Mitiq: A software package for error mitigation on noisy quantum computers

LaRose, Ryan; Mari, Andrea; Kaiser, Sarah; Karalekas, Peter J.; Alves, Andre A.; Czarnik, Piotr; Mandouh, Mohamed El; Gordon, Max Hunter; Hindy, Yousef; Robertson, Aaron; Shammah, Nathan; Zeng, William J.

doi:10.48550/arxiv.2009.04417

Cited by 23 publications

(39 citation statements)

References 36 publications

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“…In this work we implemented three techniques: zero-noise extrapolation (ZNE) [50], Clifford data regression (CDR) [51] and variable noise Clifford data regression (vnCDR) [52]. We used the open source software package Mitiq [53] to execute these methods. For more details regarding the implementation of these techniques we refer the reader to Appendix G.…”

Section: B Plane Waves On Quantum Hardwarementioning

confidence: 99%

Algebraic Bethe Circuits

Sopena,

Gordon,

García-Martín

et al. 2022

Preprint

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Section: B Plane Waves On Quantum Hardwarementioning

confidence: 99%

Algebraic Bethe Circuits

Sopena,

Gordon,

García-Martín

et al. 2022

Preprint

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“…Error mitigation methods are essential when running computations on noisy near-term quantum devices. One such method, zero-noise extrapolation (ZNE), is a technique that estimates a noiseless value of a result by running a circuit for increasing values of some scale factor that adds noise, and then extrapolating the results back to the zero-noise case [28][29][30][31]. ZNE naturally incorporates both single and batch transforms: a number of common methods used for addition of noise, such as CNOT pair insertion and unitary folding [32], can be implemented as single transforms; we can then implement a batch transform which uses the single transform to create new programs with different amounts of noise, and then compute a function of the results to obtain the mitigated value.…”

Section: Transforms and Noisementioning

confidence: 99%

Quantum computing with differentiable quantum transforms

Matteo¹,

Izaac²,

Bromley³

et al. 2022

Preprint

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We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples across quantum computing (gradient computation, circuit compilation, and error mitigation), and implement them using the transform framework of PennyLane, a software library for differentiable quantum programming. In this framework, the transforms themselves are differentiable and can be parametrized and optimized, which opens up the possibility of improved quantum resource requirements across a spectrum of tasks. I. INTRODUCTIONQuantum machine learning (QML) is a rapidly-growing area of research with great potential. Tools for designing QML algorithms and applications are increasingly being incorporated into open-source quantum software [1][2][3][4][5]. Some of these integrate with classical machine learning tools such as Autograd [6], PyTorch [7], TensorFlow [8], and JAX [9]. The core functionality provided by all of these libraries is automatic differentiation (autodifferentiation) of mathematical functions, which is used to compute the gradients required for model training. The capabilities provided by these libraries have enabled significant advances to be made in classical machine learning, allowing developers and practitioners to focus more on architectures, data, and algorithms, rather than on the technical implementation of computing derivatives.Differentiation is a process that maps a function f (x) to another function, g(x) = ∇f (x), which computes its derivative. Differentiation can thus be viewed as a function transform. Some classical frameworks, such as JAX [9], Dex [10], and functorch [11] make this notion explicit. For example, JAX is branded as a library for transforming numerical functions, and its built-in transforms include just-in-time compilation and automatic vectorization (vmap) in addition to autodifferentiation. The system is extensible and allows users to write their own transforms. Furthermore, these transforms preserve differentiability of whatever they act upon.There are many processes in quantum computing that rely on the idea of transforming a quantum function, most often represented by a quantum circuit. Such quantum transforms take a circuit as input, and return a new, modified circuit as output. This idea, and the underlying functional programming elements, is perhaps most explicit in the Haskell-based language Quipper [12], which contains a built-in, user-extensible system for monad transformers that can be applied to quantum circuits. Such transforms are used most often in the context of quantum compilation or transpilation, wherein gates in a circuit are rewritten in terms of other gates, reordered, or otherwise optimized. Many multipurpose quantum software libraries such as Qiskit [2] and Cirq [13], and the compiler t|ket [14], expose such optimization transforms to the user. However, the concept of quantum transforms extends beyond...

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“…Some projects are more focused on simulation, such as Qualcs [Suzuki et al 2021], others on interacting with quantum computing devices, such as Amazon Braket [ Gonzalez 2021]. Others again are specialized on domain-specific abstractions, such as Pennylane [Bergholm et al 2020] and Tequila [Kottmann et al 2021], or on advanced error mitigation techniques, such as Mitiq [LaRose et al 2021]. Qiskit [Qis 2021] and Cirq [Developers 2021], two large-scale projects backed by IBM and Google, respectively, are perhaps closest to a complete platform, but studying only them would ignore more specialized projects.…”

Section: Selecting Projects To Studymentioning

confidence: 99%

Bugs in Quantum Computing Platforms: An Empirical Study

Paltenghi¹,

Pradel²

2021

Preprint

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The interest in quantum computing is growing, and with it, the importance of software platforms to develop quantum programs. Ensuring the correctness of such platforms is important, and it requires a thorough understanding of the bugs they typically suffer from. To address this need, this paper presents the first in-depth study of bugs in quantum computing platforms. We gather and inspect a set of 223 real-world bugs from 18 open-source quantum computing platforms. Our study shows that a significant fraction of these bugs (39.9%) are quantum-specific, calling for dedicated approaches to prevent and find them. The bugs are spread across various components, but quantum-specific bugs occur particularly often in components that represent, compile, and optimize quantum programming abstractions. Many quantum-specific bugs manifest through unexpected outputs, rather than more obvious signs of misbehavior, such as crashes. Finally, we present a hierarchy of recurrent bug patterns, including ten novel, quantum-specific patterns. Our findings not only show the importance and prevalence bugs in quantum computing platforms, but they help developers to avoid common mistakes and tool builders to tackle the challenge of preventing, finding, and fixing these bugs.

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Mitiq: A software package for error mitigation on noisy quantum computers

Abstract: To test installation, one can run the following. 1 import mitiq 2 mitiq . about () Codeblock 2. Testing installation & viewing package versions.This code prints information about the Mitiq version, versions of installed packages, and installation path.

Cited by 23 publications

References 36 publications

Algebraic Bethe Circuits

Algebraic Bethe Circuits

Quantum computing with differentiable quantum transforms

Bugs in Quantum Computing Platforms: An Empirical Study

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