum computation. However, the value of a qubit is not deterministic. A quantum state of n interacting qubits is parameterized by 2n complex numbers, which are called amplitudes and cannot be accessed directly; measuring such a state produces a single random n-bit classical string with probability dictated by a corresponding amplitude.
A powerful feature of quantum computation is that manipulating n qubits allows users to sample from an exponentially larger probability distribution over 2n outcomes. However, an analogous claim can be made for randomized classical algorithms operating on n probabilistic bits (e.g., flipping n coins). A key difference between the two is that quantum algorithms seem to be able to sample from certain kinds of probability distributions that may take exponentially longer for randomized classical algorithms to mimic.
For example, Shor’s seminal 25-year-old quantum algorithm for factoring integers requires exponentially fewer steps than the best-known classical counterparts. Exponential quantum advantages are also known for other fundamental scientific problems such as solving a certain kind of linear systems of equations and simulating quantum-mechanical systems, currently a critical bottleneck in many physical and chemical applications. The precise source of quantum computational advantage is not well understood; however, it is attributed in part to quantum computation’s ability to efficiently generate entanglement among qubits, yielding probability distributions with correlations that in some cases overstep the reach of efficient classical algorithms.
Successes in designing theoretical quantum algorithms have fueled the hope that other quantum advantages can be discovered and exploited. Ideal quantum advantages would provide: (i) an exponential (or at least super-polynomial) computational speedup, (ii) practical applications, and (iii) implementation on a physically realizable quantum system (ideally scalable). A foremost open question in quantum computing is whether all three of these can be simultaneously achieved. A significant hurdle for (iii) is that prepared quantum states are fragile and highly susceptible to environmental noise and rapid entropic decay. Contemporary quantum information science (QIS) research addresses (i) and (ii) by developing novel quantum algorithms and applications and (iii) through scientific and engineering efforts to develop noise-resilient and scalable quantum infrastructure.
After decades of steady progress, mainly in academia, the past five years have seen an explosion of interest and effort in QIS. The fifteen years of QC research at Sandia spans the Labs’ expertise from theoretical computer science and physics to microelectronic fabrication, laboratory demonstrations, and systems engineering. Hardware platforms developed at Sandia include a variety of efforts in trapped-ion, neutral atom, and semiconductor spin qubits. Complementary theoretical efforts have created unique capabilities, from quantum characterization verification and validation protocols to multi-scale qubit device modeling tools. Even efforts that are ostensibly purely theoretical, such as quantum algorithms development, are tied to applications of interest ranging from optimization and machine learning to materials simulation The breadth of current Sandia research activities coupled with the longevity of Sandia’s program have established Sandia as a leading U.S. National Laboratory in QC and broader QIS research.
Most recently, Sandia has been successful in securing a number of quantum computing projects funded by the recent push from DOE Office of Science and the National Nuclear Security Administration. Among these projects, closest to the hardware, are the Advanced Scientific Computing Research (ASCR)-funded Quantum Scientific Open User Testbed (QSCOUT) and Quantum Performance Assessment (QPerformance) projects. In just over a year, the first edition of the QSCOUT testbed with three trapped-ion qubits was stood up. While this will be increased to thirty-two qubits in time, the testbed is most significant for providing researchers complete access to generation of the control signals that specify how gates are operated so they can further investigate the quantum computer itself. A critical component of this effort is the Sandia-developed Jaqal quantum assembly language which will be used to specify programs executed on QSCOUT. The QPerformance project is aimed at creating techniques for evaluating every aspect of a testbed QC’s performance and understanding and tracking how these change with improvements to the QC hardware and software. The effort isn’t limited to the QSCOUT testbed and it will invent and deploy platform-independent holistic benchmarks that will capture high-level characteristics that will be predictive in evaluating the suitability of QC platforms for DOE mission-relevant applications.
At the next level of the computing hierarchy sits the ASCR-funded “Optimization, verification and engineered reliability of quantum computers” (OVER-QC). Led by Sandia, this project aims to develop tools that get the most out of near-term QC hardware, which will be noisy and imperfect. By developing specialized techniques to interpret the output, and to increase the reliability of such noisy hardware, OVER-QC aims to understand and push the limits of QC hardware.
Sandia complements these efforts driven by near-term QC hardware with ASCR-funded efforts focusing on developing fundamental hardware-agnostic quantum algorithms for future fault-tolerant quantum computers. These Sandia-led projects, “Quantum Optimization and Learning and Simulation” (QOALAS) and “Fundamental Algorithmic Research for Quantum Computing” (FAR-QC), are multi-institutional interdisciplinary efforts leveraging world-class computer science, physics, and applied mathematics expertise at Sandia and more than ten partner institutions. QOALAS seeks to develop novel quantum algorithms enabling new applications in optimization, machine learning, and quantum simulation. FAR-QC expands upon the scope of QOALAS to identify problems and domains in which quantum resources may offer significant advantages over classical counterparts. Some of the achievements of these projects include new quantum algorithms offering significant advantages for solving linear systems, convex optimization, machine learning kernels, and rigorous simulation of physical systems.
Among the key mission priorities of Sandia are those related to stockpile stewardship. The Advance Simulation and Computing (ASC)-funded Gate-Based Quantum Computing (GBQC) project is focused on understanding the prospects for QC platforms to eventually have significant impacts on the unique problems of stockpile stewardship. In this context, quantum simulation is a key capability.
Sandia’s stockpile stewardship mission requires models for the behavior of materials in extreme conditions that are both challenging and expensive to evaluate experimentally. GBQC is focused on understanding what will be required to realize a simulation capability that would be exceptionally impactful to ASC and the broader DOE. Recent research directions have broadened the scope of this work to understand the impacts that QCs might have on numerical linear algebra, which is a key capability for not only ASC applications, but most computational science.
Sandia has spent fifteen years developing a strong program in QIS and QC to better serve DOE and NNSA customers. As a result, Sandia is poised to be a leader in the fields of QIS and QC research, while integrating capabilities across the whole QC stack.


