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.
Stability of noisy quantum computing devices.arXiv preprint arXiv:2105.09472
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
A universal superstabilizer method adapts color codes on square lattices to isolated defects in data and ancilla qubits, with optimizations that reuse resources and support Clifford gates plus lattice surgery.
The paper proposes AQCP, an algorithm that provides asymptotic average coverage guarantees for quantum conformal prediction under arbitrary hardware noise by repeated recalibration.
Systematic numerical study of QAOA parameter transfer on heavy-hex Ising models with local cubic terms shows transferred angles from small instances yield improving expectation values up to 49 layers on instances up to 156 qubits, with hardware runs confirming gains up to p=10.
citing papers explorer
-
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.
-
Adaptive Deformation of Color Code in Square Lattices with Defects
A universal superstabilizer method adapts color codes on square lattices to isolated defects in data and ancilla qubits, with optimizations that reuse resources and support Clifford gates plus lattice surgery.
-
Adaptive Conformal Prediction for Quantum Machine Learning
The paper proposes AQCP, an algorithm that provides asymptotic average coverage guarantees for quantum conformal prediction under arbitrary hardware noise by repeated recalibration.
-
Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms
Systematic numerical study of QAOA parameter transfer on heavy-hex Ising models with local cubic terms shows transferred angles from small instances yield improving expectation values up to 49 layers on instances up to 156 qubits, with hardware runs confirming gains up to p=10.