Scalable Quantum Computing

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Quantum computing aims to solve some classical (i.e., non-quantum) problems faster than classical computers allow. Technically, this speedup means that the computational time scales better for a quantum computer than for a classical computer. Three examples help to explain. Whereas the time to find a marked item in an unstructured search of N items is linear in N classically, a quantum search is provably quadratically better, i.e., requiring only the square root of N evaluations. Quantum factorization, which is based on finding hidden periodicity efficiently on a quantum computer, is superpolynomially faster than the sub-exponential-time classical number-field sieve and has potential applications to linear-equation solvers. Quantum annealers aspire to demonstrate speedup over classical computing for some hard optimization problems and the quantum advantage is an open question. Optimization problems are ubiquitous, such as in finance and protein folding, so success with quantum annealing could have enormous positive ramifications for society. A key point of these goals is that the quantum computer itself must scale well to larger numbers of qubits and run-times or the theoretical advantage of quantum computing is lost to hardware problems. This research program addresses and seeks to resolve the problems of scalability, driven by the principle of co-design, namely that the type of problem being solved influences choices about architecture and design and the limitations of the computer influences which computational problems are suitable for quantum computing. This research involves one undergraduate student annually, five graduate students and one postdoctoral researcher in strategically important research topics that underpin positively disrupting, i.e., rapidly transforming, computer technology with ramifications in many areas of science, technology and beyond. These trainees will have opportunities to work with experts in experimental physics and in computer science, which prepares them for academic and industrial opportunities in quantum computing. This project aims to advance scalability of quantum computing along the following avenues, namely designing high-fidelity quantum circuits, developing quantum algorithms for dozens-of-qubits quantum computing, creating algorithms for sampling problems inspired by the BosonSampling problem, articulating verification-and-validation to know that components and devices meet standards and do what they are supposed to, and establishing confidentiality, integrity and availability for interconnected quantum things. In this last case, things refers to components, devices and systems and pertains to quantum networking, multi-party quantum communication and quantum computing on the cloud. One graduate student will work on each of these five avenues and a postdoctoral researcher will have the grand view and interconnect these discoveries. The undergraduate students will focus on solving technical issues.