As we look toward the next decade and ask ourselves “What is the future of education?”, we can only know one thing for sure… nothing is certain. However, it is clear that technology will continue to play a significant role in shaping the ways we learn and teach. One area that appears destined to see significant growth and evolution is that of virtual learning and online education.
In recent years, there has been a notable increase in the number of educational institutions offering online courses, and this trend is expected to continue. With the widespread availability of high-speed internet and affordable computing devices, students now have greater access to educational content from any location… but this is just the start. So what does this mean for the future, and what core pillars of technology will play a role?
Let’s start with virtual campuses — online learning environments that allow students and teachers to communicate and interact in a 3D space, in real-time, in a similar way to how they would in a physical environment. This is our core focus at Axon Park, to enable remote learning in collaborative, social, 3D worlds where students and educators can feel a shared sense of presence, no matter where they are in the physical world.
One of the major advantages of virtual campuses is their flexibility. Students have the freedom to learn from anywhere, while institutions can significantly reduce their overhead. 3D virtual learning environments also offer significant engagement and interactivity benefits, which can arguably go above and beyond the physical world. For example, you could take a virtual field trip to the center of the earth, or to a subatomic scale. Try doing that in Kansas, Dorthy!
Virtual classrooms are not just a convenient alternative to in-person learning; they have the potential to revolutionize the way we teach and learn.
The use of immersive virtual reality (VR) technology has the potential to transform the way in which we experience and interact with educational content. Imagine being able to visit ancient Rome or explore the depths of the ocean, all from the comfort of your own home.
In addition to providing immersive learning experiences, VR can also be used to simulate real-world scenarios and allow students to practice and apply their knowledge in a safe and controlled environment. For example, a student studying biology could use VR to dissect a virtual frog, or a student learning about engineering could use VR to design and build virtual rocket engines. This type of experiential learning can be especially beneficial for students who may not have access to real-world opportunities to practice and apply their knowledge. On top of this, the interactive nature of VR environments with head, hand, eye, and other types of tracking, provide significantly more learner interaction data than any prior digital platform. This allows for robust real-time analytics which can be used to support the learner and provide insights to educators.
VR technology has the ability to make learning more immersive, engaging, and interactive, and it’s likely that we will see a greater incorporation of this technology into educational settings in the coming years.
AI Personalized Learning
On top of the advancements in virtual campus based learning and VR, it is also anticipated that there will be a greater focus on personalized learning in the future. This approach involves the use of artificial intelligence (AI) and other technologies to assess a student’s strengths and weaknesses, and provide customized learning experiences accordingly. This can be accomplished through the use of adaptive learning algorithms, which are able to analyze a student’s performance on quizzes, exams, and other assessments and adjust the content and difficulty level of future lessons accordingly. As we saw in the section above, the rich flow of data from interactive VR simulations can be a powerful feed for the AI.
In addition to adapting content to a student’s individual abilities, AI can also be used to provide personalized feedback and support. For example, an AI tutor could analyze a student’s work and provide specific recommendations for improvement, or suggest additional resources for further study. This type of personalized support can be especially beneficial for students who may be struggling with certain concepts or who need extra help to keep up with their peers.
When we look into the distant future, we should also consider how quantum computing has the potential to revolutionize the way we process and analyze information. By utilizing the principles of quantum mechanics, quantum computers are able to perform certain calculations much, much, faster than traditional binary computers. This has the potential to significantly impact the field of education, as it could allow for the development of new educational tools and technologies that are able to compute and analyze massive amounts of data in real-time.
Quantum simulations may be able to replicate the workings of physical reality in ways that are impossible for the binary computers of today. For example, a quantum computer could be used to simulate protein folding, then provide scientists with extremely relevant and nuanced real-time feedback. Overall, while the potential applications of quantum computing in education are still largely unexplored, it is clear that this technology has the potential to significantly impact the way we learn and teach in the distant future.
In conclusion, the future of education can’t be predicted with certainty. However, it is clear that technology will continue to have a major impact on the way in which we learn and teach. From virtual campuses and VR to AI personalized learning, the next 20 years are sure to bring exciting developments and innovations in the field of education.


Two milliseconds – or two thousandths of a second – is an extraordinarily long time in the world of quantum computing.
On these time scales, a blink of an eye – to a 10th of a second – is like an eternity.
Now a team of researchers from UNSW Sydney have broken new ground by proving that ‘spin qubits’ – properties of electrons representing the basic units of information in quantum computers – can retain information for up to two milliseconds . Known as “coherence time,” the amount of time qubits can be manipulated into increasingly complicated calculations, the achievement is 100 times longer than previous benchmarks in the same quantum processor.
“Longer coherence time means you have more time for your quantum information to be stored – which is exactly what you need when performing quantum operations,” says PhD student Ms Amanda Seedhouse, whose work in theoretical quantum computing have contributed to the realization.
“Coherence time basically tells you how long you can perform all the operations in the algorithm or sequence you want to perform before you lose all of your qubit information.”
In quantum computing, the longer you can keep spins moving, the better the chance that information can be retained during calculations. When the spin qubits stop spinning, the computation collapses and the values represented by each qubit are lost. The concept of coherence extension has already been confirmed experimentally by quantum engineers from UNSW in 2016.
Making the task even more difficult is the fact that working quantum computers of the future will need to track the values of millions of qubits if they are to solve some of humanity’s greatest challenges, such as finding effective vaccines, modeling systems weather and predict the impacts of climate change.
Late last year, the same team at UNSW Sydney solved a technical problem that had stuck engineers for decades on how to manipulate millions of qubits without generating more heat and interference. Rather than adding thousands of tiny antennas to control millions of electrons with magnetic waves, the research team found a way to use a single antenna to control all of the chip’s qubits by introducing a crystal called dielectric resonator. These results were published in Scientists progress.
