Quantum mean value approximator for hard integer value problems

Abstract

Evaluating the expectation of a quantum circuit is a classically difficult problem known as the quantum mean value problem (QMV). It is used to optimize the quantum approximate optimization algorithm and other variational quantum eigensolvers. We show that such an optimization can be improved substantially by using an approximation rather than the exact expectation. Together with efficient classical sampling algorithms, a quantum algorithm with minimal gate count can thus improve the efficiency of general integer-value problems, such as the shortest vector problem (SVP) investigated in this work.

3 Figures, 4 pages main text, 4 pages supplementary material

References in corpus (6)

• Variational Quantum Algorithms
• A Quantum Approximate Optimization Algorithm
• Noisy intermediate-scale quantum (NISQ) algorithms
• Training variational quantum algorithms is NP-hard
• The Quantum Approximate Optimization Algorithm Needs to See the Whole Graph: A Typical Case
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Cited by in corpus (2)

• Grover's oracle for the Shortest Vector Problem and its application in hybrid classical-quantum solvers
• Iterative Quantum Optimization with Adaptive Problem Hamiltonian