Google announced a 72-qubit universal quantum computer that promises the same low error rates the company saw in its first 9-qubit quantum computer. Google believes that this quantum computer, called Bristlecone, will be able to bring us to an age of quantum supremacy.
There’s another quantum computer to keep track of in this Wild West era of quantum computing research we’re in. And it uses some parts you might already be familiar with.Researchers from two teams now working with Intel have reported advances in a new quantum computing architecture, called spin qubits, in a pair of papers out today. They’re obviously not the full-purpose quantum computers of the future. But they’ve got a major selling point over other quantum computing designs.“We made these qubits in silicon chips, similar to what’s used in classical computer processes,” study author Thomas Watson from TU Delft in the Netherlands told me. “The hope is that by doing things this way, we can potentially scale up to larger numbers needed to perform useful quantum computing.”
Quantum computing has been a major research topic for multiple companies the past few years, with D-Wave, IBM, and Intel all launching their own systems and improving them at a fairly rapid clip. At CES 2018 this week, Intel’s CEO, Brian Krzanich, declared the company’s new 49-qubit quantum computer represented a step towards “quantum supremacy.”A 49 qubit system is a major advance for Intel, which just demonstrated a 17-qubit system two months ago. Intel’s working with the Netherlands-based Qutech on this project, and expanding the number of qubits is key to creating quantum computers that can deliver real-world results.When Intel launched its 17-qubit system in October, it wrote: “Qubits are tremendously fragile: Any noise or unintended observation of them can cause data loss. This fragility requires them to operate at about 20 millikelvin – 250 times colder than deep space.” This is also why we won’t be seeing quantum computers in anyone’s house at any point. While early classical computers were also room-sized and required commensurate cooling solutions, there’s no known way to build hand-sized or even desktop quantum machines that incorporate the cooling required.
Future quantum computers will be able to solve problems where conventional computers fail today. We are still far away from any large-scale implementation, however, because quantum systems are very sensitive to environmental noise. Although systems can be protected from noise in principle, researchers have been able to build only small prototypes of quantum computers experimentally. One way to reduce the error rate is by encoding quantum information not in one single quantum particle but in several quantum objects. These logical quantum bits or qubits are more robust against noise. In the last few years, theoretical physicists have developed a whole range of error correction codes and optimized them for specific tasks. Physicists Hendrik Poulsen Nautrup and Hans Briegel from the Institute of Theoretical Physics of the University of Innsbruck and Nicolai Friis, now at the Institute of Quantum Optics and Quantum Information in Vienna, have found a technique to transfer quantum information between systems that are encoded differently.
RMIT University researchers have trialled a quantum processor capable of routing quantum information from different locations in a critical breakthrough for quantum computing.
The work opens a pathway towards the "quantum data bus", a vital component of future quantum technologies.
The research team from the Quantum Photonics Laboratory at RMIT in Melbourne, Australia, the Institute for Photonics and Nanotechnologies of the CNR in Italy and the South University of Science and Technology of China, have demonstrated for the first time the perfect state transfer of an entangled quantum bit (qubit) on an integrated photonic device.
Quantum Photonics Laboratory Director Dr Alberto Peruzzo said after more than a decade of global research in the specialised area, the RMIT results were highly anticipated.
"The perfect state transfer has emerged as a promising technique for data routing in large-scale quantum computers," Peruzzo said.
John Martinis used the arm of his reading glasses to indicate the spot where he intends to demonstrate an almost unimaginably powerful new form of computer in a few years. It is a cylindrical socket an inch and a half across, at the bottom of a torso-sized stack of plates, blocks, and wires of brass, copper, and gold. The day after I met with him this fall, he loaded the socket with an experimental superconducting chip etched with a microscopic Google logo and cooled the apparatus to a hundredth of a degree Celsius above absolute zero. To celebrate that first day of testing the machine, Martinis threw what he called “a little party” at a brewpub with colleagues from his newly outfitted Google lab in Santa Barbara, California.
That party was nothing compared with the celebration that will take place if Martinis and his group can actually create the wonder computer they seek. Because it would harness the strange properties of quantum physics that arise in extreme conditions like those on the ultracold chip, the new computer would let a Google coder run calculations in a coffee break that would take a supercomputer of today millions of years. The software that Google has developed on ordinary computers to drive cars or answer questions could become vastly more intelligent. And earlier-stage ideas bubbling up at Google and its parent company, such as robots that can serve as emergency responders or software that can converse at a human level, might become real.
