Hybrid quantum walks with optimal dynamical coin operators outperform QAOA on Max-Cut and MIS by accessing a strictly larger Jordan-Lie algebra that enables faster convergence and higher accuracy.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Divide-and-conquer QAOA samples and Hamming-weight-conditioned neural network surrogates accelerate MCMC mixing for constrained Ising problems by average factors of 20.3 and 7.6 over classical pair-flip baselines.
QUEST is a new adaptive framework for quantum state engineering that constructs states one Pauli rotation at a time to satisfy multiple expectation-value targets simultaneously.
A hybrid quantum framework decomposes CVRP into bounded-width knapsack subproblems, trains a reinforcement learning controller for Lagrangian multipliers, and uses a contextual bandit to adapt quantum hardware execution, yielding improved routing quality on standard test instances.
Variational compression of Trotterized circuits preserves reaction rate coefficients in nonadiabatic dynamics simulations while reducing circuit depth.
citing papers explorer
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Beyond Single Trajectories: Optimal Control and Jordan-Lie Algebra in Hybrid Quantum Walks for Combinatorial Optimization
Hybrid quantum walks with optimal dynamical coin operators outperform QAOA on Max-Cut and MIS by accessing a strictly larger Jordan-Lie algebra that enables faster convergence and higher accuracy.
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Divide-and-Conquer Neural Network Surrogates for Quantum Sampling: Accelerating Markov Chain Monte Carlo in Large-Scale Constrained Optimization Problems
Divide-and-conquer QAOA samples and Hamming-weight-conditioned neural network surrogates accelerate MCMC mixing for constrained Ising problems by average factors of 20.3 and 7.6 over classical pair-flip baselines.
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Quantum State Engineering Under Multiple Expectation-Value Constraints
QUEST is a new adaptive framework for quantum state engineering that constructs states one Pauli rotation at a time to satisfy multiple expectation-value targets simultaneously.
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Qubit-Scalable CVRP via Lagrangian Knapsack Decomposition and Noise-Aware Quantum Execution
A hybrid quantum framework decomposes CVRP into bounded-width knapsack subproblems, trains a reinforcement learning controller for Lagrangian multipliers, and uses a contextual bandit to adapt quantum hardware execution, yielding improved routing quality on standard test instances.
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Variationally Compressing Quantum Circuits to Approximate Nonadiabatic Molecular Quantum Dynamics
Variational compression of Trotterized circuits preserves reaction rate coefficients in nonadiabatic dynamics simulations while reducing circuit depth.