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Chemistry is quantum computing’s killer app

Katherine Bourzac

Published Oct 30, 2017• 8 citations
Publisher

Abstract

In an IBM lab in Yorktown Heights, N.Y., a circuit board sits at the bottom of a large tub cooled to temperatures colder than those of outer space. Shiny, solderlike zigzags of niobium cover the board, connecting 16 unusual computing elements. These elements, called quantum bits, or qubits, may form the basis of future computers that will solve problems by taking advantage of quantum mechanics. So far, quantum computers like IBM’s 16-qubit system can solve only simple problems, all of them easy enough to be handled by traditional computers. This spring, IBM set a quantum-computing record by modeling beryllium hydride—which contains a mere three atoms (Nature 2017, DOI: 10.1038/nature23879). Earlier prototypes modeled hydrogen and factored the number 15. As quantum computers become more sophisticated, they will allow chemists to solve problems that are intractable on today’s desktops and even supercomputers. In fact, physicists expect that chemistry simulations will be quantum

DOI
10.1021/cen-09543-cover

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In an IBM lab in Yorktown Heights, N.Y., a circuit board sits at the bottom of a large tub cooled to temperatures colder than those of outer space. Shiny, solderlike zigzags of niobium cover the board, connecting 16 unusual computing elements. These elements, called quantum bits, or qubits, may form the basis of future computers that will solve problems by taking advantage of quantum mechanics. So far, quantum computers like IBM’s 16-qubit system can solve only simple problems, all of them easy enough to be handled by traditional computers. This spring, IBM set a quantum-computing record by modeling beryllium hydride—which contains a mere three atoms (Nature 2017, DOI: 10.1038/nature23879). Earlier prototypes modeled hydrogen and factored the number 15. As quantum computers become more sophisticated, they will allow chemists to solve problems that are intractable on today’s desktops and even supercomputers. In fact, physicists expect that chemistry simulations will be quantum
Quantum Computing often described as one of the most exciting frontiers of modern science—a field that does not just push the boundaries of technology but also reshapes how we think about solving problems. Unlike classical computers, which process information in simple 0s and 1s, quantum mechanics, allowing a single qubit to explore multiple possibilities at the same time. This gives them the potential to perform calculations that would take even today’s most powerful supercomputers thousands of years. In recent years, progress has been rapid, with breakthroughs in hardware, algorithms and applications that range from developing new medicines to strengthening artificial intelligence and tackling climate change. Yet, enormous challenges remain, such as stabilizing fragile qubits, reducing error rates, and making these systems accessible beyond a handful of elite labs. This paper explores both the promises and the pitfalls of quantum computing, offering a closer look at how it could reshape industries, global security and even the pace of human innovation in the decades ahead.
We have ubiquitous presence of computers today, ranging from simple controllers in modern appliances to smartphones in our pockets that provide a wide range of everyday services, to powerful supercomputers and large data centers that carry out the most computationally intensive tasks. These computational machines have a few things in common: for example, the information they handle is stored in bits (0 or 1), and the procedure for processing the information is specified by a program. A great deal is known about the limits of what such computational machines can and cannot do efficiently. There are many important computational problems that are believed to be very difficult to solve using even the most powerful computers, where the resource requirement-whether it is the size of the machine or the time it takes to finish the task-increases exponentially as a function of the problem size.
T he S cience of C ryogenics B S J Nick Catrini Have you ever wondered how NASA launched their monumental space shuttles? Or perhaps you have heard of the scientists who can slow the speed of light to almost zero? Or about Walt Disney’s attempt at cryogenic stasis, where he waits to be awoken when a cure for lung cancer is developed? Unfortunately, Walt Disney being put into cryogenic stasis is a rumor and after his death he was cremated-but all three of these ideas have their roots in the field of cryogenics. Cryogenics is the art of achieving extremely low temperatures and observing phenomena that don’t normally occur in the chaotic, energy rich universe we experience from day to day. People have been freezing things for ages, but the age has come where there’s a demand for colder and colder temperatures, to the point where even the vacuum of outer space is a blazing inferno