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Advances in Quantum Machine Learning and Deep Learning for Image Classification: A Survey

Ruba Kharsa, Ahmed Bouridane, Abbes Amira

NeurocomputingSep 28, 202356 citations

Addressing the interpretability problem for deep learning using many valued quantum logic

Swapnil Nitin Shah

Deep learning models are widely used for various industrial and scientific applications. Even though these models have achieved considerable success in recent years, there exists a lack of understanding of the rationale behind decisions made by such systems in the machine learning community. This problem of interpretability is further aggravated by the increasing complexity of such models. This paper utilizes concepts from machine learning, quantum computation and quantum field theory to demonstrate how a many valued quantum logic system naturally arises in a specific class of generative deep learning models called Convolutional Deep Belief Networks. It provides a robust theoretical framework for constructing deep learning models equipped with the interpretability of many valued quantum logic systems without compromising their computing efficiency.Deep learning models are widely used for various industrial and scientific applications. Even though these models have achieved considerable success in recent years, there exists a lack of understanding of the rationale behind decisions made by such systems in the machine learning community. This problem of interpretability is further aggravated by the increasing complexity of such models. This paper utilizes concepts from machine learning, quantum computation and quantum field theory to demonstrate how a many valued quantum logic system naturally arises in a specific class of generative deep learning models called Convolutional Deep Belief Networks. It provides a robust theoretical framework for constructing deep learning models equipped with the interpretability of many valued quantum logic systems without compromising their computing efficiency.

arXiv:2007.01819Jul 2, 20201 citation

Introduction to Quantum Machine Learning

Nikhil Gupta, Mr. VIGNESHWAR RANGINI, Mr. Bhushan +1 more

Jan 30, 20260 citations

An Introduction to Quantum Machine Learning Algorithms

Rongji Li, Juan Xu, Jiabin Yuan +1 more

Advances in intelligent systems and computingJul 1, 20206 citations

Modern applications of machine learning in quantum sciences

Anna Dawid, Julian Arnold, Borja Requena +26 more

In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.

arXiv:2204.04198Cambridge University Press eBooksApr 8, 202263 citations

A Gentle Introduction to Quantum Machine Learning

Yuxuan Du, Xinbiao Wang, Naixu Guo +6 more

Springer eBooksJan 1, 20256 citations

An introduction to quantum machine learning: from quantum logic to quantum deep learning

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