Hybrid Quantum-Classical Approach to Quantum Optimal Control

TL;DR

This paper introduces a hybrid quantum-classical method for quantum optimal control, leveraging quantum simulators to efficiently compute gradients and optimize control parameters, demonstrated experimentally on a nine-spin NMR system.

Contribution

It presents a novel hybrid approach that uses quantum simulators for gradient calculations, reducing classical computational complexity in quantum control optimization.

Findings

Successfully prepared a seven-correlated quantum state on a nine-spin NMR system.

Demonstrated the feasibility of hybrid quantum-classical control optimization.

Reduced classical computational load by utilizing quantum simulation for gradient computation.

Abstract

A central challenge in quantum computing is to identify more computational problems for which utilization of quantum resources can offer significant speedup. Here, we propose a hybrid quantum-classical scheme to tackle the quantum optimal control problem. We show that the most computationally demanding part of gradient-based algorithms, namely computing the fitness function and its gradient for a control input, can be accomplished by the process of evolution and measurement on a quantum simulator. By posing queries to and receiving messages from the quantum simulator, classical computing devices update the control parameters until an optimal control solution is found. To demonstrate the quantum-classical scheme in experiment, we use a nine-spin nuclear magnetic resonance system, on which we have succeeded in preparing a seven-correlated quantum state without involving classical computation of the large Hilbert space evolution.