This solved the problem of space, heat, and noise that would inevitably increase as more and more qubits come online to perform the mind-bending calculations that are possible when qubits represent not just 1 or 0 like conventional binary computers, but both at the same time. , using a phenomenon known as quantum superposition.
Global control vs individual control
However, this proof-of-concept achievement still left some challenges to be resolved. Lead researcher Ms Ingvild Hansen joined Ms Seedhouse in addressing these questions in a series of journal articles Physical examination B, Physical examination A and Applied physics exams – the last article published this week.
Being able to control millions of qubits with a single antenna was a big step forward. But while controlling millions of qubits at once is a great feat, working quantum computers will also need them to be manipulated individually. If all spin qubits spin at roughly the same frequency, they will have the same values. How can we control them individually so that they can represent different values in a calculation?
“We first showed theoretically that we can improve coherence time by continuously spinning qubits,” says Hansen.
“If you imagine a circus performer spinning plates, while they are still spinning, the show can go on. Similarly, if we continuously drive qubits, they can hold information longer. We have shown that such “dressed” qubits have coherence times of more than 230 microseconds [230 millionths of a second].”
After the team showed that coherence times could be extended with so-called “dressed” qubits, the next challenge was to make the protocol more robust and show that globally controlled electrons can also be individually controlled so that they may contain different necessary values. for complex calculations.
This was achieved by creating what the team dubbed the “SMART” qubit protocol – Sinusoidally Modulated, Always Rotating and Tailored.
Rather than spinning the qubits in circles, they manipulated them to rock back and forth like a metronome. Then, if an electric field is individually applied to any qubit – bringing it out of resonance – it can be put into a different tempo from its neighbors, but still moving at the same rate.
“Think of it like two kids on a seesaw moving forward and backward pretty much in sync,” says Ms Seedhouse. “If we nudge one of them, we can get them to reach the end of their arc at opposite ends, so one can be a 0 when the other is now a 1. .”
The result is that not only can a qubit be controlled individually (electronically) under the influence of a global control (magnetically), but the coherence time is, as mentioned before, significantly longer and suitable for quantum calculations.
“We have shown a simple and elegant way to control all qubits at once, which also comes with better performance,” says Dr. Henry Yang, one of the team’s lead researchers.
“The SMART protocol will be a potential route for large-scale quantum computers.”
The research team is led by Professor Andrew Dzurak, CEO and founder of Diraq, a UNSW spin-off company that develops quantum computing processors that can be fabricated using standard silicon chip fabrication.
“Our next goal is to show it works with two-qubit computations after showing our proof of concept in our experimental paper with one qubit,” says Hansen.
“After that, we want to show that we can do it for a handful of qubits as well, to show that the theory is proven in practice.”
#long #time #Quantum #computing #engineers #setting #standard #silicon #chip #performance


The current artificial intelligence (AI) systems are regulated by other existing regulations such as data protection, consumer protection and market competition laws.
It is critical for governments, leaders, and decision makers to develop a firm understanding of the fundamental differences between artificial intelligence, machine learning, and deep learning.
Artificial intelligence (AI) applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, and decision trees. AI recognizes patterns from vast amounts of quality data providing insights, predicting outcomes, and making complex decisions.
Machine learning (ML) is a subset of AI that utilises advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa and Apple’s Siri improve every year thanks to constant use by consumers coupled with the machine learning that takes place in the background.
Deep learning (DL) is a subset of machine learning that uses advanced algorithms to enable an AI system to train itself to perform tasks by exposing multilayered neural networks to vast amounts of data. It then uses what it learns to recognize new patterns contained in the data. Learning can be human-supervised learning, unsupervised learning, and/or reinforcement learning, like Google used with DeepMind to learn how to beat humans at the game Go.
State of Artificial Intelligence in the Pandemic Era
Artificial intelligence (AI) is stepping up in more concrete ways in blockchain, education, internet of things, quantum computing, arm race and vaccine development.
During the Covid-19 pandemic, we have seen AI become increasingly pivotal to breakthroughs in everything from drug discovery to mission critical infrastructure like electricity grids.
AI-first approaches have taken biology by storm with faster simulations of humans’ cellular machinery (proteins and RNA). This has the potential to transform drug discovery and healthcare.
Transformers have emerged as a general purpose architecture for machine learning, beating the state of the art in many domains including natural language planning (NLP), computer vision, and even protein structure prediction.
AI is now an actual arms race rather than a figurative one.
Organizations must learn from the mistakes made with the internet, and prepare for a safer AI.
Artificial intelligence deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment.
There are 3 stages of artificial intelligence:
1. Artificial Narrow Intelligence (ANI), which has a limited range of capabilities. As an example: AlphaGo, IBM’s Watson, virtual assistants like Siri, disease mapping and prediction tools, self-driving cars, machine learning models like recommendation systems and deep learning translation.
2. Artificial General Intelligence (AGI), which has attributes that are on par with human capabilities. This level hasn’t been achieved yet.
3. Artificial Super Intelligence (ASI), which has skills that surpass humans and can make them obsolete. This level hasn’t been achieved yet.
Why Governments Need to Regulate Artificial Intelligence?
We need to regulate artificial intelligence for two reasons.
First, because governments and companies use AI to make decisions that can have a significant impact on our lives. For example, algorithms that calculate school performance can have a devastating effect.
Second, because whenever someone takes a decision that affects us, they have to be accountable to us. Human rights law sets out minimum standards of treatment that everyone can expect. It gives everyone the right to a remedy where those standards are not met, and you suffer harm.
Is There An International Artificial Intelligence Law?
As of today, there is no international artificial intelligence law nor specific legislation designed to regulate its use. However, progress has been made as bills have been passed to regulate certain specific AI systems and frameworks.
Artificial intelligence has changed rapidly over the last few decades. It has made our lives so much easier and saves us valuable time to complete other tasks.
AI must be regulated to protect the positive progress of the technology. Legislators across the globe have to this day failed to design laws that specifically regulate the use of artificial intelligence. This allows profit-oriented companies to develop systems that may cause harm to individuals and to the broader society.
