From the 2 of 9 linked papers with an AI index.
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From Circuits to Hardware: Benchmarking Standard and Qubit-Efficient Quantum Optimization on Real Hardware
Monit Sharma, Hoong Chuin Lau
Despite rapid progress in quantum optimization, broad real-hardware benchmarks comparing multiple algorithmic families across diverse combinatorial problems under a common protocol…
AutoQResearch: LLM-Guided Closed-Loop Policy Search for Adaptive Variational Quantum Optimization
Monit Sharma, Hoong Chuin Lau
The paper introduces AutoQResearch, a framework that uses large language models to autonomously search for adaptive policies that configure variational quantum algorithms for combi…
Diagnosing Simulation and Hardware Barriers to Cross-Size Transfer in Equivariant Quantum Reinforcement Learning
Monit Sharma, Hoong Chuin Lau
Equivariant quantum circuits (EQCs) parameterise reinforcement-learning policies for combinatorial optimisation with a size-independent parameter count, suggesting policies trained…
Qubit-Scalable CVRP via Lagrangian Knapsack Decomposition and Noise-Aware Quantum Execution
Monit Sharma, Hoong Chuin Lau
Hybrid quantum optimization for vehicle routing faces a practical bottleneck: direct QUBO encodings of CVRP quickly exceed near-term qubit and gate budgets, while quantum evaluatio…
Cutting Slack: Quantum Optimization with Slack-Free Methods for Combinatorial Benchmarks
Monit Sharma, Hoong Chuin Lau
Constraint handling remains a key bottleneck in quantum combinatorial optimization. While slack-variable-based encodings are straightforward, they significantly increase qubit coun…
Adaptive Graph Shrinking for Quantum Optimization of Constrained Combinatorial Problems
Monit Sharma, Hoong Chuin Lau
A range of quantum algorithms, especially those leveraging variational parameterization and circuit-based optimization, are being studied as alternatives for solving classically in…