Publications related to Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Elisabetta Cornacchia

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits.In the ...

EPFL2023

Learning curves for the multi-class teacher-student perceptron

Lenka Zdeborová, Elisabetta Cornacchia, Bruno Loureiro, Bruno Loureiro, Francesca Mignacco

One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher-student perceptron on i.i.d. Gaussian inputs. Both Bayes-optimal (BO) ...

IOP Publishing Ltd2023

Self-correcting quantum many-body control using reinforcement learning with tensor networks

Friederike Metz

Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems an ...

NATURE PORTFOLIO2023

Benign Overfitting in Deep Neural Networks under Lazy Training

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu

This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayesoptimal test error for classification while obtaining (nearly) zero-trai ...

2023

Quantum process tomography with unsupervised learning and tensor networks

Giuseppe Carleo

The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from measurement data, r ...

NATURE PORTFOLIO2023

Network Classifiers Based On Social Learning

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...

IEEE2021

Scaling description of generalization with number of parameters in deep learning

Matthieu Wyart, Clément Hongler, Mario Geiger, Stefano Spigler, Levent Dogus Sagun, Franck Raymond Gabriel, Arthur Jacot

Supervised deep learning involves the training of neural networks with a large number N of parameters. For large enough N, in the so-called over-parametrized regime, one can essentially fit the training data points. Sparsitybased arguments would suggest th ...

IOP PUBLISHING LTD2020

Adaptive hot water production based on Supervised Learning

Dolaana Khovalyg, Amirreza Heidari

A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

2020

A quantum-implementable neural network model

Edoardo Charbon, Lingli Wang

A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this m ...

2017

Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Learning curves for the multi-class teacher-student perceptron

Self-correcting quantum many-body control using reinforcement learning with tensor networks

Benign Overfitting in Deep Neural Networks under Lazy Training

Quantum process tomography with unsupervised learning and tensor networks

Network Classifiers Based On Social Learning

Scaling description of generalization with number of parameters in deep learning

Adaptive hot water production based on Supervised Learning

A quantum-implementable neural network model

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Quantum process tomography with unsupervised learning and tensor networks

Network Classifiers Based On Social Learning

Adaptive hot water production based on Supervised Learning

Learning curves for the multi-class teacher-student perceptron

Self-correcting quantum many-body control using reinforcement learning with tensor networks

Benign Overfitting in Deep Neural Networks under Lazy Training

Scaling description of generalization with number of parameters in deep learning

A quantum-implementable neural network model