arXiv Machine Learning
Measurement-Efficient Differentiable Quantum Architecture Search for Combinatorial Optimization
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DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems
arXiv:2607. 20225v1 Announce Type: cross Abstract: While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces.
arXiv Machine Learning
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Beyond QAOA: A Review of AI and Quantum Computing for Adaptive Combinatorial Optimization
The review examines how machine learning can adapt quantum combinatorial optimization workflows by learning decisions such as formulation, penalties, shot budgets, and backend selection. It categorizes the literature into three paradigms—AI for quantum optimization, quantum for AI-driven optimization, and AI‑quantum co‑optimization—and analyzes 119 papers, finding stronger evidence for AI assisting quantum methods than for quantum enhancing learned optimizers. The authors highlight limited experimental controls, introduce an M0‑M5 evidence hierarchy, and argue that scaling is a systems problem requiring careful allocation of classical and quantum resources.
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Zero-shot Quantum Neural Architecture Search
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arXiv Machine Learning
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Adaptive directional gradients for parameterised quantum circuits
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arXiv AI
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LLM-Driven Algorithm Design for Quantum Circuit Synthesis based on Binary Decision Diagrams
The paper introduces QuantumEvo, an evolutionary framework that employs a large language model (LLM) to generate heuristics for ordering variables in binary decision diagrams (BDDs) used in reversible quantum circuit synthesis. By searching over heuristic families and directly manipulating BDD variable orderings, QuantumEvo produces the HGA-QE heuristic, which modifies the sifting step of a genetic algorithm to better align with quantum circuit cost (QCC). Across benchmark sets, HGA-QE achieves a 70.9% tie-or-win rate against the best per-function baseline and strictly outperforms it on 13.5% of functions, demonstrating competitive QCC performance and a clear advantage on benchmarks from different data sources.
arXiv Machine Learning
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Neural Guided Sampling for Quantum Circuit Optimization
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