In retrospect, I should’ve been suspicious, when more than a year went by with no major D-Wave announcements that everyone wanted me to react to immediately. Could it really be that this debate was over—or not “over,” but where it always should’ve been, in the hands of experts who might disagree vehemently but are always careful to qualify speedup claims—thereby freeing up the erstwhile Chief D-Wave Skeptic for more “rewarding” projects, like charting a middle path through the Internet’s endless social justice wars?
Nope.
During the last two years, the Google Quantum AI team has made progress in understanding the physics governing quantum annealers. We recently applied these new insights to construct proof-of-principle optimization problems and programmed these into the D-Wave 2X quantum annealer that Google operates jointly with NASA. The problems were designed to demonstrate that quantum annealing can offer runtime advantages for hard optimization problems characterized by rugged energy landscapes.
We found that for problem instances involving nearly 1000 binary variables, quantum annealing significantly outperforms its classical counterpart, simulated annealing. It is more than 108 times faster than simulated annealing running on a single core. We also compared the quantum hardware to another algorithm called Quantum Monte Carlo. This is a method designed to emulate the behavior of quantum systems, but it runs on conventional processors. While the scaling with size between these two methods is comparable, they are again separated by a large factor sometimes as high as 10^8.
What is the Computational Value of Finite Range Tunneling?
Authors:
Denchev et al
Abstract:
Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling can provide considerable computational advantage. For a crafted problem designed to have tall and narrow energy barriers separating local minima, the D-Wave 2X quantum annealer achieves significant runtime advantages relative to Simulated Annealing (SA). For instances with 945 variables this results in a time-to-99\%-success-probability that is ∼108 times faster than SA running on a single processor core. We also compared physical QA with Quantum Monte Carlo (QMC), an algorithm that emulates quantum tunneling on classical processors. We observe a substantial constant overhead against physical QA: D-Wave 2X runs up to ∼108 times faster than an optimized implementation of QMC on a single core. To investigate whether finite range tunneling will also confer an advantage for problems of practical interest, we conduct numerical studies on binary optimization problems that cannot yet be represented on quantum hardware. For random instances of the number partitioning problem, we find numerically that QMC, as well as other algorithms designed to simulate QA, scale better than SA and better than the best known classical algorithms for this problem. We discuss the implications of these findings for the design of next generation quantum annealers.
Physicists at the Technical University of Munich, the Los Alamos National Laboratory and Stanford University (USA) have tracked down semiconductor nanostructure mechanisms that can result in the loss of stored information - and halted the amnesia using an external magnetic field. The new nanostructures comprise common semiconductor materials compatible with standard manufacturing processes.
Quantum bits, qubits for short, are the basic logical elements of quantum information processing (QIP) that may represent the future of computer technology. Since they process problems in a quantum-mechanical manner, such quantum computers might one day solve complex problems much more quickly than currently possible, so the hope of researchers.
Quantum Memristors
Authors:
Pfeiffer et al
Abstract:
Technology based on memristors, resistors with memory whose resistance depends on the history of the crossing charges, has lately enhanced the classical paradigm of computation with neuromorphic architectures. However, in contrast to the known quantized models of passive circuit elements, such as inductors, capacitors or resistors, the design and realization of a quantum memristor is still missing. Here, we introduce the concept of a quantum memristor as a quantum dissipative device, whose decoherence mechanism is controlled by a continuous-measurement feedback scheme, which accounts for the memory. Indeed, we provide numerical simulations showing that memory effects actually persist in the quantum regime. Our quantization method, specifically designed for superconducting circuits, may be extended to other quantum platforms, allowing for memristor-type constructions in different quantum technologies. The proposed quantum memristor is then a building block for neuromorphic quantum computation and quantum simulations of non-Markovian systems.
Australian scientists have designed a 3D silicon chip architecture based on single atom quantum bits, which is compatible with atomic-scale fabrication techniques - providing a blueprint to build a large-scale quantum computer.
Scientists and engineers from the Australian Research Council Centre of Excellence for Quantum Computation and Communication Technology (CQC2T), headquartered at the University of New South Wales (UNSW), are leading the world in the race to develop a scalable quantum computer in silicon - a material well-understood and favoured by the trillion-dollar computing and microelectronics industry.
