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.
Can error mitigation improve trainability of noisy variational quantum algorithms?
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
quant-ph 4verdicts
UNVERDICTED 4representative citing papers
Presents unbiased uncertainty quantification for post-processing error mitigation and applies it to optimize hyperparameters in Zero Noise Extrapolation and Clifford Data Regression under finite-shot noise.
Continuous-variable photonic platform with 20,000-mode cluster state simulates advection transport equation, achieving relative errors of 0.8% and 0.92% on first- and second-order moments via homodyne readout.
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.
citing papers explorer
-
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.
-
Robust design under uncertainty in quantum error mitigation
Presents unbiased uncertainty quantification for post-processing error mitigation and applies it to optimize hyperparameters in Zero Noise Extrapolation and Clifford Data Regression under finite-shot noise.
-
Analog photonic simulator for large-scale transport
Continuous-variable photonic platform with 20,000-mode cluster state simulates advection transport equation, achieving relative errors of 0.8% and 0.92% on first- and second-order moments via homodyne readout.
-
Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.