Tag Archives: Quantum Computers
#432882 Why the Discovery of Room-Temperature ...
Superconductors are among the most bizarre and exciting materials yet discovered. Counterintuitive quantum-mechanical effects mean that, below a critical temperature, they have zero electrical resistance. This property alone is more than enough to spark the imagination.
A current that could flow forever without losing any energy means transmission of power with virtually no losses in the cables. When renewable energy sources start to dominate the grid and high-voltage transmission across continents becomes important to overcome intermittency, lossless cables will result in substantial savings.
What’s more, a superconducting wire carrying a current that never, ever diminishes would act as a perfect store of electrical energy. Unlike batteries, which degrade over time, if the resistance is truly zero, you could return to the superconductor in a billion years and find that same old current flowing through it. Energy could be captured and stored indefinitely!
With no resistance, a huge current could be passed through the superconducting wire and, in turn, produce magnetic fields of incredible power.
You could use them to levitate trains and produce astonishing accelerations, thereby revolutionizing the transport system. You could use them in power plants—replacing conventional methods which spin turbines in magnetic fields to generate electricity—and in quantum computers as the two-level system required for a “qubit,” in which the zeros and ones are replaced by current flowing clockwise or counterclockwise in a superconductor.
Arthur C. Clarke famously said that any sufficiently advanced technology is indistinguishable from magic; superconductors can certainly seem like magical devices. So, why aren’t they busy remaking the world? There’s a problem—that critical temperature.
For all known materials, it’s hundreds of degrees below freezing. Superconductors also have a critical magnetic field; beyond a certain magnetic field strength, they cease to work. There’s a tradeoff: materials with an intrinsically high critical temperature can also often provide the largest magnetic fields when cooled well below that temperature.
This has meant that superconductor applications so far have been limited to situations where you can afford to cool the components of your system to close to absolute zero: in particle accelerators and experimental nuclear fusion reactors, for example.
But even as some aspects of superconductor technology become mature in limited applications, the search for higher temperature superconductors moves on. Many physicists still believe a room-temperature superconductor could exist. Such a discovery would unleash amazing new technologies.
The Quest for Room-Temperature Superconductors
After Heike Kamerlingh Onnes discovered superconductivity by accident while attempting to prove Lord Kelvin’s theory that resistance would increase with decreasing temperature, theorists scrambled to explain the new property in the hope that understanding it might allow for room-temperature superconductors to be synthesized.
They came up with the BCS theory, which explained some of the properties of superconductors. It also predicted that the dream of technologists, a room-temperature superconductor, could not exist; the maximum temperature for superconductivity according to BCS theory was just 30 K.
Then, in the 1980s, the field changed again with the discovery of unconventional, or high-temperature, superconductivity. “High temperature” is still very cold: the highest temperature for superconductivity achieved was -70°C for hydrogen sulphide at extremely high pressures. For normal pressures, -140°C is near the upper limit. Unfortunately, high-temperature superconductors—which require relatively cheap liquid nitrogen, rather than liquid helium, to cool—are mostly brittle ceramics, which are expensive to form into wires and have limited application.
Given the limitations of high-temperature superconductors, researchers continue to believe there’s a better option awaiting discovery—an incredible new material that checks boxes like superconductivity approaching room temperature, affordability, and practicality.
Tantalizing Clues
Without a detailed theoretical understanding of how this phenomenon occurs—although incremental progress happens all the time—scientists can occasionally feel like they’re taking educated guesses at materials that might be likely candidates. It’s a little like trying to guess a phone number, but with the periodic table of elements instead of digits.
Yet the prospect remains, in the words of one researcher, tantalizing. A Nobel Prize and potentially changing the world of energy and electricity is not bad for a day’s work.
Some research focuses on cuprates, complex crystals that contain layers of copper and oxygen atoms. Doping cuprates with various different elements, such exotic compounds as mercury barium calcium copper oxide, are amongst the best superconductors known today.
Research also continues into some anomalous but unexplained reports that graphite soaked in water can act as a room-temperature superconductor, but there’s no indication that this could be used for technological applications yet.
In early 2017, as part of the ongoing effort to explore the most extreme and exotic forms of matter we can create on Earth, researchers managed to compress hydrogen into a metal.
The pressure required to do this was more than that at the core of the Earth and thousands of times higher than that at the bottom of the ocean. Some researchers in the field, called condensed-matter physics, doubt that metallic hydrogen was produced at all.
It’s considered possible that metallic hydrogen could be a room-temperature superconductor. But getting the samples to stick around long enough for detailed testing has proved tricky, with the diamonds containing the metallic hydrogen suffering a “catastrophic failure” under the pressure.
Superconductivity—or behavior that strongly resembles it—was also observed in yttrium barium copper oxide (YBCO) at room temperature in 2014. The only catch was that this electron transport lasted for a tiny fraction of a second and required the material to be bombarded with pulsed lasers.
Not very practical, you might say, but tantalizing nonetheless.
