QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing

2026-08-07

AI Summary

This work addresses the challenge of rigorously identifying and validating genuine quantum speedups while avoiding misjudgments arising from inappropriate assumptions or methodological mismatches. The authors propose an auditable agent-based workflow that systematically constructs and conservatively verifies quantum advantage claims through structured task modeling, classical bottleneck analysis, matching to quantum primitives, and evidence graph generation. A key innovation is the introduction of typed state transitions and a deterministic ten-point verification protocol, ensuring consistency and traceability across task definitions, access models, promise conditions, and complexity classes. Evaluated on 582 open discovery tasks, the method achieves an average ODS score of 53.1—outperforming the strongest baseline by 17.3 points, surpassing it in 355 tasks, and attaining a 99.8% evidence graph audit pass rate—ranking first across all seven task categories.

Abstract

Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and output models, expose required promises, and remain within a defensible complexity scope. We present QuantumMind, an auditable agentic workflow for generating and conservatively screening quantum-acceleration hypotheses. A fixed sequence of typed, role-specialized actions formalizes the public task, analyzes structure and classical bottlenecks, matches a source-linked registry of quantum primitives and barriers, and constructs a scoped candidate scheme. A deterministic ten-check validator assigns the authoritative verdict; completed states are compiled into a Quantum Acceleration Evidence Graph and passed through a downward-only research screen that cannot strengthen the decision. We evaluate QuantumMind against seven task-adapted prompting and agentic controls on 582 identical open-discovery tasks. Under the frozen Open-Discovery Score (ODS), QuantumMind obtains 53.1 mean ODS, exceeding the strongest baseline by 17.3 points (48.2% relative), and wins 355 of 582 paired tasks against that baseline. It passes the graph audit on 99.8% of tasks, compared with 43.6% for the strongest baseline, and ranks first in all seven task families. The results indicate that typed state transitions and deterministic evidence control contribute beyond fluent generation alone.

Problem

• quantum speedup
• quantum computing
• complexity analysis
• validation framework
• computational advantage

Innovation

• agentic reasoning
• quantum speedup
• constraint-grounded workflow
• evidence graph
• deterministic validation