Introduces forward-assisted purification via a new spatiotemporal framework that outperforms conventional static purification by up to 50x in copy efficiency and circumvents no-purification theorems for Bell states.
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8 Pith papers cite this work, alongside 636 external citations. Polarity classification is still indexing.
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Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
Derives MSE bounds for PEC and CDR under finite shots, revealing CDR-dominant windows scaling as 1/(δ₁²p) and a projection theorem for affine CDR bias removal.
Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.
A residual neural network trained on one quantum device's noise data can be fine-tuned with 20 samples from a second device to improve prediction of ideal circuit outputs, recovering 34.9% of the performance gap.
HAVQDS achieves higher approximation ratios on 6-14 qubit SK instances than adiabatic or CD methods while cutting CNOT counts by 1-2 orders of magnitude.
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.
A simulation-derived phenomenological model optimizes the trade-off between quantum circuit size and iteration count to minimize total gate operations for a desired accuracy in noisy VQE algorithms.
citing papers explorer
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Forward-Assisted Purification: A Spatiotemporal Framework Beyond Conventional Limits
Introduces forward-assisted purification via a new spatiotemporal framework that outperforms conventional static purification by up to 50x in copy efficiency and circumvents no-purification theorems for Bell states.
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The finite-shot help-harm boundary of zero-noise extrapolation
Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
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Finite-shot operating windows for probabilistic error cancellation and Clifford data regression
Derives MSE bounds for PEC and CDR under finite shots, revealing CDR-dominant windows scaling as 1/(δ₁²p) and a projection theorem for affine CDR bias removal.
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Certified Finite-Shot Operating Windows for Virtual Distillation and Symmetry Verification
Proves finite-shot mean-squared-error laws for virtual distillation and symmetry verification that define certified operating windows and a selection trichotomy for their comparison.
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Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
A residual neural network trained on one quantum device's noise data can be fine-tuned with 20 samples from a second device to improve prediction of ideal circuit outputs, recovering 34.9% of the performance gap.
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Hybrid Real-Imaginary Time Evolution for Low-Depth Hamiltonian Simulation in Quantum Optimization
HAVQDS achieves higher approximation ratios on 6-14 qubit SK instances than adiabatic or CD methods while cutting CNOT counts by 1-2 orders of magnitude.
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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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Optimizing resource allocation for accuracy in noisy variational quantum algorithms
A simulation-derived phenomenological model optimizes the trade-off between quantum circuit size and iteration count to minimize total gate operations for a desired accuracy in noisy VQE algorithms.