Quantum Prediction of Transport Dynamics in Discretized State Spaces
For researchers in quantum computing and Bayesian filtering, this work provides a novel quantum algorithm for high-dimensional state estimation, though it is an incremental step as it addresses a known bottleneck with a hybrid approach.
The paper proposes a gate-based quantum algorithm for the prediction step of Bayesian state estimation using the Fokker-Planck equation, achieving strong agreement with exact solutions in numerical evaluations. The method enables efficient representation and propagation of high-dimensional probability densities, with state space growing exponentially with qubits.
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.