A Quantum Implementation Model for Artificial Neural Networks

Daskin, Ammar

doi:10.12743/quanta.v7i1.65

“…Another interesting area of research that is likely to grow is asking if and how quantum computers can help improve state-of-the art ML algorithms (Arunachalam and de Wolf, 2017; Benedetti et al , 2016, 2017; Bromley and Rebentrost, 2018; Ciliberto et al , 2017; Daskin, 2018; Innocenti et al , 2018; Mitarai et al , 2018; Perdomo-Ortiz et al , 2017; Rebentrost et al , 2017; Schuld et al , 2017; Schuld and Killoran, 2018; Schuld et al , 2015). Concrete examples that seek to extend some of the basic ideas and methods we introduced in this review to the quantum computing realm include: algorithms for quantum-assisted gradient descent (Kerenidis and Prakash, 2017; Rebentrost et al , 2016), classification (Schuld and Petruccione, 2017), and Ridge regression (Yu et al , 2017).…”

Section: Discussionmentioning

confidence: 99%

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

Mehta

¹

,

Bukov

²

,

Wang

³

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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“…Wurde zu Beginn noch der Vergleich mit einem Räderwerk vorgenommen, folgten später kommunizierende Röhren, dann unter dem Einfluss der Kybernetik der klassische Computer (Eckoldt 2016). Heute orientiert sich die Forschung u. a. an neuronalen Netzen, die im Gehirn zu beobachten sind und versucht sie auf die Entwicklung künstlicher Intelligenz und Quantencomputer zu übertragen (Daskin 2018;Musser 2018;Schempp 1992). Neuronale Netze erscheinen als Datenautobahnen, die je nach Bedarf angepasst werden.…”

Section: Unser Gehirn -Aktuelles Verständnis Und Aufbauunclassified

System-Aufstellungen und ihre naturwissenschaftliche Begründung

Gehlert

¹

2020

Systemaufstellungen in Wissenschaft Und Praxis

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“…Another interesting area of research that is likely to grow is asking if and how quantum computers can help improve state-of-the art ML algorithms (Arunachalam and de Wolf, 2017;Benedetti et al, 2016Benedetti et al, , 2017Bromley and Rebentrost, 2018;Ciliberto et al, 2017;Daskin, 2018;Innocenti et al, 2018;Mitarai et al, 2018;Perdomo-Ortiz et al, 2017;Rebentrost et al, 2017;Schuld and Killoran, 2018;Schuld et al, 2015). Concrete examples that seek to extend some of the basic ideas and methods we introduced in this review to the quantum computing realm include: algorithms for quantum-assisted gradient descent (Kerenidis and Prakash, 2017;Rebentrost et al, 2016), classification , and Ridge regression (Yu et al, 2017).…”

Section: A Research At the Intersection Of Physics And MLmentioning

confidence: 99%

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

Mehta,

Bukov,

Wang

et al. 2018

Preprint

View full text Add to dashboard Cite

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.

show abstract

A Quantum Implementation Model for Artificial Neural Networks

Cited by 6 publications

References 47 publications

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

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

System-Aufstellungen und ihre naturwissenschaftliche Begründung

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

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