Artificial neural networks | Quantum programming | Quantum information science

Quantum neural network

Quantum neural networks are computational neural network models which are based on the principles of quantum mechanics. The first ideas on quantum neural computation were published independently in 1995 by Subhash Kak and Ron Chrisley, engaging with the theory of quantum mind, which posits that quantum effects play a role in cognitive function. However, typical research in quantum neural networks involves combining classical artificial neural network models (which are widely used in machine learning for the important task of pattern recognition) with the advantages of quantum information in order to develop more efficient algorithms. One important motivation for these investigations is the difficulty to train classical neural networks, especially in big data applications. The hope is that features of quantum computing such as quantum parallelism or the effects of interference and entanglement can be used as resources. Since the technological implementation of a quantum computer is still in a premature stage, such quantum neural network models are mostly theoretical proposals that await their full implementation in physical experiments. Most Quantum neural networks are developed as feed-forward networks. Similar to their classical counterparts, this structure intakes input from one layer of qubits, and passes that input onto another layer of qubits. This layer of qubits evaluates this information and passes on the output to the next layer. Eventually the path leads to the final layer of qubits. The layers do not have to be of the same width, meaning they don't have to have the same number of qubits as the layer before or after it. This structure is trained on which path to take similar to classical artificial neural networks. This is discussed in a lower section. Quantum neural networks refer to three different categories: Quantum computer with classical data, classical computer with quantum data, and quantum computer with quantum data. (Wikipedia).

Quantum neural network
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Quantum Computing for Beginners | How to get started with Quantum Computing

Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is used to perform such computation, which can be implemented theoretically or physically. The field of quantum computing is actually a sub-field

From playlist Quantum Physics

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What Is Quantum Computing | Quantum Computing Explained | Quantum Computer | #Shorts | Simplilearn

🔥Explore Our Free Courses With Completion Certificate by SkillUp: https://www.simplilearn.com/skillup-free-online-courses?utm_campaign=QuantumComputingShorts&utm_medium=ShortsDescription&utm_source=youtube Quantum computing is a branch of computing that focuses on developing computer tech

From playlist #Shorts | #Simplilearn

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The Map of Quantum Computing | Quantum Computers Explained

An excellent summary of the field of quantum computing. Find out more about Qiskit at https://qiskit.org and their YouTube channel https://www.youtube.com/c/qiskit And get the poster here: https://store.dftba.com/collections/domain-of-science/products/map-of-quantum-computing With this vi

From playlist Quantum Physics Videos - Domain of Science

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Quantum Computer in a Nutshell (Documentary)

The reservoir of possibilities offered by the fundamental laws of Nature, is the key point in the development of science and technology. Quantum computing is the next step on the road to broaden our perspective from which we currently look at the Universe. The movie shows the history of pr

From playlist Quantum computing

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Who Has The Best Quantum Computer?

This is a summary of all the main companies building quantum computers today, and what their most powerful machines are. You can get the digital image here: https://www.flickr.com/photos/95869671@N08/51849762629/in/dateposted-public/ But we can’t simply look at qubits counts because so man

From playlist Quantum Physics Videos - Domain of Science

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Quantum Computers, Explained With Quantum Physics

Quantum computers aren’t the next generation of supercomputers—they’re something else entirely. Before we can even begin to talk about their potential applications, we need to understand the fundamental physics that drives the theory of quantum computing. (Featuring Scott Aaronson, John Pr

From playlist Explainers

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Quantum Mechanics 1.1: Introduction

In this video I provide some motivation behind the development of quantum mechanics, kicking off a new series on everything you've been wondering about quantum mechanics! Twitter: https://twitter.com/SciencePlease_

From playlist Quantum Mechanics

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Quantum Theory - Full Documentary HD

Check: https://youtu.be/Hs_chZSNL9I The World of Quantum - Full Documentary HD http://www.advexon.com For more Scientific DOCUMENTARIES. Subscribe for more Videos... Quantum mechanics (QM -- also known as quantum physics, or quantum theory) is a branch of physics which deals with physica

From playlist TV Appearances

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Neural Network Overview

This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com

From playlist Intro to Data Science

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Masayuki Ohzeki: "Quantum annealing and machine learning - new directions of quantum"

Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Quantum annealing and machine learning - new directions of quantum" Masayuki Ohzeki - Tohoku University Abstract: Quantum annealing is a generic solver of combinator

