Policy Gradient based Quantum Approximate Optimization Algorithm

TL;DR

This paper introduces a reinforcement learning approach using policy gradients to optimize the Quantum Approximate Optimization Algorithm (QAOA) parameters, making it more robust to noise and uncertainties in quantum systems.

Contribution

The paper demonstrates that policy-gradient RL algorithms can effectively optimize QAOA parameters under physical constraints and noise, outperforming existing methods in noisy quantum environments.

Findings

RL-based optimization outperforms traditional algorithms in noisy conditions

The method is effective for quantum state transfer in multi-qubit systems

Robustness to errors and uncertainties in quantum control is improved

Abstract

The quantum approximate optimization algorithm (QAOA), as a hybrid quantum/classical algorithm, has received much interest recently. QAOA can also be viewed as a variational ansatz for quantum control. However, its direct application to emergent quantum technology encounters additional physical constraints: (i) the states of the quantum system are not observable; (ii) obtaining the derivatives of the objective function can be computationally expensive or even inaccessible in experiments, and (iii) the values of the objective function may be sensitive to various sources of uncertainty, as is the case for noisy intermediate-scale quantum (NISQ) devices. Taking such constraints into account, we show that policy-gradient-based reinforcement learning (RL) algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion, opening up the way for developing RL techniques for continuous quantum control. This is advantageous to help mitigate and monitor the potentially unknown sources of errors in modern quantum simulators. We analyze the performance of the algorithm for quantum state transfer problems in single- and multi-qubit systems, subject to various sources of noise such as error terms in the Hamiltonian, or quantum uncertainty in the measurement process. We show that, in noisy setups, it is capable of outperforming state-of-the-art existing optimization algorithms.