Generating three-qubit quantum circuits with neural networks

Swaddle, Michael; Noakes, Lyle; Smallbone, Harry; Salter, L A; Wang, Jingbo

doi:10.1016/j.physleta.2017.08.043

Cited by 25 publications

(70 citation statements)

References 15 publications

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“…These are parametric non-linear models which play a prominent role in many machine learning tasks, such as dimensionality reduction, classification, and feature extraction [50,51]. NNs have also recently proven useful for several problems in quantum many-body theory [52][53][54][55][56][57][58][59], quantum compilation [60], quantum stabilizer codes [61] and entanglement quantification [62].…”

Section: Appendix B Supervised Learning Approachmentioning

confidence: 99%

Supervised learning of time-independent Hamiltonians for gate design

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et al. 2019

Quantum Information and Measurement (QIM) V: Quantum Technologies

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We present a general framework to tackle the problem of finding time-independent dynamics generating target unitary evolutions. We show that this problem is equivalently stated as a set of conditions over the spectrum of the time-independent gate generator, thus translating the task into an inverse eigenvalue problem. We illustrate our methodology by identifying suitable time-independent generators implementing Toffoli and Fredkin gates without the need for ancillae or effective evolutions. We show how the same conditions can be used to solve the problem numerically, via supervised learning techniques. In turn, this allows us to solve problems that are not amenable, in general, to direct analytical solution, providing at the same time a high degree of flexibility over the types of gate-design problems that can be approached. As a significant example, we find generators for the Toffoli gate using only diagonal pairwise interactions, which are easier to implement in some experimental architectures. To showcase the flexibility of the supervised learning approach, we give an example of a non-trivial four-qubit gate that is implementable using only diagonal, pairwise interactions.

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Section: Appendix B Supervised Learning Approachmentioning

confidence: 99%

Supervised learning of time-independent Hamiltonians for gate design

Innocenti

Banchi

Ferraro

et al. 2019

Quantum Information and Measurement (QIM) V: Quantum Technologies

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“…Machine learning techniques involving neural networks were also used to study quantum and fault-tolerant error correction (Baireuther et al , 2017; Breuckmann and Ni, 2017; Chamberland and Ronagh, 2018; Davaasuren et al , 2018; Krastanov and Jiang, 2017; Maskara et al , 2018), estimate rates of coherent and incoherent quantum processes (Greplova et al , 2017), to obtain spectra of 1 /f -noise in spin-qubit devices (Zhang and Wang, 2018), and the recognition of state and charge configurations and auto-tuning in quantum dots (Kalantre et al , 2017). In quantum information theory, it has been shown that one can perform gate decompositions with the help of neural nets (Swaddle et al , 2017). In lattice quantum chromodynamics, DNNs have been used to learn action parameters in regions of parameter space where principal component analysis fails (Shanahan et al , 2018).…”

Section: An Introduction To Feed-forward Deep Neural Network (Dnns)mentioning

confidence: 99%

A high-bias, low-variance introduction to Machine Learning for physicists

et al. 2019

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Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, generalization, and gradient descent before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists may be able to contribute.

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“…It may not be obvious how to optimize algorithms for a given connectivity and a given gate set. This motivates the idea of an automated approach for discovering and optimizing quantum algorithms [6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Learning the quantum algorithm for state overlap

et al. 2018

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Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap rs ( ) Tr between two quantum states ρ and σ. The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to ρ=σ, quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error-compared to the Swap Test-on these computers.

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Generating three-qubit quantum circuits with neural networks

Cited by 25 publications

References 15 publications

Supervised learning of time-independent Hamiltonians for gate design

Supervised learning of time-independent Hamiltonians for gate design

A high-bias, low-variance introduction to Machine Learning for physicists

Learning the quantum algorithm for state overlap

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