CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
A survey of monte carlo tree search methods.IEEE Transactions on Computational Intelligence and AI in games, 4(1):1–43
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A new method decomposes property differences between weakly related molecules into minimal chemical edits to train a directional evaluator that guides multi-step optimization with less oracle querying.
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Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
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From Single-Step Edit Response to Multi-Step Molecular Optimization
A new method decomposes property differences between weakly related molecules into minimal chemical edits to train a directional evaluator that guides multi-step optimization with less oracle querying.