High-Performance Contraction of Quantum Circuits for Riemannian Optimization

TL;DR

This paper introduces a high-performance, matrix-free Riemannian optimization framework for quantum circuit gate contraction, enabling efficient approximation of Hamiltonian evolution on large Hilbert spaces.

Contribution

It develops a novel matrix-free, HPC-optimized Riemannian trust-region algorithm for quantum circuit optimization, exploiting sparsity and parallelization for large-scale problems.

Findings

Achieved nearly linear speed-up on 16-site Fermi-Hubbard model

Demonstrated efficiency of matrix-free approach over explicit matrix methods

Exploited Hamiltonian symmetries for computational savings

Abstract

This work focuses on optimizing the gates of a quantum circuit with a given topology to approximate the unitary time evolution governed by a Hamiltonian. Recognizing that unitary matrices form a mathematical manifold, we employ Riemannian optimization methods -- specifically the Riemannian trust-region algorithm -- which involves second derivative calculations with respect to the gates. Our key technical contribution is a matrix-free algorithmic framework that avoids the explicit construction and storage of large unitary matrices acting on the whole Hilbert space. Instead, we evaluate all quantities as sums over state vectors, assuming that these vectors can be stored in memory. We develop HPC-optimized kernels for applying gates to state vectors and for the gradient and Hessian computation. Further improvements are achieved by exploiting sparsity structures due to Hamiltonian conservation laws, such as parity conservation, and lattice translation invariance. We benchmark our implementation on the Fermi-Hubbard model with up to 16 sites, demonstrating a nearly linear parallelization speed-up with up to 112 CPU threads. Finally, we compare our implementation with an alternative matrix product operator-based approach.