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.
Improved simulation of stabilizer circuits.Physical Review A—Atomic, Molecular, and Optical Physics, 70(5):052328, 2004
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Optimal O(2^n) algorithms materialize n-qubit stabilizer state vectors and Clifford matrices from quadratic-form and check-matrix descriptions without extra polynomial factors.
Bra-ket entanglement indicates a shift from coherence-dominated to magic-dominated entanglement generation as its value increases.
citing papers explorer
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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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Optimal algorithms for materializing stabilizer states and Clifford gates from compact descriptions
Optimal O(2^n) algorithms materialize n-qubit stabilizer state vectors and Clifford matrices from quadratic-form and check-matrix descriptions without extra polynomial factors.
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Bra-ket entanglement, an indicator bridging entanglement, magic, and coherence
Bra-ket entanglement indicates a shift from coherence-dominated to magic-dominated entanglement generation as its value increases.