Development and experimental implementation of noise characterisation techniques on a superconducting quantum processor
quant-ph
Submitted: 2026-10-13
Updated: 2026-10-13
Code: https://github.com/Algorithmiq/TensorNetworksNoiseLearning
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Fermionic dynamics on a trapped-ion quantum computer beyond exact classical simulation
- Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor
- Resolving Structure in Prethermal Floquet Dynamics with Precision Quantum Computation
- Observable Estimation in the Absence of Classical Verification
- Scalability of quantum error mitigation techniques: from utility to advantage
- Myths around quantum computation before full fault tolerance: What no-go theorems rule out and what they don't
- Scalable tensor-network error mitigation for near-term quantum computing
- Characterization of Unlearnable Noise with Mid-Circuit-Measurement-Based Cycle Benchmarking
- Efficient learning of Sparse Pauli Lindblad models for fully connected qubit topology
- Better Pauli Channel Learning with Maximum Likelihood Estimation
- Adam: A Method for Stochastic Optimization
- Scalable quantum circuit generation for iterative ground state approximation using Majorana Propagation
- Enhanced observable estimation through classical optimization of informationally over-complete measurement data -- beyond classical shadows
- Fault-Tolerant Logical Clifford Gates from Code Automorphisms
- Benchmarking Characterization Methods for Noisy Quantum Circuits
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