Parallel Quantum Rapidly-Exploring Random Trees

Paul Lathrop, Beth Boardman and Sonia Martínez
IEEE Access, 12 (2024) 47173-47189

Abstract:

In this paper, we present the Parallel Quantum Rapidly-Exploring Random Tree (Pq-RRT) algorithm, a parallel version of a previously proposed Quantum Rapidly-Exploring Random Trees (q-RRT) algorithm. Parallel Quantum RRT is a parallel quantum algorithm formulation of a sampling-based motion planner that uses Quantum Amplitude Amplification to search databases of reachable states for addition to a tree. In this work we investigate how parallel quantum devices can more efficiently search a database, as the quantum measurement process involves the collapse of the superposition to a base state, erasing probability information and therefore the ability to efficiently find multiple solutions. Pq-RRT uses a manager/parallel-quantum-workers formulation, inspired by traditional parallel motion planning, to perform simultaneous quantum searches of a feasible state database. We present results regarding likelihoods of multiple parallel units finding any and all solutions contained with a shared database, with and without reachability errors, allowing efficiency predictions to be made. We offer simulations in dense obstacle environments showing efficiency, density/heatmap, and speed comparisons for Pq-RRT against q-RRT, classical RRT, and classical parallel RRT. We then present Quantum Database Annealing, a database construction strategy for Pq-RRT and q-RRT that uses a temperature construct to define database creation over time for balancing exploration and exploitation.

File: (ArXiv version)

Bib-tex entry:

@article{PL-BB-SM:24-access,
author = {P. D. Lathrop and B. Boardman and S. Mart{\'\i}nez},
title = {Parallel Quantum Rapidly-Exploring Random Trees},
journal= {IEEE Access},
pages = {47173--47189},
volume = {12},
year = {2024},
note = {Freely available codes at https://github.com/pdlathrop/QRRT}
}