From playlist Machine Learning for Physics and the Physics of Learning 2019

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Neural networks discovering quantum error (...) - F. Marquardt - PRACQSYS 2018 - CEB T2 2018

Florian Marquardt (Max Planck Institute for the Science of Light, Erlangen, Germany & Physics Department, University of Erlangen-Nuremberg, Erlangen, Germany) / 02.07.2018 Neural networks discovering quantum error correction strategies from scratch Suppose you are given a set of a few qu

From playlist 2018 - T2 - Measurement and Control of Quantum Systems: Theory and Experiments

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Stanford Seminar - Computing with Physical Systems

Peter McMahon, Cornell University June 1, 2022 With conventional digital computing technology reaching its limits, there has been a renaissance in analog computing across a wide range of physical substrates. In this talk I will introduce the concept of Physical Neural Networks [1] and des

From playlist Stanford EE380-Colloquium on Computer Systems - Seminar Series

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Neural Tangent Kernel theory from High Energy Physics by Junyu Liu

PROGRAM NONPERTURBATIVE AND NUMERICAL APPROACHES TO QUANTUM GRAVITY, STRING THEORY AND HOLOGRAPHY (HYBRID) ORGANIZERS: David Berenstein (University of California, Santa Barbara, USA), Simon Catterall (Syracuse University, USA), Masanori Hanada (University of Surrey, UK), Anosh Joseph (II

From playlist NUMSTRING 2022

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Roger Melko Public Lecture: Artificial Intelligence and the Complexity Frontier

In his May 2 public lecture at Perimeter Institute, Roger Melko (Associate Faculty, Perimeter Institute and University of Waterloo) explored how computers have helped humanity solve increasingly complex puzzles, and ask which challenges, if any, only human intuition is equipped to tackle i

From playlist Public Lecture Series

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Maria Kieferova - Training quantum neural networks with an unbounded loss function - IPAM at UCLA

Recorded 27 January 2022. Maria Kieferova of the University of Technology Sydney presents "Training quantum neural networks with an unbounded loss function" at IPAM's Quantum Numerical Linear Algebra Workshop. Abstract: Quantum neural networks (QNNs) are a framework for creating quantum al

From playlist Quantum Numerical Linear Algebra - Jan. 24 - 27, 2022

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Eun-Ah Kim - Machine Learning for Quantum Simulation - IPAM at UCLA

Recorded 15 April 2022. Eun-Ah Kim of Cornell University presents "Machine Learning for Quantum Simulation" at IPAM's Model Reduction in Quantum Mechanics Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-ii-model-reduction-in-quantum-mechanics/?tab=sched

From playlist 2022 Model Reduction in Quantum Mechanics Workshop

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Shi-Ju Ran: "Deep learning quantum states for Hamiltonian predictions"

Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop II: Tensor Network States and Applications "Deep learning quantum states for Hamiltonian predictions" Shi-Ju Ran - Capital Normal University Abstract: Human experts cannot efficiently access the phys

From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021

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Aiichiro Nakano - Quantum Material Dynamics at Nexus of Exascale Computing, AI, & Quantum Computing

Recorded 27 March 2023. Aiichiro Nakano of the University of Southern California presents "Quantum Materials Dynamics at the Nexus of Exascale Computing, Artificial Intelligence, and Quantum Computing" at IPAM's Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale

From playlist 2023 Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing

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Christine Silberhorn: Time-multiplexed quantum walks

Photonic quantum systems, which comprise multiple optical modes, have become an established platform for the experimental implementation of quantum walks. However, the implementation of large systems with many modes, this means for many step operations, a high and dynamic control of many d

From playlist Mathematical Physics

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DDPS | Machine Learning for materials and chemical dynamics by Sergei Tretiak

Machine learning (ML) is quickly becoming a premier tool for modeling chemical processes and materials. Generally, ML provides a surrogate model trained on the dataset of some reference data. This model establishes a relationship between structure and underlying chemical properties, guidin

From playlist Data-driven Physical Simulations (DDPS) Seminar Series

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Qubit | No-cloning theorem | Quantum cognition | Unitary operator | Feedforward neural network | Ancilla bit | Big data | Reversible computing | Quantum phase estimation algorithm | Differentiable programming | Artificial neural network | Unitary matrix | Quantum logic gate | Quantum entanglement | Quantum machine learning | Reservoir computing | Perceptron | Measurement in quantum mechanics | Backpropagation | Quantum information | Fuzzy logic | Optical neural network | Quantum circuit | Quantum computing