Optimal Compilers for Quantum Machines

Sahil Gulania, University of Southern California
Calculating the Benefits of Exascale and Quantum Computers

Abstract: Recent availability of quantum computers for public use has made the long-term dream of simulating a wide range of scientific problems on quantum machines into reality. However, present quantum machines are prone to error for longer simulation times. This problem could be addressed by producing quantum machine instructions which are smaller in size, thus completing simulation before noise overpowers the results. We have recently derived a smaller set of machine instructions for simulating quantum materials dynamics by designing optimal compilers. The compilers' design borrows ideas from heuristic techniques commonly used in artificial intelligence, making them scale well with dynamics simulation time-step and quantum system size. The compiler beat the IBM's and Rigetti's compilers by 25-30% in quantum machine instructions size and around 40% in compiling time. The same idea can be borrowed for other problems and may provide a smarter simulation on quantum machines.

Reference: L. Bassman, S. Gulania, C. Powers, R. Li, T. Linker, K. Liu, T. K. S. Kumar, R. K. Kalia, A. Nakano, P. Vashishta, “Domain-Specific Compilers for Dynamic Simulations of Quantum Materials on Quantum Computers”, Quantum Science and Technology, in press, arXiv:2004.07418 (2020).

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