National and International Artificial Intelligence Regulations
National and local governments have started adopting strategies and working on new laws for a number of years, but no legislation has been passed yet.
China for example has developed in 2017 a strategy to become the world’s leader in AI in 2030. In the US, the White House issued ten principles for the regulation of AI. They include the promotion of “reliable, robust and trustworthy AI applications”, public participation and scientific integrity. International bodies that give advice to governments, such as the OECD or the World Economic Forum, have developed ethical guidelines.
The Council of Europe created a Committee dedicated to help develop a legal framework on AI. The most ambitious proposal yet comes from the EU. On 21 April 2021, the EU Commission put forward a proposal for a new AI Act.
Ethical Concerns of Artificial Intelligence
Police forces across the EU deploy facial recognition technologies and predictive policing systems. These systems are inevitably biased and thus perpetuate discrimination and inequality.
Crime prediction and recidivism risk are a second AI application fraught with legal problems. A ProPublica investigation into an algorithm-based criminal risk assessment tool found the formula more likely to flag black defendants as future criminals, labelling them at twice the rate as white defendants, and white defendants were mislabeled as low-risk more often than black defendants. We need to think about the way we are mass producing decisions and processing people, particularly low income and low-status individuals, through automation and their consequences for society.
How to Regulate Artificial Intelligence the Right Way
An effective, rights-protecting AI regulation must, at a minimum, contain the following safeguards. First, artificial intelligence regulation must prohibit use cases, which violate fundamental rights, such as biometric mass surveillance or predictive policing systems. The prohibition should not contain exceptions that allow corporations or public authorities to use them “under certain conditions”.
Second, there must be clear rules setting out exactly what organizations have to make public about their products and services. Companies must provide a detailed description of the AI system itself. This includes information on the data it uses, the development process, the systems’ purpose and where and by whom it is used. It is also key that individuals exposed to AI are informed about it, for example in the case of hiring algorithms. Systems that can have a significant impact on people’s lives should face extra scrutiny and feature in a publicly accessible database. This would make it easier for researchers and journalists to make sure companies and governments are protecting our freedoms properly.
Third, individuals and organisations protecting consumers need to be able to hold governments and corporations responsible when there are problems. Existing rules on accountability must be adapted to recognise that decisions are made by an algorithm and not by the user. This could mean putting the company that developed the algorithm under an obligation to check the data with which algorithms are trained and the decisions algorithms make so they can correct problems.
Fourth, new regulations must make sure that there is a regulator that can make companies and the authorities accountable and that they are following the rules properly. This watchdog should be independent and have the resources and powers it needs to do its job.
Finally, AI regulation should also contain safeguards to protect the most vulnerable. It should set up a system that allows people who have been harmed by AI systems to make a complaint and get compensation. Workers should have the right to take action against invasive AI systems used by their employer without fear of retaliation.
A trustworthy artificial intelligence should respect all applicable laws and regulations, as well as a series of requirements; specific assessment lists aim to help verify the application of each of the key requirements:
Human agency and oversight: AI systems should enable equitable societies by supporting human agency and fundamental rights, and not decrease, limit or misguide human autonomy.
Robustness and safety: Trustworthy AI requires algorithms to be secure, reliable and robust enough to deal with errors or inconsistencies during all life cycle phases of AI systems.
Privacy and data governance: Citizens should have full control over their own data, while data concerning them will not be used to harm or discriminate against them.
Transparency: The traceability of AI systems should be ensured.
Diversity, non-discrimination and fairness: AI systems should consider the whole range of human abilities, skills and requirements, and ensure accessibility.
Societal and environmental well-being: AI systems should be used to enhance positive social change and enhance sustainability and ecological responsibility.
Accountability: Mechanisms should be put in place to ensure responsibility and accountability for AI systems and their outcomes.


Quantum technologies are the way of the future, but will that future ever arrive?
Maybe so. Physicists have cleared a bit more of the path to a plausible quantum future by constructing an elementary network for exchanging and storing quantum information. The network features two all-purpose nodes that can send, receive and store quantum information, linked by a fiber-optic cable that carries it from one node to another on a single photon.
The network is only a prototype, but if it can be refined and scaled up, it could form the basis of communication channels for relaying quantum information. A group from the Max Planck Institute of Quantum Optics (M.P.Q.) in Garching, Germany, described the advance in the April 12 issue of Nature. (Scientific American is part of Nature Publishing Group.)
Quantum bits, or qubits, are at the heart of quantum information technologies. An ordinary, classical bit in everyday electronics can store one of two values: a 0 or a 1. But thanks to the indeterminacy inherent to quantum mechanics, a qubit can be in a so-called superposition, hovering undecided between 0 and 1, which adds a layer of complexity to the information it carries. Quantum computers would boast capabilities beyond the reach of even the most powerful classical supercomputers, and cryptography protocols based on the exchange of qubits would be more secure than traditional encryption methods.
Physicists have used all manner of quantum objects to store qubits—electrons, atomic nuclei, photons and so on. In the new demonstration, the qubit at each node of the network is stored in the internal quantum state of a single rubidium atom trapped in a reflective optical cavity. The atom can then transmit its stored information via an optical fiber by emitting a single photon, whose polarization state carries the mark of its parent atom's quantum state; conversely, the atom can absorb a photon from the fiber and take on the quantum state imprinted on that photon's polarization.
Because each node can perform a variety of functions—sending, receiving or storing quantum information—a network based on atoms in optical cavities could be scaled up simply by connecting more all-purpose nodes. "We try to build a system where the network node is universal," says M.P.Q. physicist Stephan Ritter, one of the study's authors. "It's not only capable of sending or receiving—ideally, it would do all of the things you could imagine." The individual pieces of such a system had been demonstrated—atoms sending quantum information on single emitted photons, say—but now the technologies are sufficiently advanced that they can work as an ensemble. "This has now all come together and enabled us to realize this elementary version of a quantum network," Ritter says.