Other new materials display enticing properties too. The 2016 Nobel Prize in Physics was awarded for the theoretical work that characterizes topological insulators—materials that exhibit similarly strange quantum behaviors. They can be considered perfect insulators for the bulk of the material but extraordinarily good conductors in a thin layer on the surface.
Microsoft is betting on topological insulators as the key component in their attempt at a quantum computer. They’ve also been considered potentially important components in miniaturized circuitry.
A number of remarkable electronic transport properties have also been observed in new, “2D” structures—like graphene, these are materials synthesized to be as thick as a single atom or molecule. And research continues into how we can utilize the superconductors we’ve already discovered; for example, some teams are trying to develop insulating material that prevents superconducting HVDC cable from overheating.
Room-temperature superconductivity remains as elusive and exciting as it has been for over a century. It is unclear whether a room-temperature superconductor can exist, but the discovery of high-temperature superconductors is a promising indicator that unconventional and highly useful quantum effects may be discovered in completely unexpected materials.
Perhaps in the future—through artificial intelligence simulations or the serendipitous discoveries of a 21st century Kamerlingh Onnes—this little piece of magic could move into the realm of reality.
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#431155 What It Will Take for Quantum Computers ...
Quantum computers could give the machine learning algorithms at the heart of modern artificial intelligence a dramatic speed up, but how far off are we? An international group of researchers has outlined the barriers that still need to be overcome.
This year has seen a surge of interest in quantum computing, driven in part by Google’s announcement that it will demonstrate “quantum supremacy” by the end of 2017. That means solving a problem beyond the capabilities of normal computers, which the company predicts will take 49 qubits—the quantum computing equivalent of bits.
As impressive as such a feat would be, the demonstration is likely to be on an esoteric problem that stacks the odds heavily in the quantum processor’s favor, and getting quantum computers to carry out practically useful calculations will take a lot more work.
But these devices hold great promise for solving problems in fields as diverse as cryptography or weather forecasting. One application people are particularly excited about is whether they could be used to supercharge the machine learning algorithms already transforming the modern world.
The potential is summarized in a recent review paper in the journal Nature written by a group of experts from the emerging field of quantum machine learning.
“Classical machine learning methods such as deep neural networks frequently have the feature that they can both recognize statistical patterns in data and produce data that possess the same statistical patterns: they recognize the patterns that they produce,” they write.
“This observation suggests the following hope. If small quantum information processors can produce statistical patterns that are computationally difficult for a classical computer to produce, then perhaps they can also recognize patterns that are equally difficult to recognize classically.”
Because of the way quantum computers work—taking advantage of strange quantum mechanical effects like entanglement and superposition—algorithms running on them should in principle be able to solve problems much faster than the best known classical algorithms, a phenomenon known as quantum speedup.
Designing these algorithms is tricky work, but the authors of the review note that there has been significant progress in recent years. They highlight multiple quantum algorithms exhibiting quantum speedup that could act as subroutines, or building blocks, for quantum machine learning programs.
We still don’t have the hardware to implement these algorithms, but according to the researchers the challenge is a technical one and clear paths to overcoming them exist. More challenging, they say, are four fundamental conceptual problems that could limit the applicability of quantum machine learning.
The first two are the input and output problems. Quantum computers, unsurprisingly, deal with quantum data, but the majority of the problems humans want to solve relate to the classical world. Translating significant amounts of classical data into the quantum systems can take so much time it can cancel out the benefits of the faster processing speeds, and the same is true of reading out the solution at the end.
The input problem could be mitigated to some extent by the development of quantum random access memory (qRAM)—the equivalent to RAM in a conventional computer used to provide the machine with quick access to its working memory. A qRAM can be configured to store classical data but allow the quantum computers to access all that information simultaneously as a superposition, which is required for a variety of quantum algorithms. But the authors note this is still a considerable engineering challenge and may not be sustainable for big data problems.
Closely related to the input/output problem is the costing problem. At present, the authors say very little is known about how many gates—or operations—a quantum machine learning algorithm will require to solve a given problem when operated on real-world devices. It’s expected that on highly complex problems they will offer considerable improvements over classical computers, but it’s not clear how big problems have to be before this becomes apparent.
Finally, whether or when these advantages kick in may be hard to prove, something the authors call the benchmarking problem. Claiming that a quantum algorithm can outperform any classical machine learning approach requires extensive testing against these other techniques that may not be feasible.
They suggest that this could be sidestepped by lowering the standards quantum machine learning algorithms are currently held to. This makes sense, as it doesn’t really matter whether an algorithm is intrinsically faster than all possible classical ones, as long as it’s faster than all the existing ones.
Another way of avoiding some of these problems is to apply these techniques directly to quantum data, the actual states generated by quantum systems and processes. The authors say this is probably the most promising near-term application for quantum machine learning and has the added benefit that any insights can be fed back into the design of better hardware.
“This would enable a virtuous cycle of innovation similar to that which occurred in classical computing, wherein each generation of processors is then leveraged to design the next-generation processors,” they conclude.
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