Quantum Fourier analysis for multivariate functions and applications to a class of Schr\"odinger-type partial differential equations

TL;DR

This paper introduces a Fourier-based quantum algorithm for efficiently solving Schrödinger-type PDEs, demonstrating high accuracy and low qubit requirements on ideal and near-term quantum computers.

Contribution

It develops a Fourier analysis method for quantum function representation and applies it to create a variational quantum algorithm for PDEs, showing promising results with minimal qubits.

Findings

Achieved low infidelities of 10^{-4} to 10^{-5} with 3-4 qubits.

Demonstrated high information compression in quantum representations.

Benchmark results on quantum harmonic oscillator and qubit systems.

Abstract

In this work, we develop a highly efficient representation of functions and differential operators based on Fourier analysis. Using this representation, we create a variational hybrid quantum algorithm to solve static, Schr\"odinger-type, Hamiltonian partial differential equations (PDEs), using space-efficient variational circuits, including the symmetries of the problem, and global and gradient-based optimizers. We use this algorithm to benchmark the performance of the representation techniques by means of the computation of the ground state in three PDEs, i.e., the one-dimensional quantum harmonic oscillator, and the transmon and flux qubits, studying how they would perform in ideal and near-term quantum computers. With the Fourier methods developed here, we obtain low infidelities of order $10^{-4}-10^{-5}$ using only three to four qubits, demonstrating the high compression of information in a quantum computer. Practical fidelities are limited by the noise and the errors of the evaluation of the cost function in real computers, but they can also be improved through error mitigation techniques.