SNT merges SV and PEC for subspace-tailored error mitigation in Trotterized FHM simulations, mapping out optimal combinations by hardware quality and shot budget while quantifying when noisy devices could surpass classical methods.
Qualifying quantum approaches for hard industrial optimization problems. a case study in the field of smart- charging of electric vehicles
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Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
A hybrid optimal-control-plus-contextual-RL framework learns low-dimensional residual pulse corrections that preserve high-fidelity controlled-phase gates on two qutrits under realistic static model mismatch.
citing papers explorer
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Near-Term Fermionic Simulation with Subspace Noise Tailored Quantum Error Mitigation
SNT merges SV and PEC for subspace-tailored error mitigation in Trotterized FHM simulations, mapping out optimal combinations by hardware quality and shot budget while quantifying when noisy devices could surpass classical methods.
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Neural-powered unit disk graph embedding: qubits connectivity for some QUBO problems
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
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Reinforcement Learning for Robust Calibration of Multi-Qudit Quantum Gates
A hybrid optimal-control-plus-contextual-RL framework learns low-dimensional residual pulse corrections that preserve high-fidelity controlled-phase gates on two qutrits under realistic static model mismatch.