REVIEW 10 cited by
Quantum Error Mitigation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Quantum Error Mitigation
read the original abstract
For quantum computers to successfully solve real-world problems, it is necessary to tackle the challenge of noise: the errors which occur in elementary physical components due to unwanted or imperfect interactions. The theory of quantum fault tolerance can provide an answer in the long term, but in the coming era of `NISQ' machines we must seek to mitigate errors rather than completely remove them. This review surveys the diverse methods that have been proposed for quantum error mitigation, assesses their in-principle efficacy, and then describes the hardware demonstrations achieved to date. We identify the commonalities and limitations among the methods, noting how mitigation methods can be chosen according to the primary type of noise present, including algorithmic errors. Open problems in the field are identified and we discuss the prospects for realising mitigation-based devices that can deliver quantum advantage with an impact on science and business.
Forward citations
Cited by 10 Pith papers
-
Algebraic Speedups for Exact Inversion of Hamiltonian Evolutions
For Hamiltonian families with known generators and hidden parameters, the exact query cost of implementing the inverse is determined by spectral sumset relations and representation-theoretic reduction, yielding polyno...
-
Algebraic Speedups for Exact Inversion of Hamiltonian Evolutions
Known generator structure—additive eigenvalue relations and Wedderburn sector multiplicities—determines and often drastically lowers the exact query cost of reversing a Hamiltonian evolution.
-
Simulating quantum circuits with a neural statebank
A compact neural statebank based on autoregressive Transformers simulates 34-qubit quantum circuits with ~0.01 infidelity using 0.3 million parameters, outperforming tested approximate simulators.
-
Reliable high-accuracy error mitigation for utility-scale quantum circuits
QESEM is a characterization-based error mitigation technique that achieves unbiased estimates with substantially reduced runtime cost compared to probabilistic error cancellation while outperforming zero-noise extrapo...
-
Efficient implementation of randomized quantum algorithms with dynamic circuits
Engineering method using dynamic circuits to generate randomized algorithm distributions on quantum hardware, achieving 14000x speedup for Pauli measurements and enabling 10M-circuit classical shadows on 28-40 qubit h...
-
Robust design under uncertainty in quantum error mitigation
Presents unbiased uncertainty quantification for post-processing error mitigation and applies it to optimize hyperparameters in Zero Noise Extrapolation and Clifford Data Regression under finite-shot noise.
-
Feasibility of performing quantum chemistry calculations on quantum computers
New criteria reveal VQE needs fault-tolerant quantum computers due to decoherence and QPE has exponentially suppressed success probability from orthogonality catastrophe in classical input states.
-
Thermodynamic-limit dispersion relations on trapped-ion quantum hardware
NLCE+QA on trapped-ion QPU computes TFIM thermodynamic-limit energies and dispersions using ASP, VQE, and a new CX-test.
-
Certification and Classification of Linear Quantum Error Mitigation Methods
Introduces metrics, criteria, and taxonomy for linear quantum error mitigation methods with an example strategy for stochastic and rotational errors on characterized hardware, emphasizing precise characterization.
-
Compton Form Factor Extraction using Quantum Deep Neural Networks
Quantum-inspired deep neural networks extract Compton form factors from JLab data with higher predictive accuracy and tighter uncertainties than classical DNNs on pseudodata benchmarks, then applied to real measurements.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.