Dynamic control flow in large-scale machine learning

Yuan, Yu; Abadi, Martı́n; Barham, Paul; Brevdo, Eugene; Burrows, Mike; Davis, Andy; Dean, Jeff; Ghemawat, Sanjay; Harley, Tim; Hawkins, Peter; Isard, Michael; Kudlur, Manjunath; Monga, Rajat; Murray, Derek G.; Zheng, Xiaoqiang

doi:10.1145/3190508.3190551

Cited by 81 publications

(49 citation statements)

References 20 publications

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“…In contrast to quantum full-stack libraries that focus on the discrete gate model, Strawberry Fields is a Pythonbased library for the continuous gate model, developed by the startup Xanadu [63,64]. It is based on the Blackbird quantum programming language and it is the only quantum software project built on top of a deep learning library: its computational backend for simulations is written in TensorFlow [65]. Strawberry Fields' repository contains example implementations of quantum algorithms, including quantum teleportation, boson sampling and several quantum machine learning algorithms.…”

Section: Projects Consideredmentioning

confidence: 99%

“…An example is machine learning: fairly complex mathematical models must be tested and deployed on hardware that is difficult to program and use to its full potential, for instance, on graphical processing units. By providing high-quality open source frameworks, such as TensorFlow [65] or PyTorch [? ], the commercial entities attract the best developers towards their ecosystem.…”

Section: Introductionmentioning

confidence: 99%

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Open source software in quantum computing

2018

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Open source software is becoming crucial in the design and testing of quantum algorithms. Many of the tools are backed by major commercial vendors with the goal to make it easier to develop quantum software: this mirrors how well-funded open machine learning frameworks enabled the development of complex models and their execution on equally complex hardware. We review a wide range of open source software for quantum computing, covering all stages of the quantum toolchain from quantum hardware interfaces through quantum compilers to implementations of quantum algorithms, as well as all quantum computing paradigms, including quantum annealing, and discrete and continuous-variable gate-model quantum computing. The evaluation of each project covers characteristics such as documentation, licence, the choice of programming language, compliance with norms of software engineering, and the culture of the project. We find that while the diversity of projects is mesmerizing, only a few attract external developers and even many commercially backed frameworks have shortcomings in software engineering. Based on these observations, we highlight the best practices that could foster a more active community around quantum computing software that welcomes newcomers to the field, but also ensures high-quality, well-documented code.

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Section: Projects Consideredmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Open source software in quantum computing

2018

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“…For example, for the derivative of a conditional expression if B then M 1 else M 2 , it would output if B then N 1 else N 2 , where N 1 and N 2 are the derivatives of M 1 and M 2 respectively. This approach is employed, for instance, in Theano [Bergstra et al 2010], Ten-sorFlow 1.0 [Abadi et al 2016a;Yu et al 2018], and Tangent [van Merrienboer et al 2018]. • Another approach relies on tracing, typically eliminating control structures to produce a simpler form of code, which we call an execution trace, that can more easily be differentiated.…”

Section: Introductionmentioning

confidence: 99%

A simple differentiable programming language

Abadi

Plotkin

2019

Proc. ACM Program. Lang.

Self Cite

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Automatic differentiation plays a prominent role in scientific computing and in modern machine learning, often in the context of powerful programming systems. The relation of the various embodiments of automatic differentiation to the mathematical notion of derivative is not always entirely clear-discrepancies can arise, sometimes inadvertently. In order to study automatic differentiation in such programming contexts, we define a small but expressive programming language that includes a construct for reverse-mode differentiation. We give operational and denotational semantics for this language. The operational semantics employs popular implementation techniques, while the denotational semantics employs notions of differentiation familiar from real analysis. We establish that these semantics coincide.

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“…Lately, optimal control techniques have been used with great success. The "differentiable programming" toolkits [45] that have enabled the rapid development of STEADY is readily applicable to the reverse problem of optimal control [47]: we "freeze" the model parameters and now optimize with respect to the control drives, while the minimization target is not the distance between a measured and predicted state, but rather between the desired and predicted state. However, in the context of our work, there is a more exciting application of optimal control, that would permit parameter estimation at much lower resource/time cost.…”

Section: Optimal Control and Experimental Designmentioning

confidence: 99%

“…We briefly discuss the effects of parameter drift, non unitary errors, and nonlinearities in the Hamiltonian (as a function of the control pulses). Together with this manuscript we also provide a software package based on a popular differentiable programming framework [45] that implements our techniques for various models including unitary or non-unitary evolution.…”

Section: Introductionmentioning

confidence: 99%

Stochastic estimation of dynamical variables

Krastanov

Zhou

Flammia

et al. 2019

Quantum Sci. Technol.

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Estimating the parameters governing the dynamics of a system is a prerequisite for its optimal control. We present a simple but powerful method that we call STEADY, for STochastic Estimation Algorithm for DYnamical variables, to estimate the Hamiltonian (or Lindbladian) governing a quantum system of a few qubits. STEADY makes efficient use of all measurements and its performance scales as the information-theoretic limits for such an estimator. Importantly, it is inherently robust to state preparation and measurement errors. It is not limited to evaluating only a fixed set of possible gates, rather it estimates the complete Hamiltonian of the system. The estimator is applicable to any Hamiltonian that can be written as a piecewise-differentiable function and it can easily include estimators for the non-unitary parameters as well. At the heart of our approach is a stochastic gradient descent over the difference between experimental measurement and model prediction.A common task in physics and engineering is the control of a system, where the control pulses sent to the system pass through a complex transfer function before they effect a useful change to the state of the system. There are two overarching prerequisites for good control: learning the dynamical law that governs the system (the goal of disciplines like experimental design and parameter estimation) and, consecutively, the derivation of control pulses for the given system (broadly covered by optimal control theory). Advances in these areas are crucial for applications in quantum information science, where the precise control of well-characterized quantum systems will form the basis for quantum computers.Here we present STEADY, a conceptually simple but performant method for approaching the parameter estimation problem for dynamical variables. We can model a piece of quantum hardware with a Hamiltonian (or Lindbladian)H(ω; d) which depends on the parameters to be estimated ω and on the control pulses d(t). Our goal becomes finding the value for ω that leads to anH that (for any value of the control pulses d) most closely mimics the dynamical law H governing the real hardware. As is commonly done in parameter estimation, we do this by searching for a value of ω that minimizes some measure of distance betweenH and H.Our contribution follows in the rich traditions of stochastic methods and compressed sensing: instead of performing full process tomography on the hardware which would be extremely time consuming, we run a relatively small number of random control pulses on it and study its response. For each control pulse we sample the final state of the system (for instance by projectively measuring the qubits in the computational basis). We then estimate the difference between this experimental measurement and the prediction based on theH(ω) model. This measure of "difference" is stochastic, as it uses only a small finite sample of possible control drives.This leads to a number of properties that make STEADY perform particularly well. First, the distance measur...

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Dynamic control flow in large-scale machine learning

Cited by 81 publications

References 20 publications

Open source software in quantum computing

Open source software in quantum computing

A simple differentiable programming language

Stochastic estimation of dynamical variables

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