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Utilizing Intermediate States in Quantum Annealing for Multi-objective Optimization
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DOI：10.7566/jpsj.95.053001.png)
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Abstract
En 中文
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 tradeoff 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.
Keywords:
PYTHON FRAMEWORK
DYNAMICS
QUTIP
Journal
J
IF:
2.2
Papers:
231
Citations:
1.8W