by comparison to what’s needed in the lab today. Simply put, the colder you get to absolute zero, the easier it is to observe the true quantum nature of atoms, where generalizations and models made for atoms at room temperature begin to break down, and what’s actually going on takes a different route. The ability to find and exploit these differences could lead to breakthrough technologies such as room temperature superconductors, quantum computers, improvements on existing technologies, and it could also answer questions about the origin and nature of the universe itself. Liquid nitrogen is cold, space is colder, but what scientists need is in nanokelvin, billionths of a degree. One historic study that used ultracold temperatures focused on slowing the speed of light to seventeen meters per second in “If quantum mechanics hasn’t profoundly shocked you, you haven’t understood it yet.” Niels Bohr an ultracold atomic gas. The method of getting of the gas to be ultracold was the focal point of the study, since the colder it would get, the slower light would travel through it. The scientists reached a whopping 435 nanokelvin, but in a surprising way to the unfamiliar layman (Hau, 1999). In order to slow light down to seventeen meters per second, lasers were used to cool the atoms down to 50 microkelvin, and then a magnetic field was applied to the cloud of cold atoms to filter out any atoms that do not have magnetic dipoles against the field, creating an effect similar to a coffee cup cooling off, where the improperly aligned atoms steamed off and out of the magnetic field, or cup, and the remaining aligned ones were the only ones left (Marangos, 1999). Now, nanokelvin temperatures are great, but in some cases even nanokelvin is too hot for simulating some phenomena (Campbell, 2011). Again, atomic gases are cooled with lasers put into arrays called optical lattices that “trap” atoms and prevent them from moving, lowering the temperature. Now, why are scientists studying this again? As Niels Bohr once said, “If quantum mechanics hasn’t profoundly shocked you, you haven’t understood it yet.” For one, the mere attempt to use advances in science and technology to tackle hard problems can lead to various discoveries in the process, such as the Bose-Einstein condensate or the slowing of light to 17 meters per second. But now, is the cost worth it? Slowing light promotes stronger light-matter interaction, which can benefit devices that clean up distortion and noise over long distance data transmission through optical fibers (Krauss, 2008). Last but not least, one of the most important discoveries in all of cryogenics’ time is nuclear magnetic resonance imaging over scanning. That handful of words is better known as an MRI or PET scan you’d get at a hospital. MRI is revolutionary since it performs non-invasive imaging of anatomical structures with no known adverse biological effects, it can determine data not available by other techniques, and it increases the specificity of a diagnosis (Partain, 1984). Research in these cooler temperatures today covers areas such as quantum magnetism and high temperature superconductivity, and the working memory needed for a quantum computer. The words “quantum computer” may sound very enticing, like some sort of science fiction mystery magic machine capable of solving any problem and running day to day tasks exponentially faster than even the most expensive supercomputer, but the reality is a stark contrast. In fact, the quantum computer got its roots in NMR-based technology (Ladd, 2010). A quantum computer is special because unlike a classical computer that has “bits” of information, a 1 or a 0, a quantum computer uses “qubits” which can be 1, 0, or anything in between. Qubits are particularly useful for a select set of problems, usually anything dealing with many 6 • B erkeley S cientific J ournal • E xtremes • F all 2014 • V olume 19 • I ssue 1
In principle, it is possible to model any physical system exactly using quantum mechanics; in practice, it quickly becomes infeasible. Recognising this, Richard Feynman suggested that quantum systems be used to model quantum problems [1]. For example, the fundamental problem faced in quantum chemistry is the calculation of molecular properties, which are of practical importance in fields ranging from materials science to biochemistry. Within chemical precision, the total energy of a molecule as well as most other properties, can be calculated by solving the Schrödinger equation. However, the computational resources required to obtain exact solutions on a conventional computer generally increase exponentially with the number of atoms involved [1, 2]. In the late 1990’s an efficient algorithm was proposed to enable a quantum processor to calculate molecular energies using resources that increase only polynomially in the molecular size [2–4]. Despite the many different physical architectures that have been explored experimentally since that time—including ions, atoms, superconducting circuits, and photons—this appealing algorithm has not been demonstrated to date.