QREChem: Quantum Resource Estimation Software for Chemistry Applications

TL;DR

QREChem tackles the challenge of predicting resource requirements for quantum chemistry computations on quantum hardware by introducing a modular framework that focuses on the Trotterized quantum phase estimation approach. It delivers end-to-end, logical resource estimates for ground-state energy calculations, incorporating heuristic overheads for Trotter steps and ancilla counts, and benchmarks these estimates against TFermion and OpenFermion. The study analyzes a range of small molecules and FeMoco, highlighting how hardware and error-correction considerations—especially surface-code overhead—shape practical feasibility. The work underlines the importance of co-design between quantum hardware and algorithms and outlines future enhancements to broaden algorithm support and improve hardware modeling for realistic quantum-chemistry simulations.

Abstract

As quantum hardware continues to improve, more and more application scientists have entered the field of quantum computing. However, even with the rapid improvements in the last few years, quantum devices, especially for quantum chemistry applications, still struggle to perform calculations that classical computers could not calculate. In lieu of being able to perform specific calculations, it is important have a systematic way of estimating the resources necessary to tackle specific problems. Standard arguments about computational complexity provide hope that quantum computers will be useful for problems in quantum chemistry but obscure the true impact of many algorithmic overheads. These overheads will ultimately determine the precise point when quantum computers will perform better than classical computers. We have developed QREChem to provide logical resource estimates for ground state energy estimation in quantum chemistry through a Trotter-based quantum phase estimation approach. QREChem provides resource estimates which include the specific overheads inherent to problems in quantum chemistry by including heuristic estimates of the number of Trotter steps and number of necessary ancilla, allowing for more accurate estimates of the total number of gates. We utilize QREChem to provide logical resource estimates for a variety of small molecules in various basis sets, obtaining estimates in the range of $10^7-10^{15}$ for total number of T gates. We also determine estimates for the FeMoco molecule and compare all estimates to other resource estimation tools.

Figures (3)

• Figure 1: Schematic diagram of QREChem.
• Figure 2: Estimated total numbers of logical
• Figure 3: Estimated total resources with hardware and surface code error correction overheads included for QREChem's Trotter algorithm. The total space-time volume (shown in (a)), in qubit-seconds, is larger for a trapped ion system compared with a superconducting qubit system. While the number of physical qubits is smaller for a trapped ion system due to the lower error rates (see (b)), the total time (see (c)) is much higher due to the slower error correction cycle time.