Demonstrations of quantum advantage for random circuit and boson sampling over the past few years have generated considerable excitement for the future of quantum computing and has further spurred the development of a wide range of gate-based digital quantum computers, which represent quantum programs as a sequence of quantum gates acting on one and two qubits. Amongst this excitement, analog quantum computation has become less prominent, with expectations that, in time, digital quantum computers will be sufficient for emulating analog quantum computation and thus rendering analog quantum computation obsolete. In this work we show that the overhead of digital emulation of analog quantum computers is substantial enough that analog quantum computation is likely to have some advantages over digital quantum computers for the foreseeable future. Furthermore, we observe that commercially available analog quantum computers have reached a technology readiness level that is relevant to high-impact applications and develop an argument of the potential uses of these computers for important physics simulation applications in the next few years.
*We strongly encourage attendees to use their full name (and if possible, their UMD credentials) to join the zoom session.*
Dr. Carleton Coffrin is a senior scientist at Los Alamos National Laboratory with expertise in computer science, optimization algorithms, and artificial intelligence. Dr. Coffrin started exploring the field of quantum computation in 2016 and has had the opportunity to benchmark multiple generations of quantum computing hardware from vendors including D-Wave Systems and IBM. Dr. Coffrin was one of the founders of LANL's Quantum Computing Summer School program serving as a co-lead for three years (2018, 2019, 2020) and leads quantum computing research team of more than 20 staff scientists. In addition to quantum computing research, Dr. Coffrin has expertise in benchmarking optimization methods for energy systems applications, especially the AC Optimal Power Flow problem, and is a core contributor to the design and operation of ARPA-e’s Grid Optimization Competition.