State-space reduction techniques exploiting specific constraints for quantum search Application to a specific job scheduling problem

TL;DR

This paper introduces a novel state-space reduction technique for quantum search algorithms applied to job scheduling problems, exploiting specific constraints to improve efficiency by focusing on a smaller, relevant solution space.

Contribution

It proposes a method to exploit scheduling constraints for state-space reduction, enabling more practical quantum search applications in industrial problems.

Findings

State-space reduction leads to a nearly quadratic increase in initial superposition size.

Numerical results on quantum emulators demonstrate potential efficiency gains.

The approach focuses quantum search on relevant solution subsets, improving scalability.

Abstract

Quantum search has emerged as one of the most promising fields in quantum computing. State-of-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density of these elements until reaching a high density. This kind of algorithms demonstrate a theoretical quadratic speed-up on the number of queries compared to classical search algorithms in unstructured spaces. Unfortunately, the major part of the existing literature applies quantum search to problems which size grows exponnentialy with the input size without exploiting any specific problem structure, rendering this kind of approach not exploitable in real industrial problems. In contrast, this work proposes exploiting specific constraints of scheduling problems to build an initial superposition of states with size almost quadraticaly increasing as a function of the problem size. This state space reduction, inspired by the quantum walk algorithm, constructs a state superposition corresponding to all paths in a state-graph embedding spacing constraints between jobs. Our numerical results on quantum emulators highlights the potential of state space reduction approach, which could lead to more efficient quantum search processes by focusing on a smaller, more relevant, solution space.