Physicists proposed using optical cavities for quantum networks 15 years ago, because they marry the best features of atomic qubits and photonic qubits—namely that atoms stay put, making them an ideal storage medium, whereas photons are speedy, making them an ideal message carrier between stationary nodes. But getting the photons and atoms to communicate with one another has been a challenge. "If you want to use single atoms and single photons, as we do, they hardly interact," Ritter adds.
That is where the optical cavity comes in. The mirrors of the cavity reflect a photon past the rubidium atom tens of thousands of times, boosting the chances of an interaction. "During this time, there's enough time to really do this information exchange in a reliable way," Ritter says. "The cavity enhances the coupling between the light field and the atom."
The M.P.Q. group put their prototype network through a series of tests—transferring a qubit from a single photon to a single atom and reversing the process to transfer information from an atom onto a photon. Combining those read/write operations, the physicists managed to transmit a qubit from one rubidium atom to another located in a separate laboratory 21 meters away, using a messenger photon as the carrier between nodes. (The actual length of optical fiber connecting the two nodes is 60 meters, because it snakes along an indirect route.)
A significant number of the photons get lost along the way, limiting the efficiency of the process. But in principle, optical fibers could connect nodes at greater distances. "We're absolutely not limited to these 21 meters," Ritter says. "This 21 meters is just the distance that we happened to have between the two labs."
The researchers also demonstrated that their photonic link can be used to entangle the two distant atoms. Quantum entanglement is a phenomenon by which two particles share correlated properties—in other words, the quantum state of one particle depends on the state of its entangled partner. Manipulating one of the particles, then, affects the other particle's state, even if it is located in another laboratory. Researchers hope that entanglement can be harnessed to circumvent the photon losses that come from passage through optical fibers. In a proposed application called a quantum repeater, a series of nodes, linked by entanglement, would extend the quantum connection down the line without depending on any one photon as the carrier.
Ritter acknowledges that the new work is simply a prototype, and one for which numerous improvements are possible. For instance, the transfer of a quantum state between labs succeeded only 0.2 percent of the time, owing to various inefficiencies and technical limitations. "Everything is at the edge of what can be done," he says. "All these characteristics are good enough to do what we've done, but there are clear strategies to pursue to make them even better."


Condensed matter physics – the branch of physics responsible for discovering and describing most of these phases – has traditionally classified phases by the way their fundamental building blocks – usually atoms – are arranged. The key is something called symmetry.
To understand symmetry, imagine flying through liquid water in an impossibly tiny ship: the atoms would swirl randomly around you and every direction – whether up, down, or sideways – would be the same. The technical term for this is "symmetry" – and liquids are highly symmetric. Crystal ice, another phase of water, is less symmetric. If you flew through ice in the same way, you would see the straight rows of crystalline structures passing as regularly as the girders of an unfinished skyscraper. Certain angles would give you different views. Certain paths would be blocked, others wide open. Ice has many symmetries – every "floor" and every "room" would look the same, for instance – but physicists would say that the high symmetry of liquid water is broken.
Classifying the phases of matter by describing their symmetries and where and how those symmetries break is known as the Landau paradigm. More than simply a way of arranging the phases of matter into a chart, Landau’s theory is a powerful tool which both guides scientists in discovering new phases of matter and helps them grapple with the behaviours of the known phases. Physicists were so pleased with Landau’s theory that for a long time they believed that all phases of matter could be described by symmetries. That’s why it was such an eye-opening experience when they discovered a handful of phases that Landau couldn’t describe.
Beginning in the 1980s, condensed matter researchers, including Xiao-Gang Wen – now a faculty member at Perimeter Institute – investigated new quantum systems where numerous ground states existed with the same symmetry. Wen pointed out that those new states contain a new kind of order: topological order. Topological order is a quantum mechanical phenomenon: it is not related to the symmetry of the ground state, but instead to the global properties of the ground state’s wave function. Therefore, it transcends the Landau paradigm, which is based on classical physics concepts.
Topological order is a more general understanding of quantum phases and the transitions between them. In the new framework, the phases of matter were described not by the patterns of symmetry in the ground state, but by the patterns of a decidedly quantum property – entanglement. When two particles are entangled, certain measurements performed on one of them immediately affect the other, no matter how far apart the particles are. The patterns of such quantum effects, unlike the patterns of the atomic positions, could not be described by their symmetries. If you were to describe a city as a topologically ordered state from the cockpit of your impossibly tiny ship, you’d no longer be describing the girders and buildings of the crystals you passed, but rather invisible connections between them – rather like describing a city based on the information flow in its telephone system.
This more general description of matter developed by Wen and collaborators was powerful – but there were still a few phases that didn’t fit. Specifically, there were a set of short-range entangled phases that did not break the symmetry, the so-called symmetry-protected topological phases. Examples of symmetry-protected phases include some topological superconductors and topological insulators, which are of widespread immediate interest because they show promise for use in the coming first generation of quantum electronics. In the paper featured in today’s issue of Science, Wen and collaborators reveal a new system which can, at last, successfully classify these symmetry-protected phases. Using modern mathematics – specifically group cohomology theory and group super-cohomology theory – the researchers have constructed and classified the symmetry-protected phases in any number of dimensions and for any symmetries. Their new classification system will provide insight about these quantum phases of matter, which may in turn increase our ability to design states of matter for use in superconductors or quantum computers.
This paper is a revealing look at the intricate and fascinating world of quantum entanglement, and an important step toward a modern reclassification of all phases of matter.
- Read the paper in Science
- The current issue of Nature provides experimental confirmation of the existence of quantum spin liquids, one of the new states of matter that was theoretically predicted by Wen and collaborators
- Wen’s essay on the connections between condensed matter physics and cosmology
- An introduction to understanding phases of matter based on symmetry
About Xiao-Gang Wen
Regarded as one of the world’s leading condensed matter theorists, Xiao-Gang Wen holds the BMO Financial Group Isaac Newton Chair at Perimeter Institute for Theoretical Physics. The BMO/Newton Chair was established by a $4 million gift from the BMO Financial Group in 2010 and, in 2011, Wen joined Perimeter from MIT as its inaugural occupant. Read a lay-accessible overview of his research.