Within the last several years, considerable progress has been made in developing a quantum computer, which holds the promise of solving problems a lot more efficiently than a classical computer. Physicists are now able to realize the basic building blocks, the quantum bits (qubits) in a laboratory, control them and use them for simple computations. For practical application, a particular class of quantum computers, the so-called adiabatic quantum computer, has recently generated a lot of interest among researchers and industry. It is designed to solve real-world optimization problems conventional computers are not able to tackle. All current approaches for adiabatic quantum computation face the same challenge: The problem is encoded in the interaction between qubits; to encode a generic problem, an all-to-all connectivity is necessary, but the locality of the physical quantum bits limits the available interactions. "The programming language of these systems is the individual interaction between each physical qubit. The possible input is determined by the hardware. This means that all these approaches face a fundamental challenge when trying to build a fully programmable quantum computer," explains Wolfgang Lechner from the Institute for Quantum Optics and Quantum Information (IQOQI) at the Austrian Academy of Sciences in Innsbruck.
The significant advance, by a team at the University of New South Wales (UNSW) in Sydney appears today in the international journal Nature.
"What we have is a game changer," said team leader Andrew Dzurak, Scientia Professor and Director of the Australian National Fabrication Facility at UNSW.
"We've demonstrated a two-qubit logic gate - the central building block of a quantum computer - and, significantly, done it in silicon. Because we use essentially the same device technology as existing computer chips, we believe it will be much easier to manufacture a full-scale processor chip than for any of the leading designs, which rely on more exotic technologies.
"This makes the building of a quantum computer much more feasible, since it is based on the same manufacturing technology as today's computer industry," he added.
The advance represents the final physical component needed to realise the promise of super-powerful silicon quantum computers, which harness the science of the very small - the strange behaviour of subatomic particles - to solve computing challenges that are beyond the reach of even today's fastest supercomputers.
It is an inevitability that cryptographers dread: the arrival of powerful quantum computers that can break the security of the Internet. Although these devices are thought to be a decade or more away, researchers are adamant that preparations must begin now.
Computer-security specialists are meeting in Germany this week to discuss quantum-resistant replacements for today’s cryptographic systems—the protocols used to scramble and protect private information as it traverses the web and other digital networks. Although today’s hackers can, and often do, steal private information by guessing passwords, impersonating authorized users or installing malicious software on computer networks, existing computers are unable to crack standard forms of encryption used to send sensitive data over the Internet.
But on the day that the first large quantum computer comes online, some widespread and crucial encryption methods will be rendered obsolete. Quantum computers exploit laws that govern subatomic particles, so they could easily defeat existing encryption methods.
Benchmarking a quantum annealing processor with the time-to-target metric.
Authors:
King et al
Abstract:
In the evaluation of quantum annealers, metrics based on ground state success rates have two major drawbacks. First, evaluation requires computation time for both quantum and classical processors that grows exponentially with problem size. This makes evaluation itself computationally prohibitive. Second, results are heavily dependent on the effects of analog noise on the quantum processors, which is an engineering issue that complicates the study of the underlying quantum annealing algorithm. We introduce a novel \time-to-target" metric which avoids these two issues by challenging software solvers to match the results obtained by a quantum annealer in a short amount of time. We evaluate D-Wave's latest quantum annealer, the D-Wave 2X system, on an array of problem classes and and that it performs well on several input classes relative to state of the art software solvers running single-threaded on a CPU.
The new quantum-proof version of TLS generates encryption keys using a different mathematical problem that’s believed to be beyond the practical reach of both conventional and quantum computers.
That system was tested by using it to encrypt data moving between two PCs, one taking the role of a Web browser and the other a Web server. The quantum-proof encryption protocol moved data 21 percent more slowly than a version using elliptic curve cryptography, as some websites do today, but the researchers consider that penalty a reasonable one to pay if their idea is polished up for real-world use.
Ari Juels, a professor at Cornell Tech and previously chief scientist at the security company RSA, says it makes sense to prepare our encryption for quantum computers now. Outdated encryption lingering in websites or software already causes security problems, even with the relatively slow progress made on encryption-beating attacks, he says.
