Quantum-assisted quantum compiling

Khatri, Sumeet; LaRose, Ryan; Poremba, Alexander; Cincio, Lukasz; Sornborger, Andrew Tyler; Coles, Patrick Joseph

doi:10.22331/q-2019-05-13-140

Title: Quantum-assisted quantum compiling

Journal Article · Mon May 13 00:00:00 EDT 2019 · Quantum

DOI:https://doi.org/10.22331/q-2019-05-13-140· OSTI ID:1530785

Khatri, Sumeet ^[1]; LaRose, Ryan ^[2]; Poremba, Alexander ^[3];

^[4];

^[4]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Louisiana State Univ., Baton Rouge, LA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Michigan State Univ., East Lansing, MI (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); California Inst. of Technology (CalTech), Pasadena, CA (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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 $T r (V^{†} U)$ 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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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; Laboratory Directed Research & Development (LDRD)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1530785

Report Number(s):: LA-UR-18-25861

Journal Information:: Quantum, Vol. 3; ISSN 2521-327X

Publisher:: Quantum Science Open CommunityCopyright Statement

Country of Publication:: United States

Language:: English

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Parameterized quantum circuits as machine learning models Benedetti, Marcello; Lloyd, Erika; Sack, Stefan Quantum Science and Technology, Vol. 4, Issue 4 https://doi.org/10.1088/2058-9565/ab4eb5	journal	October 2019
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