A Nov. 5, 2013 Vienna University of Technology press release (also available on EurekAlert) describes research that may make quantum optical switches possible,
With just a single atom, light can be switched between two fibre optic cables at the Vienna University of Technology. Such a switch enables quantum phenomena to be used for information and communication technology.
The press release goes on to describe a ‘light in a bottle’ technique which leads, the researchers hope, that they may have discovered how to create a quantum light switch,
Professor Arno Rauschenbeutel and his team at the Vienna University of Technology capture light in so-called “bottle resonators”. At the surface of these bulgy glass objects, light runs in circles. If such a resonator is brought into the vicinity of a glass fibre which is carrying light, the two systems couple and light can cross over from the glass fibre into the bottle resonator.
“When the circumference of the resonator matches the wavelength of the light, we can make one hundred percent of the light from the glass fibre go into the bottle resonator – and from there it can move on into a second glass fibre”, explains Arno Rauschenbeutel.
A Rubidium Atom as a Light Switch
This system, consisting of the incoming fibre, the resonator and the outgoing fibre, is extremely sensitive: “When we take a single Rubidium atom and bring it into contact with the resonator, the behaviour of the system can change dramatically”, says Rauschenbeutel. If the light is in resonance with the atom, it is even possible to keep all the light in the original glass fibre, and none of it transfers to the bottle resonator and the outgoing glass fibre. The atom thus acts as a switch which redirects light one or the other fibre.
Both Settings at Once: The Quantum Switch
In the next step, the scientists plan to make use of the fact that the Rubidium atom can occupy different quantum states, only one of which interacts with the resonator. If the atom occupies the non-interacting quantum state, the light behaves as if the atom was not there. Thus, depending on the quantum state of the atom, light is sent into either of the two glass fibres. This opens up the possibility to exploit some of the most remarkable properties of quantum mechanics: “In quantum physics, objects can occupy different states at the same time”, says Arno Rauschenbeutel. The atom can be prepared in such a way that it occupies both switch states at once. As a consequence, the states “light” and “no light” are simultaneously present in each of the two glass fibre cables. [emphasis mine]
For the classical light switch at home, this would be plain impossible, but for a “quantum light switch”, occupying both states at once is not a problem. “It will be exciting to test, whether such superpositions are also possible with stronger light pulses. Somewhere we are bound to encounter a crossover between quantum physics and classical physics”, says Rauschenbeutel.
This light switch is a very powerful new tool for quantum information and quantum communication. “We are planning to deterministically create quantum entanglement between light and matter”, says Arno Rauschenbeutel. “For that, we will no longer need any exotic machinery which is only found in laboratories. Instead, we can now do it with conventional glass fibre cables which are available everywhere.”
Darrick Chang offers a good introduction (i.e., it’s challenging but you don’t need a physics degree to read it) and some analysis of this work in his Nov. 4, 2013 article for Physics (6, 121 (2013) DOI: 10.1103/Physics.6.121) titled: Viewpoint: A Single-Atom Optical Switch.
Quantum scientists over the past two decades have dreamt of realizing powerful new information technologies that exploit the laws of quantum mechanics in their operation. While many approaches are being pursued, a prevailing choice consists of using single atoms and particles of light—single photons—as the fundamental building blocks of these technologies . In this paradigm, one envisions that single atoms naturally act as quantum processors that produce and interface with single photons, while the photons naturally act as wires to carry information between processors. Reporting in Physical Review Letters, researchers at the Vienna University of Technology, Austria, have taken an important step forward in this pursuit, by experimentally demonstrating a microphotonic optical switch that is regulated by just a single atom .
This article is open access.
For those willing to tackle a more challenging paper, here’s a link to and a citation for the Vienna University of Technology researchers’ paper,
Fiber-Optical Switch Controlled by a Single Atom by Danny O’Shea, Christian Junge, Jürgen Volz, and Arno Rauschenbeute. Phys. Rev. Lett. 111, 193601 (2013) [5 pages]
This work is behind a paywall.
Minutes after publishing: here’s an image that illustrates superpositioning in a quantum switch,


Nanoscale cavity strongly links quantum particles
Scientists have created a crystal structure that boosts the interaction between tiny bursts of light and individual electrons, an advance that could be a significant step toward establishing quantum networks in the future.
Today’s networks use electronic circuits to store information and optical fibers to carry it, and quantum networks may benefit from a similar framework. Such networks would transmit qubits – quantum versions of ordinary bits – from place to place and would offer unbreakable security for the transmitted information. But researchers must first develop ways for qubits that are better at storing information to interact with individual packets of light called photons that are better at transporting it, a task achieved in conventional networks by electro-optic modulators that use electronic signals to modulate properties of light.
Now, researchers in the group of Edo Waks, a fellow at JQI and an Associate Professor in the Department of Electrical and Computer Engineering at the University of Maryland, have struck upon an interface between photons and single electrons that makes progress toward such a device. By pinning a photon and an electron together in a small space, the electron can quickly change the quantum properties of the photon and vice versa. The research was reported online Feb. 8 in the journal Nature Nanotechnology.
“Our platform has two major advantages over previous work,” says Shuo Sun, a graduate student at JQI and the first author of the paper. “The first is that the electronic qubit is integrated on a chip, which makes the approach very scalable. The second is that the interactions between light and matter are fast. They happen in only a trillionth of a second – 1,000 times faster than previous studies.”
CONSTRUCTING AN INTERFACE
The new interface utilizes a well-studied structure known as a photonic crystal to guide and trap light. These crystals are built from microscopic assemblies of thin semiconductor layers and a grid of carefully drilled holes. By choosing the size and location of the holes, researchers can control the properties of the light traveling through the crystal, even creating a small cavity where photons can get trapped and bounce around.
