Hierarchical Clifford transformations to reduce entanglement in quantum chemistry wavefunctions

TL;DR

This paper introduces hierarchical Clifford transformations that reduce entanglement in quantum chemistry wavefunctions, enabling more efficient classical and quantum computations of molecular ground states by exploring new bases through spectrum-preserving similarity transformations.

Contribution

It presents a novel method for constructing Clifford similarity transformations that reduce entanglement in molecular Hamiltonians, improving computational efficiency in quantum chemistry.

Findings

Significant reduction in bipartite entanglement in ground states.

Enhanced efficiency of classical methods like DMRG.

Improved performance of variational quantum algorithms.

Abstract

The performance of computational methods for many-body physics and chemistry is strongly dependent on the choice of basis used to cast the problem; hence, the search for better bases and similarity transformations is important for progress in the field. So far, tools from theoretical quantum information have been not thoroughly explored for this task. Here we take a step in this direction by presenting efficiently computable Clifford similarity transformations for quantum chemistry Hamiltonians, which expose bases with reduced entanglement in the corresponding molecular ground states. These transformations are constructed via block diagonalization of a hierarchy of truncated molecular Hamiltonians, preserving the full spectrum of the original problem. We show that the bases introduced here allow for more efficient classical and quantum computation of ground state properties. First, we find a systematic reduction of bipartite entanglement in molecular ground states as compared to standard problem representations. This entanglement reduction has implications in classical numerical methods such as ones based on the density matrix renormalization group. Then, we develop variational quantum algorithms that exploit the structure in the new bases, showing again improved results when the hierarchical Clifford transformations are used.