Chebyshev Approximated Variational Coupled Cluster for Quantum Computing

TL;DR

This paper introduces Chebyshev polynomial-based methods to approximate variational coupled cluster theory on quantum computers, improving efficiency and accuracy for quantum state preparation and tomography.

Contribution

It proposes the Chebyshev approximated VCC and Hermitian-part Chebyshev VCC methods, enabling efficient non-unitary wave function implementation on quantum computers.

Findings

C^d-VCC converges rapidly with increasing degree d.

HC^d-VCC reduces Chebyshev expansion error.

Methods facilitate initial state preparation and quantum state tomography.

Abstract

We propose an approach to approximately implement the variational coupled cluster (VCC) theory on quantum computers, which struggles with exponential scaling of computational costs on classical computers. To this end, we employ expanding the exponential cluster operator using Chebyshev polynomials and introduce two methods: the Chebyshev approximated VCC (C$^d$-VCC) and the Hermitian-part Chebyshev approximated VCC (HC$^d$-VCC), where $d$ indicates the maximum degree of the Chebyshev polynomials. The latter method decomposes the cluster operator into anti-Hermitian and Hermitian parts, with the anti-Hermitian part represented by the disentangled unitary coupled cluster ansatz and the Hermitian part approximated using Chebyshev expansion. We illustrate the implementation of the HC$^d$-VCC in a quantum circuit using the quantum singular value transformation technique. Numerical simulations show that the C$^d$-VCC rapidly converges to the exact VCC with increasing truncation degree $d$, and the HC$^d$-VCC effectively reduces the Chebyshev expansion error compared to the C$^d$-VCC. The HC$^d$-VCC method for realizing non-unitary coupled cluster wave functions on quantum computers is expected to be useful for initial state preparation on quantum computers and efficient tomography of the quantum state for post-processing on classical computers after quantum computations.