Quantum Prediction of Transport Dynamics in Discretized State Spaces

TL;DR

This paper introduces a quantum algorithm for predicting transport dynamics in discretized state spaces, leveraging quantum Fourier transforms and a Wick rotation to efficiently simulate Bayesian state estimation governed by the Fokker-Planck equation.

Contribution

It presents a novel quantum approach that accurately models drift and diffusion in high-dimensional probability distributions using unitary operations and spectral methods.

Findings

The quantum algorithm accurately reproduces classical transport dynamics.

Numerical evaluations show strong agreement with exact Fokker-Planck solutions.

The method enables efficient high-dimensional probability density propagation on quantum computers.

Abstract

We propose a gate-based quantum algorithm for the prediction step of Bayesian state estimation based on the Fokker-Planck equation on a discretized position-velocity state space. The probability density is encoded in the amplitudes of a quantum state, enabling a compact representation of high-dimensional distributions. Exploiting the circulant structure of finite-difference operators, the evolution is realized in the spectral domain using quantum Fourier transforms and phase rotations. A key result is that the drift component can be implemented exactly in amplitude space, leading to an accurate reproduction of the classical transport dynamics. In contrast, the diffusion term does not admit a linear representation in amplitude space due to the nonlinear relation between probability density and wave function. To enable a quantum implementation, we introduce a unitary surrogate based on a Wick rotation, transforming diffusion into a dispersive phase evolution. This yields a fully unitary propagation that can be implemented efficiently on a gate-based quantum computer. The proposed method is evaluated numerically for different scenarios and shows strong agreement with the exact solution of the Fokker-Planck equation. The approach demonstrates the potential of quantum computing for Bayesian state estimation, as the representable state space grows exponentially with the number of qubits. This allows the efficient representation and propagation of probability densities that would otherwise require complex tensor decompositions on classical hardware, making the method a promising candidate for high-dimensional filtering problems.