Fundamental limits of quantum error mitigation

Takagi, Ryuji; Endo, Suguru; Minagawa, Shintaro; Gu, Mile

doi:10.1038/s41534-022-00618-z

Cited by 75 publications

(27 citation statements)

References 67 publications

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“…QEM aims to produce accurate expectation values of observables. It refers to various software methods to alleviate the effects of noise on computational results during execution of an algorithm on a QPU [27,124,[157][158][159][160][161][162].…”

Section: Quantum Error Mitigationmentioning

confidence: 99%

“…QEM effectively involves creating a noisy distribution of results, and then extracting the desired quantum information via post-processing. Currently, the main principles are: zero noise extrapolation (ZNE) [27,[157][158][159], and probabilistic error cancellation (PEC) [157][158][159][160][161][162].…”

Section: Quantum Error Mitigationmentioning

confidence: 99%

“…Probabilistic error cancellation (PEC) works by measuring the noise spectrum and applying an inverted quasi-probability distribution to the result of the computation via post-processing [157][158][159][160][161][162]. PEC requires a full characterization of the noisy computational operations.…”

Section: Quantum Error Mitigationmentioning

confidence: 99%

See 2 more Smart Citations

Quantum information processing with superconducting circuits: a perspective

Wendin¹

2023

Preprint

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The last five years have seen a dramatic evolution of platforms for quantum computing, taking the field from physics experiments to quantum hardware and software engineering. Nevertheless, despite this progress of quantum processors, the field is still in the noisy intermediate-scale quantum (NISQ) regime, seriously limiting the performance of software applications. Key issues involve how to achieve quantum advantage in useful applications for quantum optimization and materials science, connected to the concept of quantum supremacy first demonstrated by Google in 2019. In this article we will describe recent work to establish relevant benchmarks for quantum supremacy and quantum advantage, present recent work on applications of variational quantum algorithms for optimization and electronic structure determination, discuss how to achieve practical quantum advantage, and finally outline current work and ideas about how to scale up to competitive quantum systems.

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Section: Quantum Error Mitigationmentioning

confidence: 99%

Section: Quantum Error Mitigationmentioning

confidence: 99%

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Quantum information processing with superconducting circuits: a perspective

Wendin¹

2023

Preprint

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“…Approaches to realizing FTQC are expected to require error mitigation techniques 15 (e.g. [97][98][99][100][101][102][103][104][105][106][107][108][109]) to bring hardware error rates below a fault-tolerance threshold [15]. Below this threshold, it may be possible to use error correcting codes (e.g.…”

Section: Logical Versus Noisy Qubitsmentioning

confidence: 99%

“…For example, a variety of strategies to enhance their capabilities have been leveraged in experiments. Among these strategies are error mitigation [97][98][99][100][101][103][104][105][106][107][108], post-processing to improve accuracy [306,307], approaches to reducing the number of quantum circuit evaluations [150,308,309] and approaches to dynamically modify the quantum circuits [143]. It is anticipated that these types of practical enhancements, among others, will be crucial to realizing many empirical quantum advantages in the near term both within and outside the space of quantum simulation problems.…”

Section: Variational Quantum Eigensolvermentioning

confidence: 99%

Biology and medicine in the landscape of quantum advantages

Cordier

Sawaya

Guerreschi

et al. 2022

J. R. Soc. Interface.

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Quantum computing holds substantial potential for applications in biology and medicine, spanning from the simulation of biomolecules to machine learning methods for subtyping cancers on the basis of clinical features. This potential is encapsulated by the concept of a quantum advantage, which is contingent on a reduction in the consumption of a computational resource, such as time, space or data. Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.

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Purity‐Assisted Zero‐Noise Extrapolation for Quantum Error Mitigation

Jin,

Shi,

Wang

et al. 2024

Adv Quantum Tech

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Quantum error mitigation aims to reduce errors in quantum systems and improve accuracy. Zero‐noise extrapolation (ZNE) is a commonly used method, where noise is amplified, and the target expectation is extrapolated to a noise‐free point. However, ZNE relies on assumptions about error rates based on the error model. In this study, a purity‐assisted zero‐noise extrapolation (pZNE) method is utilized to address limitations in error rate assumptions and enhance the extrapolation process. The pZNE is based on the Pauli diagonal error model implemented using the Pauli twirling technique. Although this method does not significantly reduce the bias of routine ZNE, it extends its effectiveness to a wider range of error rates where routine ZNE may face limitations. In addition, the practicality of the pZNE method is verified through numerical simulations and experiments on the online quantum computation platform,Quafu. Comparisons with routine ZNE and virtual distillation methods show that biases in extrapolation methods increase with error rates and may become divergent at high error rates. The bias of pZNE is slightly lower than routine ZNE, while its error rate threshold surpasses that of routine ZNE. Furthermore, for full density matrix information, the pZNE method is more efficient than the routine ZNE.

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Fundamental limits of quantum error mitigation

Cited by 75 publications

References 67 publications

Quantum information processing with superconducting circuits: a perspective

Quantum information processing with superconducting circuits: a perspective

Biology and medicine in the landscape of quantum advantages

Purity‐Assisted Zero‐Noise Extrapolation for Quantum Error Mitigation

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