Artificial neural networks | Quantum programming | Quantum information science
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 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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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