quantum computing

Quantum PDE Solvers in Practice: Application-Driven Benchmarking of the Heat Equation

arXiv:2607.12688

summary

The paper introduces a reproducible benchmark for solving the 1‑D Dirichlet heat equation on quantum computers, comparing eleven different quantum PDE solver kernels across various problem sizes and hardware models.

Abstract

Quantum PDE solvers are difficult to evaluate in practice because published studies use different discretizations, output models, reconstruction rules, and hardware assumptions. We present a reproducible, application-driven benchmark for the 1-D Dirichlet heat equation that compares eleven kernels under the same problem instances and readout contract. The benchmark covers coherent linear solvers (HHL, QSVT, and QLS-Fourier), VQLS, imaginary-time methods (QITE, var-QITE, and AVQDS), real-time Hamiltonian simulation and unitary dilations (Hamiltonian simulation, Schade-Hamiltonian, and Schr"odingerisation), and the spectral quantum simulation method (QSM). We use three initial conditions, four grid sizes from to qubits ( to ), a CFL-like ratio , and final time . Statevector, ideal-shot ( shots per step), and noisy Aer backends separate algorithmic, sampling, and device-noise errors. On statevector, QSM and Schade-Hamiltonian reproduce the semi-discrete reference to floating-point precision, Schr"odingerisation reaches approximately error, and QITE is the strongest non-transform method for smooth data. Under the fixed-shot setting, HHL degrades to approximately relative error, while several low-depth or postselected methods become readout-limited. A norm-mismatch ablation attributes 23--29% of the smooth-initial-condition error of Hamiltonian simulation, AVQDS, and QLS-Fourier to reconstruction normalization. Compact observables, including total thermal energy and individual Fourier-mode weights, require 1--3 orders of magnitude fewer shots than full-field reconstruction. The resulting public benchmark provides a practical guide for selecting quantum PDE solvers.

11 pages, 5 figures, 5 tables. Accepted for presentation at the 2026 IEEE International Conference on Quantum Computing and Engineering (QCE26, IEEE Quantum Week 2026); to appear in the conference proceedings

Topics & keywords

#quantum algorithms#partial differential equations#benchmarking#heat equation#quantum simulationHHLQSVTQLS-FourierQITEAVQDSHamiltonian simulationSchrödingerisationQSMstatevectorAer backendCFL ratio
Quantum PDE Solvers in Practice: Application-Driven Benchmarking of the Heat Equation · wovepaper