Multiple Target Tracking and Filtering using Bayesian Diabatic Quantum Annealing

TL;DR

This paper introduces a hybrid quantum/classical algorithm utilizing diabatic quantum annealing to address the NP-hard multiple target data association and tracking problem, demonstrating a novel quantum-assisted filtering approach.

Contribution

The paper presents the first demonstration of a Bayesian hybrid quantum-classical filter for multiple target tracking using diabatic quantum annealing, contrasting it with adiabatic methods.

Findings

Demonstrated the hybrid quantum/classical approach on a simple example.

Compared diabatic quantum annealing with adiabatic quantum computing for MTDA.

Analyzed technical limitations of quantum annealers in diabatic mode.

Abstract

In this paper, we present a hybrid quantum/classical algorithm to solve an NP-hard combinatorial problem called the multiple target data association (MTDA) and tracking problem. We use diabatic quantum annealing (DQA) to enumerate the low energy, or high probability, feasible assignments, and we use a classical computer to find the Bayesian expected mean track estimate by summing over these assignments. We demonstrate our hybrid quantum/classical approach on a simple example. This may be the first demonstration of a Bayesian hybrid quantum-classical multiple target tracking filter. We contrast our DQA method with the adiabatic quantum computing (AQC) approach to MTDA. We give a theoretical overview of DQA and characterize some of the technical limitations of using quantum annealers in this novel diabatic modality.