Squeezing and quantum approximate optimization

Abstract

Variational quantum algorithms offer fascinating prospects for the solution of combinatorial optimization problems using digital quantum computers. However, the achievable performance in such algorithms and the role of quantum correlations therein remain unclear. Here, we shed light on this open issue by establishing a tight connection to the seemingly unrelated field of quantum metrology: Metrological applications employ quantum states of spin-ensembles with a reduced variance to achieve an increased sensitivity, and we cast the generation of such squeezed states in the form of finding optimal solutions to a combinatorial MaxCut problem with an increased precision. By solving this optimization problem with a quantum approximate optimization algorithm (QAOA), we show numerically as well as on an IBM quantum chip how highly squeezed states are generated in a systematic procedure that can be adapted to a wide variety of quantum machines. Moreover, squeezing tailored for the QAOA of the MaxCut permits us to propose a figure of merit for future hardware benchmarks.

8+7 pages, 4+8 figures

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• Counterdiabatic Route to Entanglement Steering and Dynamical Freezing in the Floquet Lipkin-Meshkov-Glick Model
• A Monte Carlo Tree Search approach to QAOA: finding a needle in the haystack
• Simulating methylamine using symmetry adapted qubit-excitation-based variational quantum eigensolver
• Quantum Optimization Benchmarking Library - The Intractable Decathlon
• Approximate Quadratization of High-Order Hamiltonians for Combinatorial Quantum Optimization
• Efficient Online Quantum Circuit Learning with No Upfront Training