Mutual information-assisted Adaptive Variational Quantum Eigensolver

TL;DR

This paper introduces a method to efficiently construct ansatz circuits for variational quantum eigensolvers by using mutual information to select a smaller, more effective pool of entanglers, reducing complexity while maintaining accuracy.

Contribution

It proposes a mutual information-based approach to reduce entangler pool size in adaptive variational quantum algorithms, improving efficiency without sacrificing accuracy.

Findings

Reduced entangler pools achieve the same accuracy as larger pools.

Classical precomputation with DMRG supports the method.

Numerical tests on small molecules validate the approach.

Abstract

Adaptive construction of ansatz circuits offers a promising route towards applicable variational quantum eigensolvers on near-term quantum hardware. Those algorithms aim to build up optimal circuits for a certain problem and ansatz circuits are adaptively constructed by selecting and adding entanglers from a predefined pool. In this work, we propose a way to construct entangler pools with reduced size by leveraging classical algorithms. Our method uses mutual information between the qubits in classically approximated ground state to rank and screen the entanglers. The density matrix renormalization group method is employed for classical precomputation in this work. We corroborate our method numerically on small molecules. Our numerical experiments show that a reduced entangler pool with a small portion of the original entangler pool can achieve same numerical accuracy. We believe that our method paves a new way for adaptive construction of ansatz circuits for variational quantum algorithms.