”These photonic crystals can concentrate light in an extremely small volume, allowing devices to operate at the fundamental quantum limit where a single photon can make a big difference,” says Waks.
The results also rely on previous studies of how small, engineered nanocrystals called quantum dots can manipulate light. These tiny regions behave as artificial atoms and can also trap electrons in a tight space. Prior work from the JQI group showed that quantum dots could alter the properties of many photons and rapidly switch the direction of a beam of light.
The new experiment combines the light-trapping of photonic crystals with the electron-trapping of quantum dots. The group used a photonic crystal punctuated by holes just 72 nanometers wide, but left three holes undrilled in one region of the crystal. This created a defect in the regular grid of holes that acted like a cavity, and only those photons with only a certain energy could enter and leave.
Inside this cavity, embedded in layers of semiconductors, a quantum dot held one electron. The spin of that electron – a quantum property of the particle that is analogous to the motion of a spinning top – controlled what happened to photons injected into the cavity by a laser. If the spin pointed up, a photon entered the cavity and left it unchanged. But when the spin pointed down, any photon that entered the cavity came out with a reversed polarization – the direction that light’s electric field points. The interaction worked the opposite way, too: A single photon prepared with a certain polarization could flip the electron’s spin.
Both processes are examples of quantum switches, which modify the qubits stored by the electron and photon in a controlled way. Such switches will be the coin of the realm for proposed future quantum computers and quantum networks.
Those networks could take advantage of the strengths that photons and electrons offer as qubits. In the future, for instance, electrons could be used to store and process quantum information at one location, while photons could shuttle that information between different parts of the network.
Such links could enable the distribution of entanglement, the enigmatic connection that groups of distantly separated qubits can share. And that entanglement could enable other tasks, such as performing distributed quantum computations, teleporting qubits over great distances or establishing secret keys that two parties could use to communicate securely.
Before that, though, Sun says that the light-matter interface that he and his colleagues have created must create entanglement between the electron and photon qubits, a process that will require more accurate measurements to definitively demonstrate.
“The ultimate goal will be integrating photon creation and routing onto the chip itself,” Sun says. “In that manner we might be able to create more complicated quantum devices and quantum circuits.”
In addition to Waks and Sun, the paper has two additional co-authors: Glenn Solomon, a JQI fellow, and Hyochul Kim, a post-doctoral researcher in the Department of Electrical and Computer Engineering at the University of Maryland.
"Creating a quantum switch" credit: S. Kelley/JQI
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Quantum computing is one of the current big things in both physics and computer science circles. But there is a serious divide between what we think might be possible and what we can, in fact, do. There are theorists out there working themselves into a frenzy, trying to show that quantum computing will make a smoother latte. On the experimental side, many researchers are still in various stages of single gate operations. It is like the difference between trying to make a valve and knowing what you can do with lots of valves once you have them.
In a recent paper, published in Applied Physics Letters, researchers from the UK and Australia have demonstrated that quantum computing gates with very low error rates, based on integrated optical circuits, are now feasible. This might pave the way for multi-gate optical quantum computers.
Quantum computing is, as the name might suggest, a merger between classical digital computers and the quantum freakiness that permeates the world around us at the smallest scales. In a classical computer, a bit can have two values: logic one and logic zero. When we perform operations on a string of bits, we either leave them unchanged or flip them, depending on some control bits. It is important to realize that the value of a bit at any particular time does not depend on any of its partner bits.
If we add a dash of quantumness to the mix, we can do two things. First, logic elements, now qubits, are no longer logic one or logic zero; instead, they are both at the same time. When we read out the result from a program, we obtain a definite one or zero, but during the computation, the qubit really is in both states. Operations don't necessarily flip bits. Instead, they modify the probability of a measurement returning a one or a zero. The second element added to the mix is correlations between qubits. When we perform an operation on one qubit in a string of them, we are actually performing an operation on all the qubits.
There are good and bad aspects to this. A quantum computer doesn't always return the right answer, but some operations, like factoring or database searches, can be sped up. Not returning the right answer comes from two factors. There is an intrinsic uncertainty associated with measurement—it's the price we pay for being in a quantum universe. There are also instrumental imperfections, which, at the moment, play a major role in limiting quantum computing.
This is where Laing and colleagues come in. They focused on the construction of near perfect circuity. In the case of optical quantum computing logic, this corresponds to making perfect beam splitters and interferometers.
These aren't the normal optics you might find in a microscope, which makes things both easier and more difficult. For instance, in a waveguide, a beam splitter is replaced by a directional coupler, where two waveguides are brought into close proximity. Over a certain length, light from one waveguide will leak into the adjacent waveguide. The amount of light that transfers depends on how close the two waveguides are and the distance they remain close. So, in principle, it is very easy to design a perfect beam splitter. In practice, fabrication uncertainty makes this a bit of a lottery—the usual procedure is to make quite a few, test them all, and pick the good one to report on.
Interferometers are similar, in that they involve splitting and recombining light beams. However, in addition to requiring two perfect beam splitters for the interferometer, one also needs to carefully control how far the light must travel between the two. In other words, the fabrication tolerances on the two different light paths are quite tight.
However, once you have these two elements, you can make a controlled NOT gate—a gate that inverts the quantum state of one qubit, depending on the state of the controlling qubit—which is a logic element from which all other logic elements can be constructed. That is exactly what this paper demonstrates. They show that they have very low loss waveguides, and that they can make beam splitters with a splitting ratio within a couple percent of their design ratio.
To illustrate this, they showed data obtained from the quantum interference between single photons passing through their beam splitter. The error bars on the data are tiny, so within the uncertainty of their measurements, they have a perfect instrument.
