Introduction
Quantum computing is based on quantum bits or qubits. Unlike traditional computers, in which bits must have a value of either zero or one, a qubit can represent a zero, a one, or both values simultaneously. Representing information in qubits allows the information to be processed in ways that have no equivalent in classical computing, taking advantage of phenomena such as quantum tunneling and quantum entanglement. As such, quantum computers may theoretically be able to solve certain problems in a few days that would take millions of years on a classical computer.
Quantum computers—a possible future technology that would revolutionize computing by harnessing the bizarre properties of quantum bits, or qubits. Qubits are the quantum analogue to the classical computer bits “0” and “1.” Engineering materials that can function as qubits is technically challenging. Using supercomputers, scientists from the University of Chicago and Argonne National Laboratory predicted possible new qubits built out of strained aluminum nitride. Moreover, the scientists showed that certain newly developed qubits in silicon carbide have unusually long lifetimes.
Quantum computers could break common cryptography techniques, search huge datasets, and simulate quantum systems in a fraction of the time it would take today’s computers. However, engineers first need to harness the properties of quantum bits. Engineering new qubits with less difficult methods could lower one of the significant barriers to scaling quantum computers from small prototypes into larger-scale technologies.
One of the leading methods for creating qubits involves exploiting specific structural atomic defects in diamonds. Using diamonds is both technically challenging and expensive. Now researchers from the University of Chicago and Argonne National Laboratory have suggested an analogous defect in aluminum nitride, which could reduce the difficulty and ultimate cost of manufacturing materials for quantum computing applications. Using the Edison and Mira supercomputers at DOE’s National Energy Research Scientific Computing Center and Argonne National Laboratory respectively, the researchers found that by applying strain to aluminum nitride, they can create structural defects in the material that may be harnessed as qubits similar to those seen in diamonds. They performed their calculations using different levels of theory and the Quantum Espresso and WEST codes, the latter developed at the University of Chicago. The codes allowed them to accurately predict the position of the defect levels in the band-gap of semiconductors. The researchers also closely collaborated with experimentalists to understand and improve the performance of qubits in industrial materials. Recently, they showed that newly developed qubits in silicon carbide have much longer coherence times than that of the more well-established defect qubits in diamond. Their results pointed to industrially important polyatomic crystals as promising hosts for coherent qubits for scalable quantum devices.
Source: https://science.energy.gov/ascr/highlights/2017/ascr-2017-01-a/
Peter Shor’s 1994 breakthrough discovery of a polynomial time quantum algorithm for integer factorization sparked great interest in discovering additional quantum algorithms and developing hardware on which to run them. The subsequent research efforts yielded quantum algorithms offering speedups for widely varying problems, and several promising hardware platforms for quantum computation. These platforms include analog systems (usually cold atoms) used for simulating quantum lattice models from condensed-matter and high-energy physics, quantum annealers for combinatorial optimization, boson samplers, and small-scale noisy prototypes of digital gate-model quantum computers.
In the longer term, the emergence of scalable, fault-tolerant, digital quantum computers offers a new direction for progress in high performance computing as conventional technologies reach their fundamental limitations. Quantum speedups have been discovered for a number of areas of DOE interest, including simulations for chemistry, nuclear and particle physics, and materials science, as well as data analysis and machine learning. In addition, quantum speedups have been discovered for basic primitives of applied mathematics such as linear algebra, integration, optimization, and graph theory. These demonstrate the potential of quantum computers to yield better-scaling methods (in some cases exponentially better) for performing a wide variety of scientific computing tasks. Practical realization of this potential will depend not only on advances in quantum computing hardware but also advances in optimizing languages and compilers to translate these abstract algorithms into concrete sequences of realizable quantum gates, and simulators to test and verify these sequences. The development of such software has recently seen rapid progress, which can be expected to continue given sufficient support.
Source: https://science.energy.gov/~/media/ascr/pdf/programdocuments/docs/ASCRQuantumReport-final.pdf
Imagine typing a very complex query into your computer and having to wait more than a lifetime for results. Thanks to scientists like Davide Venturelli, supercomputers of the future could return those results in a fraction of a second. Davide is a quantum computer research scientist for the Universities Space Research Association. Quantum theory explains how matter acts at the tiniest levels; in applying it to computing, researchers study ways in which that behavior can advance processing power. “We explore how to control these quantum behaviors, to make them happen on demand, in order to crunch numbers and process information,” he says. “We’re pushing the boundaries of what is known in computer science.”
