Journal
QUANTUM
Volume 3, Issue -, Pages -Publisher
VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF
DOI: 10.22331/q-2019-05-13-140
Keywords
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Funding
- U.S. Department of Energy through a quantum computing program - LANL Information Science AMP
- Technology Institute
- Engineering Distinguished Fellowship through Michigan State University
- AFOSR YIP award [FA9550-16-1-0495]
- Institute for Quantum Information and Matter, an NSF Physics Frontiers Center [PHY-1733907]
- Kortschak Scholars program
- U.S. Department of Energy through the J. Robert Oppenheimer fellowship
- LANL ASC Beyond Moore's Law project
- LDRD program at LANL
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Compiling quantum algorithms for near-term quantum computers (accounting for connectivity and native gate alphabets) is a major challenge that has received significant attention both by industry and academia. Avoiding the exponential overhead of classical simulation of quantum dynamics will allow compilation of larger algorithms, and a strategy for this is to evaluate an algorithm's cost on a quantum computer. To this end, we propose a variational hybrid quantum-classical algorithm called quantum-assisted quantum compiling (QAQC). In QAQC, we use the overlap between a target unitary U and a trainable unitary V as the cost function to be evaluated on the quantum computer. More precisely, to ensure that QAQC scales well with problem size, our cost involves not only the global overlap Tr((VU)-U-dagger) but also the local overlaps with respect to individual qubits. We introduce novel short-depth quantum circuits to quantify the terms in our cost function, and we prove that our cost cannot be efficiently approximated with a classical algorithm under reasonable complexity assumptions. We present both gradient-free and gradient-based approaches to minimizing this cost. As a demonstration of QAQC, we compile various one-qubit gates on IBM's and Rigetti's quantum computers into their respective native gate alphabets. Furthermore, we successfully simulate QAQC up to a problem size of 9 qubits, and these simulations highlight both the scalability of our cost function as well as the noise resilience of QAQC. Future applications of QAQC include algorithm depth compression, black-box compiling, noise mitigation, and benchmarking.
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