Results 11 to 20 of about 283,757 (265)
Quantum generative adversarial learning [PDF]
Physical Review Letters, 2018 Generative adversarial networks (GANs) represent a powerful tool for classical machine learning: a generator tries to create statistics for data that mimics those of a true data set, while a discriminator tries to discriminate between the true and fake ...
Lloyd, Seth, Weedbrook, Christian
core +6 more sources
Quantum Hamiltonian Learning Using Imperfect Quantum Resources [PDF]
Physical Review A, 2014 Identifying an accurate model for the dynamics of a quantum system is a vexing problem that underlies a range of problems in experimental physics and quantum information theory.
Cory, David G. +3 more
core +2 more sources
Experimental Quantum Hamiltonian Learning [PDF]
Nature Physics, 2017 Efficiently characterising quantum systems, verifying operations of quantum devices and validating underpinning physical models, are central challenges for the development of quantum technologies and for our continued understanding of foundational ...
A Aspuru-Guzik +51 more
core +7 more sources
Quantum machine learning [PDF]
Nature, 2017 Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers
Jacob Biamonte +5 more
openaire +7 more sources
Quantum learning without quantum memory [PDF]
Scientific Reports, 2012 A quantum learning machine for binary classification of qubit states that does not require quantum memory is introduced and shown to perform with the very same error rate as the optimal (programmable) discrimination machine for any size of the training set.
Sentís Herrera, Gael +3 more
openaire +5 more sources
Machine Learning: Science and Technology, 2023 Abstract Quantum machine learning is a rapidly evolving field of research that could facilitate important applications for quantum computing and also significantly impact data-driven sciences. In our work, based on various arguments from complexity theory and physics, we demonstrate that a single Kerr mode can provide some ‘quantum ...
Junyu Liu +7 more
openaire +4 more sources
Quantum circuit learning [PDF]
Physical Review A, 2018 We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit.
Mitarai, K. +3 more
openaire +3 more sources
Learning Temporal Quantum Tomography [PDF]
Physical Review Letters, 2021 Main: 6 pages, 4 figures; Supplementary: 29 pages -> Revised version; Close to the accepted version.
Quoc Hoan Tran, Kohei Nakajima
openaire +3 more sources
Nature Reviews Physics, 2023 The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation and sensing. This poses severe challenges in the efficient control, calibration and validation of quantum states and their dynamics.
Valentin Gebhart +9 more
openaire +3 more sources
Social Sciences, Humanities and Education Journal, 2020 This study aims to determine the increase in self-learning motivation and learning outcomes in students of class VIII-B at State Junior High School 2 Balerejo Madiun Academic Year 2019/2020.
Eni Windarti +2 more
doaj +1 more source