Quantum computer research scientists help to solve problems. In their research, they make scientific assumptions based on quantum theory and then conduct experiments to test whether their solutions work. These scientists may be involved in a variety of projects but often focus on a specific goal. Davide focuses on finding new ways of applying quantum theory to improve how computers solve optimization problems—that is, problems for finding the best of all possible solutions. Digital computers, which are most common today, process information using variables with 1 value (either 0 or 1) at a time. Quantum computers can use both values simultaneously, which results in faster processing. “We know that quantum computers are more powerful than digital computers,” he says, “but we don’t know by how much yet.”
Research. In studying information technology, quantum computer research scientists think about possibilities. For example, Davide asks questions in his research such as, “What is the fastest possible way we can make computers process information?” Davide and other research scientists use their understanding of quantum theory to come up with solutions. Their research may lead to problem-solving computer processes that calculate and sort information much faster. For example, research scientists might develop a theoretical solution that can be run only on quantum computers designed to produce better weather forecasts.
Experiments. To test whether their theories work, quantum computer research scientists may conduct experiments or work with experimental physicists. For example, they may create a quantum environment with computer hardware, then test how particles in that environment react to different levels of laser intensity. Experiments that verify a theory may lead to improvements, such as more efficient computer design and faster, more secure communication for computer networks. But relying on theory means that scientists work with incomplete information—so they’re sometimes surprised at the outcomes. “Experiments may result in the opposite of what you expect,” says Davide, “and you analyze the data to try to figure out why.”
To become a quantum computer research scientist, you usually need a doctoral degree (Ph.D.). But you need some qualities and skills in addition to the formal credential. As researchers, quantum computer research scientists should enjoy being part of a team and sharing their findings with others, which may include engineers, mathematicians, physicists, and Ph.D. students. This collaboration helps bring varied perspectives to solving a problem. “There’s a cross-utilization of ideas when you work with different groups,” Davide says. “My colleagues are very smart and open-minded people.”
Like many scientists, quantum computer research scientists must have strong analytical, critical thinking, and reasoning skills to solve complex problems. Attention to detail is critical as scientists precisely record their theories and experiments, which must be reproducible and able to withstand peer review.
Communication skills are also important. To share their research with collaborators or the public, quantum research scientists must be able to write papers and present their findings at conferences. They may also need to write proposals for grants to fund research projects. Quantum computer research scientists usually need a Ph.D. to learn methods of discovery and to develop the tools needed for researching. Coursework in undergraduate and graduate degree programs typically includes computer science, mathematics, and physics.
You may decide to pursue a master’s degree with classes in quantum computing before entering a Ph.D. program. Davide studied physics at the bachelor’s and master’s levels, but he was passionate about computers, too. Not surprisingly, quantum computing piqued his interest. “It’s a wonderful interaction between the two disciplines,” he says. Davide earned his Ph.D. in nanophysics and numerical simulations of condensed matter.
The U.S. Bureau of Labor Statistics (BLS) does not collect data specifically on quantum computer research scientists. Instead, BLS may count these workers among physicists, of which 15,650 were employed in May 2015. The median annual wage for physicists in colleges, universities, and professional schools—where most quantum computer research scientists are likely to work—was $63,840. That’s more than the median annual wage of $36,200 for all workers.
Quantum computer research scientists work primarily indoors, in academic settings, and may travel frequently to attend seminars or conferences. Area of focus or project type may dictate specific details of their work. For example, testing particularly intricate theories may take days or months, working either independently or with other scientists.
Whether alone or with colleagues, Davide enjoys his work for the independence his job offers. “You have lots of intellectual freedom. Nobody really tells you what to do,” he says. “It’s up to your skills and vision.”
Source: Domingo Angeles, "Quantum computer research scientist," Career Outlook, U.S. Bureau of Labor Statistics, July 2016. https://www.bls.gov/careeroutlook/2016/youre-a-what/quantum-computer-research-scientist.htm