Utilizing intermediate states in quantum annealing for multi-objective optimization

TL;DR

This paper explores using intermediate quantum states during quantum annealing to improve multi-objective optimization, overcoming limitations of traditional methods and enabling better Pareto front exploration.

Contribution

It introduces a method to utilize intermediate states in quantum annealing, validated through experiments and simulations, to enhance solution diversity and convergence in multi-objective optimization.

Findings

Early measurement timing increases solution diversity.

Later timing improves convergence to non-dominated solutions.

Practical timing balances diversity and convergence.

Abstract

We investigate obtaining intermediate quantum states during the quantum annealing process to address the limitation of the linear weighted sum method in multi-objective optimization, which inherently fails to reach non-convex regions of the Pareto front. We validate this approach through physical experiments utilizing quench-based readout and numerical simulations assuming ideal mid-anneal measurements. Both methods consistently demonstrate a clear trade-off where earlier timing enhances diversity of the solutions, whereas later timing ensures convergence to non-dominated solutions. Notably, a practical compromise timing balances both metrics. The qualitative agreement between practical quench and ideal simulation indicates the potential of accessing the intermediate states for comprehensive Pareto front exploration.