Parallelization techniques for quantum simulation of fermionic systems

TL;DR

This paper explores how different fermion-to-qubit mappings and qubit connectivity influence the parallelization of quantum simulations of fermionic systems, proposing graph coloring solutions to optimize circuit depth.

Contribution

It introduces a graph coloring framework to analyze and improve parallelization in fermionic quantum simulations, considering both weak and strong coloring approaches.

Findings

Strong coloring yields better parallelization than weak coloring.

Optimized mappings significantly reduce circuit depth.

Numerical examples demonstrate practical improvements.

Abstract

Mapping fermionic operators to qubit operators is an essential step for simulating fermionic systems on a quantum computer. We investigate how the choice of such a mapping interacts with the underlying qubit connectivity of the quantum processor to enable (or impede) parallelization of the resulting Hamiltonian-simulation algorithm. It is shown that this problem can be mapped to a path coloring problem on a graph constructed from the particular choice of encoding fermions onto qubits and the fermionic interactions onto paths. The basic version of this problem is called the weak coloring problem. Taking into account the fine-grained details of the mapping yields what is called the strong coloring problem, which leads to improved parallelization performance. A variety of illustrative analytical and numerical examples are presented to demonstrate the amount of improvement for both weak and strong coloring-based parallelizations. Our results are particularly important for implementation on near-term quantum processors where minimizing circuit depth is necessary for algorithmic feasibility.