Continuous-variable quantum approximate optimization on a programmable photonic quantum processor

TL;DR

This paper demonstrates a continuous-variable quantum approximate optimization algorithm implemented on a programmable photonic quantum processor, showing potential for practical quantum advantage in solving minimization problems.

Contribution

It develops and experimentally implements a CV version of a VQA on a photonic quantum computer, enabling practical optimization on NISQ devices.

Findings

Successfully localized a wavefunction to the minimum of a simple function

Demonstrated quantum gradient descent on a photonic platform

Showed potential for extending to more complex functions

Abstract

Variational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on near-term noisy intermediate-scale quantum (NISQ) devices. Thus far, most studies on VQAs have focused on qubit-based systems, but the power of VQAs can be potentially boosted by exploiting infinite-dimensional continuous-variable (CV) systems. Here, we implement the CV version of one VQA, a quantum approximate optimization algorithm by developing an automated collaborative computing system between a programmable photonic quantum computer and a classical computer. We experimentally demonstrate that this algorithm solves the minimization problem of simple continuous functions by implementing the quantum version of gradient descent to localize an initially broadly-distributed wavefunction to the minimum. This method allows the execution of a practical CV quantum algorithm on a physical platform. Our work can be extended to the minimization of more general functions, providing an alternative to achieve the quantum advantage in practical problems.