Learning-Based Quantum Error Mitigation

Strikis, Armands; Qin, Dayue; Chen, Yanzhu; Benjamin, Simon C.; Li, Ying

doi:10.1103/prxquantum.2.040330

Cited by 130 publications

(55 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(17). For example, the quasi-probability sampling method [3] directly attempts to implement the ideal gates through imperfect ones numerically; and learning-based QEM attempts to implicitly learn the noise models and invert them through regression [19].…”

Section: A Possibilities Of Qem In Quantum Computationmentioning

confidence: 99%

NISQ: Error Correction, Mitigation, and Noise Simulation

Cao¹,

Lin²,

Kribs³

et al. 2021

Preprint

View full text Add to dashboard Cite

Error-correcting codes were invented to correct errors on noisy communication channels. Quantum error correction (QEC), however, may have a wider range of uses, including information transmission, quantum simulation/computation, and fault-tolerance. These invite us to rethink QEC, in particular, about the role that quantum physics plays in terms of encoding and decoding. The fact that many quantum algorithms, especially near-term hybrid quantum-classical algorithms, only use limited types of local measurements on quantum states, leads to various new techniques called Quantum Error Mitigation (QEM). This work addresses the differences and connections between QEC and QEM, by examining different application scenarios. We demonstrate that QEM protocols, which aim to recover the output density matrix, from a quantum circuit do not always preserve important quantum resources, such as entanglement with another party. We then discuss the implications of noise invertibility on the task of error mitigation, and give an explicit construction called quasi-inverse for non-invertible noise, which is trace preserving while the Moore-Penrose pseudoinverse may not be. We also study the consequences of erroneously characterizing the noise channels, and derive conditions when a QEM protocol can reduce the noise.

show abstract

Section: A Possibilities Of Qem In Quantum Computationmentioning

confidence: 99%

NISQ: Error Correction, Mitigation, and Noise Simulation

Cao¹,

Lin²,

Kribs³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, it would be interesting to extend the applicability of our approach to analog quantum simulators. Furthermore, we note that a related, but distinct, approach using Clifford circuits to learn quasi-probabilistic error mitigation was recently proposed [44].…”

Section: Discussionmentioning

confidence: 95%

Error mitigation with Clifford quantum-circuit data

Czarnik

Arrasmith

Coles

et al. 2021

Quantum

168

View full text Add to dashboard Cite

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.

show abstract

“…Similarly to previous works we will assume for simplicity that this assumption holds [18,9]. Some more recent research has shown that cross-correlations can be tackled by using variants of the quasiprobability method which do not rely on tomography to find the optimal quasiprobability coefficients [17].…”

Section: Quasiprobability Samplingmentioning

confidence: 99%

Quasiprobability decompositions with reduced sampling overhead

Piveteau,

Sutter,

Woerner

2021

Preprint

View full text Add to dashboard Cite

Quantum error mitigation techniques can reduce noise on current quantum hardware without the need for fault-tolerant quantum error correction. For instance, the quasiprobability method simulates a noise-free quantum computer using a noisy one, with the caveat of only producing the correct expected values of observables. The cost of this error mitigation technique manifests as a sampling overhead which scales exponentially in the number of corrected gates. In this work, we present two novel approaches to reduce the exponential basis of that overhead. First, we introduce a robust quasiprobability method that allows for a tradeoff between an approximation error and the sampling overhead via semidefinite programming. Second, we derive a new algorithm based on mathematical optimization that aims to choose the quasiprobability decomposition in a noise-aware manner. Both techniques lead to a significantly lower overhead compared to existing approaches.

show abstract

Learning-Based Quantum Error Mitigation

Cited by 130 publications

References 28 publications

NISQ: Error Correction, Mitigation, and Noise Simulation

NISQ: Error Correction, Mitigation, and Noise Simulation

Error mitigation with Clifford quantum-circuit data

Quasiprobability decompositions with reduced sampling overhead

Contact Info

Product

Resources

About