Error mitigation with Clifford quantum-circuit data

By Piotr Czarnik, Andrew Arrasmith, Patrick J. Coles, Lukasz Cincio. Quantum, 2020

310 citations recorded by the source corpus.

Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gate-based quantum computers. The method generates training data {Xinoisy,Xiexact} via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where Xinoisy and Xiexact are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8758244c42b1059b3f4f9b06e6cc0b302fa16f0e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Patrick J. Coles
2. M. Cerezo
3. Lukasz Cincio
4. Suguru Endo
5. Simon C. Benjamin
6. Jarrod R. McClean
7. Andrew Arrasmith
8. Xiao Yuan

In Atomic and Molecular Physics, and Optics

1. Patrick J. Coles
2. M. Cerezo
3. Suguru Endo
4. Lukasz Cincio
5. Simon C. Benjamin
6. Xiao Yuan
7. Ying Li
8. Jarrod R. McClean

In Computational Theory and Mathematics

1. Patrick J. Coles
2. Lukasz Cincio
3. Suguru Endo
4. Ying Li
5. T. O’Brien
6. Song Han
7. Xiao Yuan
8. Keisuke Fujii

Citations in the corpus

• Fundamental limits of quantum error mitigation (2021, methodology): “Another example includes the Clifford Data Regression [31], which can employ a linear regression based on a pattern learned from a training set.”
• QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (2021, methodology): “Other methods such as quantum subspace expansion [119] and learning-based mitigation [120], [121] are also proposed.”
• Quantum error mitigation (2022, methodology): “However, we can also obtain ⃗ θ through learning-based methods (Czarnik et al. , 2021; Strikis et al. , 2021) using training circuits.”
• Variational quantum algorithms (2020, methodology): “Alternatively, one can perform a simple regression with this Clifford data to estimate how the observables have been affected and invert this regression to estimate desired noise-free expectation values [212].”