{"paper":{"title":"Rate-Optimal Quantum Discrete Simulation Optimization","arxiv_id":"2610.03360","primary_cat":"quant-ph","submitted_at":"2026-10-02T14:22:57+00:00","version_compiled":"v1"},"notice":"Every quoted sentence is the authors' text from the arXiv listing abstract. Labels and probabilities are the outputs of classifier-1.13.0 on the question bank abstract-p1-20260923, exactly as returned. No sentence in this record was written by a language model.","schema":"pith.compiled-record/v0","status":"compiled","abstract":{"source":"arXiv listing abstract v1","sentences":[{"i":0,"p":1.0,"id":"s0","own":false,"role":"background","text":"Quantum computing has developed rapidly in recent years and has shown potential advantages over classical computing in various domains.","hedge":{"p":0.73,"label":"asserted"},"own_p":0.03,"main_claim_p":0.01,"quantitative":false,"quantitative_p":0.17,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.0,"background":1.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.0}},{"i":1,"p":0.83,"id":"s1","own":true,"role":"contribution","text":"In this paper, we study whether quantum computing can accelerate the convergence rate of discrete simulation optimization in the fixed-budget setting.","hedge":{"p":0.47,"label":"unclear"},"own_p":0.5,"main_claim_p":0.04,"quantitative":false,"quantitative_p":0.06,"role_probabilities":{"gap":0.03,"method":0.0,"result":0.0,"background":0.14,"limitation":0.0,"future_work":0.0,"contribution":0.83,"interpretation":0.0}},{"i":2,"p":0.61,"id":"s2","own":true,"role":"contribution","text":"We first formulate the quantum discrete simulation optimization problem by defining the quantum simulation oracle, the algorithmic performance measure, and the corresponding optimality guarantee.","hedge":{"p":0.99,"label":"asserted"},"own_p":0.96,"main_claim_p":0.03,"quantitative":false,"quantitative_p":0.04,"role_probabilities":{"gap":0.0,"method":0.39,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.61,"interpretation":0.0}},{"i":3,"p":0.51,"id":"s3","own":true,"role":"result","text":"Based on this formulation, we establish a minimax lower bound on the expected optimality gap for any quantum algorithm.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.96,"main_claim_p":0.07,"quantitative":true,"quantitative_p":0.78,"role_probabilities":{"gap":0.0,"method":0.02,"result":0.51,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.47,"interpretation":0.0}},{"i":4,"p":0.56,"id":"s4","own":true,"role":"method","text":"The lower bound analysis reduces the discrete simulation optimization problem to a sequential quantum phase-testing problem, and the resulting lower-bound argument may be of independent interest.","hedge":{"p":0.89,"label":"hedged_possibility"},"own_p":0.93,"main_claim_p":0.05,"quantitative":false,"quantitative_p":0.08,"role_probabilities":{"gap":0.0,"method":0.56,"result":0.03,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.29,"interpretation":0.12}},{"i":5,"p":0.97,"id":"s5","own":true,"role":"result","text":"By comparing the quantum lower bound with its classical counterpart, we show that the optimal convergence rate in the quantum setting exhibits a quadratic improvement in its dependence on the simulation budget, at the cost of a worse dependence on the number of decisions.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.95,"main_claim_p":0.6,"quantitative":true,"quantitative_p":0.97,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.97,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.03}},{"i":6,"p":0.87,"id":"s6","own":true,"role":"contribution","text":"We then propose the first fixed-budget quantum algorithm, called \\textsc{QUEST}, for discrete simulation optimization.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.96,"main_claim_p":0.08,"quantitative":false,"quantitative_p":0.11,"role_probabilities":{"gap":0.0,"method":0.13,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.87,"interpretation":0.0}},{"i":7,"p":0.99,"id":"s7","own":true,"role":"method","text":"The algorithm combines a quantum Monte Carlo subroutine with a sequential elimination framework and uses a slow-measurement mechanism to help preserve quantum superposition during the optimization process.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.93,"main_claim_p":0.04,"quantitative":false,"quantitative_p":0.05,"role_probabilities":{"gap":0.0,"method":0.99,"result":0.0,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.01,"interpretation":0.0}},{"i":8,"p":0.97,"id":"s8","own":true,"role":"result","text":"We prove that \\textsc{QUEST} achieves the optimal convergence rate up to logarithmic factors.","hedge":{"p":1.0,"label":"asserted"},"own_p":0.96,"main_claim_p":0.34,"quantitative":false,"quantitative_p":0.47,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.97,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.03,"interpretation":0.0}},{"i":9,"p":0.83,"id":"s9","own":true,"role":"result","text":"Numerical experiments further show that \\textsc{QUEST} outperforms its classical counterpart, which provides empirical evidence for the quantum advantage in improving the convergence rate of discrete simulation optimization algorithms.","hedge":{"p":0.93,"label":"asserted"},"own_p":0.95,"main_claim_p":0.28,"quantitative":true,"quantitative_p":0.8,"role_probabilities":{"gap":0.0,"method":0.0,"result":0.83,"background":0.0,"limitation":0.0,"future_work":0.0,"contribution":0.0,"interpretation":0.17}}]},"compiled":{"main_claim":{"p":0.8,"text":"By comparing the quantum lower bound with its classical counterpart, we show that the optimal convergence rate in the quantum setting exhibits a quadratic improvement in its dependence on the simulation budget, at the cost of a worse dependence on the number of decisions.","location":"abstract s5","hedge_strength":{"p":1.0,"label":"asserted"}},"provenance":{"route":{"via":"TypeSafe API","model":"classifier-1.13.0","endpoint":"https://api.typesafe.ai/v1/systemone","provider":"TypeSafe"},"packing":"one request per 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