Likewise, Laing and colleagues show a controlled NOT gate that gets it right 97 percent of the time. "Right" being a relative thing here—this is the fidelity, which means it takes into account the fact that quantum measurements have a finite chance of getting the wrong answer irrespective of the quality of the equipment. From this, they calculate that, at worst, they have an error rate between one part in 100 and one part in 1000. The latter figure is probably good enough to start thinking about multiple gate operations.
As you can see, I'm not reporting on anything startling here, just a good solid bit of technology that is necessary for optical quantum computers to do anything useful. I do wonder, however, how many of the circuit elements on the wafer were functional, because that is probably the limiting factor now. One thing missing in all optical implementations of quantum computers is programmability, because that involves switching light paths around. In integrated optic implementations, like this one, switches could be fast, and if the losses are low enough, programmability might well be on the horizon.
The bigger problem on the horizon is multi-qubit calculations. To perform a calculation represented by a register of eight qubits, every one of those qubits has to be entangled with every other qubit, and that ain't easy.
Applied Physics Letters, 2010, DOI: 10.1063/1.3497087


But it's a little more complex than this. We also have quantum mechanics to contend with. The spin of an electron is a vector. But we find that when we measure one of the components of this vector this value is quantised and can only take values +hbar/2 and -hbar/2, where hbar is Planck's constant. We choose units where h-bar is 1 so the z-component of the spin is always measured to be +1/2 or -1/2. If we write these two states as |+> and |-> then because we are dealing with quantum mechanics, the z-component of the spin can be represented by the linear combination a|+>+b|->. This corresponds to a state in which there is a probability |a|² of measuring +1/2 and a probability |b|² of measuring -1/2. This is what might have been written as a.*return (1/2)+b.*return (-1/2) in my earlier Haskell code. But that's just one component of the spin. What about the x- and y-components? Amazingly the state a|+>+b|-> tells us everything we can possibly know about the spin of an electron and we'll call it a spin state.
Suppose we have an electron in the state ψ = a|+>+b|->. What happens if we measure the y-component of its spin? One way to answer that question is to rotate the electron through π/2 so that its x-axis is rotated to align with the z-axis and then measure the z-component of its spin. In order to to that we need to know how to rotate spin states. The rule for rotation through θ about the x-axis is this (in a suitable coordinate frame):
|+> → cos(θ/2)|+>-sin(θ/2)|->
|-> → sin(θ/2)|+>+cos(θ/2)|->
Note how choosing θ=0 gives the identity, as expected. Note also that θ=π maps a|+>+b|-> to b|+>-a|-> so that the probabilities of measuring +1/2 and -1/2 are simply swapped, exactly what you'd expect for turning a state upside down. But there's something else that you should notice - there's an ambiguity. A rotation through 2π should give the same as a rotation through 0 and yet setting θ=2π in that transformation maps a state ψ to -ψ. Now |a|² = |-a|² so the probability of observing spin up or spin down is unaffected. But as I've been showing over previous posts, flipping a sign in a state can make a big difference as soon as you start performing interference experiments. The same goes for any angle: if I rotate through π should I use θ=π or θ = 3π? So can the transformation I've given make sense?
The transformation does make sense if you consider that in any physical process that rotates an electron the transformation will evolve continuously over time. Electrons don't just instantly rotate. In other words, if a rotation is applied to an electron then it will follow a path in SO(3), not just be an instantaneous application of an element of SO(3). And that allows us to resolve the ambiguity: the rotations of electrons are described by the double cover of SO(3) known as SU(2). So a rotation through 360 degrees doesn't return you to the identity although a 720 degree rotation does. The transformation I gave above is completely unambiguous if you continuously rotate an electron around the x-axis tracking a continuous value of θ, after all, the double cover is basically just the set of continuous paths from the identitiy in SO(3) (with homotopic paths considered equivalent).
And that's the bizarre fact: electron rotations aren't described by SO(3), they're described by SU(2). In particular, rotating an electron through 360 degrees does not return it to its original state, but a rotation through 720 degrees does! In a sense, like Dirac's belt, electrons can remember something about the path they took to get where they are, in particular they remember how many twists there were in the path.
What does this mean experimentally? the first thing to note is that this is true not just for electrons but any spin-1/2 fermion. This included protons and neutrons. The stuff I've been talking about manifests itself in a number of ways. In particular, the spin of a particle affects how a magnetic field acts on it. For example, spin-up and spin-down particles can be separated into distinct beams using Stern-Gerlach apparatus. Also, the spin of particles precesses in a magnetic field and this is used on a regular basis in NMR. These two facts allow us to easily manipulate and measure the spin of fermions. In other words, the fact that fermions remember how many twists there are in their rotations isn't just some esoteric nonsense, it's now engineering and the theory is tested repeatedly all over the world.
Every familiar object is invariant under rotations through 360 degrees. So the fact that electrons need to be rotated through 720 degrees to return them to their original state seems like one of the most bizarre facts about the universe I know of. And yet many books that introduce spin just slip in this fact in a routine way as if it were no different to any other.
The fact that the biggest connected cover of SO(3) is the double cover puts a big constraint on the kinds of weird effects like this can happen. We can have a 360 degree rotation multiply by -1, but not by i, because a 720 degree rotation absolutely has to return us to where we started from. But suppose the universe were 2-dimensional. If you remember what I said about SO(2) you may notice that no such constraints apply because SO(2) has an infinite cover. There is a group in which all of the rotations through 360n degrees are distinct for distinct n. This means that a physical system could have its state multiplied by any factor (of modulus 1) when rotated through 360 degrees. Particle that behave this way are called anyons. But we live in a 3D universe so we don't expect any fundamental particles to have this property. However, in quantum mechanics any kind of 'excitation' of a physical system is quantised and can be thought of as a type of particle. These are known as quasiparticles. For example, just as light is made of photons, sound is also quantised as phonons. In the right kind of solid state medium, especially those that arise from some kind of 2D lattice, it seems quite plausible that anyons might arise. This gives rise to the so called fractional quantum hall effect. Anyons might one day play an important role in quantum computing via topological quantum computation.