Resource-efficient digital quantum simulation of $d$-level systems for photonic, vibrational, and spin-$s$ Hamiltonians

TL;DR

This paper explores resource-efficient methods for simulating $d$-level quantum systems, such as spin-$s$ and bosonic Hamiltonians, on qubit-based quantum computers, focusing on encoding strategies and Trotterization to reduce circuit depth.

Contribution

It introduces a novel approach using Gray code encodings for $d$-level systems, analyzing their resource requirements and advantages over existing methods.

Findings

Gray code encoding reduces resource requirements for certain Hamiltonians.

Resource counts vary significantly with encoding choice and system parameters.

Proposed methods are applicable to a wide range of quantum simulation problems.

Abstract

Simulation of quantum systems is expected to be one of the most important applications of quantum computing, with much of the theoretical work so far having focused on fermionic and spin-$\frac{1}{2}$ systems. Here, we instead consider encodings of $d$-level (i.e. qudit) quantum operators into multi-qubit operators, studying resource requirements for approximating operator exponentials by Trotterization. We primarily focus on spin-$s$ and truncated bosonic operators in second quantization, observing desirable properties for approaches based on the Gray code, which to our knowledge has not been used in this context previously. After outlining a methodology for implementing an arbitrary encoding, we investigate the interplay between Hamming distances, sparsity patterns, bosonic truncation, and other properties of local operators. Finally, we obtain resource counts for five common Hamiltonian classes used in physics and chemistry, while modeling the possibility of converting between encodings within a Trotter step. The most efficient encoding choice is heavily dependent on the application and highly sensitive to $d$, although clear trends are present. These operation count reductions are relevant for running algorithms on near-term quantum hardware because the savings effectively decrease the required circuit depth. Results and procedures outlined in this work may be useful for simulating a broad class of Hamiltonians on qubit-based digital quantum computers.