Quantum Fourier analysis for multivariate functions and applications to a class of Schrödinger-type partial differential equations

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, Schrödinger-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.

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• Linear-depth quantum circuits for loading Fourier approximations of arbitrary functions
• Variational Quantum-Based Simulation of Waveguide Modes
• Multiobjective variational quantum optimization for constrained problems: an application to Cash Management
• Efficient quantum interpolation of natural data
• Protocols for Trainable and Differentiable Quantum Generative Modelling
• Hardware-efficient entangled measurements for variational quantum algorithms
• The State Preparation of Multivariate Normal Distributions using Tree Tensor Network
• Alternatives to a nonhomogeneous partial differential equation quantum algorithm
• Inchworm tensor train hybridization expansion quantum impurity solver
• Pseudospectral method for solving PDEs using Matrix Product States