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 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.
A quantum algorithm for evolving Schwarzschild spacetime in the WEBB NR formalism is implemented in Qiskit and tested on simulators and IBM quantum computers.
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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.