Enhancing variational quantum state diagonalization using reinforcement learning techniques

TL;DR

This paper introduces a reinforcement learning approach to design shallower quantum circuits for variational quantum state diagonalization, improving feasibility on near-term quantum hardware.

Contribution

It presents a novel RL-based method for constructing shallower quantum circuits, enhancing the implementation of variational quantum algorithms on NISQ devices.

Findings

RL-designed circuits are shallower than standard methods

The approach adapts to hardware limitations on circuit depth

Method can be extended to other variational quantum algorithms

Abstract

The variational quantum algorithms are crucial for the application of NISQ computers. Such algorithms require short quantum circuits, which are more amenable to implementation on near-term hardware, and many such methods have been developed. One of particular interest is the so-called variational quantum state diagonalization method, which constitutes an important algorithmic subroutine and can be used directly to work with data encoded in quantum states. In particular, it can be applied to discern the features of quantum states, such as entanglement properties of a system, or in quantum machine learning algorithms. In this work, we tackle the problem of designing a very shallow quantum circuit, required in the quantum state diagonalization task, by utilizing reinforcement learning (RL). We use a novel encoding method for the RL-state, a dense reward function, and an $\epsilon$-greedy policy to achieve this. We demonstrate that the circuits proposed by the reinforcement learning methods are shallower than the standard variational quantum state diagonalization algorithm and thus can be used in situations where hardware capabilities limit the depth of quantum circuits. The methods we propose in the paper can be readily adapted to address a wide range of variational quantum algorithms.