However, right now it’s not certain that the math used in Microsoft’s quantum-proof software will always be intractable for either quantum or conventional computers, says Juels. Mathematicians and cryptographers haven’t studied them as intensely as they have RSA or the encryption used today. “We’ve no solid assurance,” he says.
IBM scientists have unveiled two crucial advances toward the creation of a practical quantum computer: an effective way to detect and correct quantum errors, and the design of a silicon chip that can scale up to house a large number of entangled quantum bits.
University of California, Berkeley, scientists have proved a fundamental relationship between energy and time that sets a "quantum speed limit" on processes ranging from quantum computing and tunneling to optical switching.
The energy-time uncertainty relationship is the flip side of the Heisenberg uncertainty principle, which sets limits on how precisely you can measure position and speed, and has been the bedrock of quantum mechanics for nearly 100 years. It has become so well-known that it has infected literature and popular culture with the idea that the act of observing affects what we observe.
Not long after German physicist Werner Heisenberg, one of the pioneers of quantum mechanics, proposed his relationship between position and speed, other scientists deduced that energy and time were related in a similar way, implying limits on the speed with which systems can jump from one energy state to another. The most common application of the energy-time uncertainty relationship has been in understanding the decay of excited states of atoms, where the minimum time it takes for an atom to jump to its ground state and emit light is related to the uncertainty of the energy of the excited state.
"This is the first time the energy-time uncertainty principle has been put on a rigorous basis - our arguments don't appeal to experiment, but come directly from the structure of quantum mechanics," said chemical physicist K. Birgitta Whaley, director of the Berkeley Quantum Information and Computation Center and a UC Berkeley professor of chemistry. "Before, the principle was just kind of thrown into the theory of quantum mechanics."
The new derivation of the energy-time uncertainty has application for any measurement involving time, she said, particularly in estimating the speed with which certain quantum processes - such as calculations in a quantum computer - will occur.
An odd, iridescent material that's puzzled physicists for decades turns out to be an exotic state of matter that could open a new path to quantum computers and other next-generation electronics.
Physicists at the University of Michigan have discovered or confirmed several properties of the compound samarium hexaboride that raise hopes for finding the silicon of the quantum era. They say their results also close the case of how to classify the material--a mystery that has been investigated since the late 1960s.
The researchers provide the first direct evidence that samarium hexaboride, abbreviated SmB6, is a topological insulator. Topological insulators are, to physicists, an exciting class of solids that conduct electricity like a metal across their surface, but block the flow of current like rubber through their interior. They behave in this two-faced way despite that their chemical composition is the same throughout.
The U-M scientists used a technique called torque magnetometry to observe tell-tale oscillations in the material's response to a magnetic field that reveal how electric current moves through it. Their technique also showed that the surface of samarium hexaboride holds rare Dirac electrons, particles with the potential to help researchers overcome one of the biggest hurdles in quantum computing.
These properties are particularly enticing to scientists because SmB6 is considered a strongly correlated material. Its electrons interact more closely with one another than most solids. This helps its interior maintain electricity-blocking behavior.
This deeper understanding of samarium hexaboride raises the possibility that engineers might one day route the flow of electric current in quantum computers like they do on silicon in conventional electronics, said Lu Li, assistant professor of physics in the College of Literature, Science, and the Arts and a co-author of a paper on the findings published in Science.
What does a 1980s experimental aircraft have to do with state-of-the art quantum technology? Lots, as shown by new research from the Quantum Control Laboratory at the University of Sydney, and published in Nature Physics today.
Over several years a team of scientists has taken inspiration from aerospace research and development programs to make unusually shaped experimental aircraft fly.
"It always amazed me that the X-29, an American airplane that was designed like a dart being thrown backwards, was able to fly. Achieving this, in 1984, came through major advances in a discipline called control engineering that were able to stabilise the airplane," said Associate Professor Michael Biercuk, from the School of Physics and director of the Quantum Control Laboratory.
"We became interested in how similar concepts could play a role in bringing quantum technologies to reality. If control engineering can turn an unstable dart into a high-performance fighter jet, it's pretty amazing to think what it can do for next-generation quantum technologies."
The result is that the researchers have been able to turn fragile quantum systems into useful pieces of advanced tech useful for everything from computation and communications to building specialised sensors for industry. The trick was figuring out how to protect them from their environments using control theory.
