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Out-of-distribution generalization for learning quantum dynamics [article]

Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell, Joe Gibbs, Andrew T. Sornborger, Lukasz Cincio, Patrick J. Coles, Zoë Holmes
2022 arXiv   pre-print
In this work, we prove out-of-distribution generalization for the task of learning an unknown unitary using a QNN and for a broad class of training and testing distributions.  ...  Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are assumed to be drawn from the same data distribution.  ...  NUMERICAL RESULTS Here we provide numerical evidence to support our analytical results showing that out-of-distribution generalization is possible for the learning of quantum dynamics.  ... 
arXiv:2204.10268v1 fatcat:dhd5k32yungavdbns35zhgwyoa

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

Marcello Benedetti, John Realpe-Gómez, Rupak Biswas, Alejandro Perdomo-Ortiz
2017 Physical Review X  
Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related  ...  Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions.  ...  ACKNOWLEDGEMENTS This work was supported in part by the AFRL Information Directorate under grant F4HBKC4162G001, the Office of the Director of National Intelligence (ODNI), and the Intelligence Advanced  ... 
doi:10.1103/physrevx.7.041052 fatcat:466473a5nzaz3gubtfq5pigyay

Learning quantum dynamics with latent neural ODEs [article]

Matthew Choi, Daniel Flam-Shepherd, Thi Ha Kyaw, Alán Aspuru-Guzik
2022 arXiv   pre-print
It can learn to generate such measurement data and extrapolate outside of its training region that satisfies the von Neumann and time-local Lindblad master equations for closed and open quantum systems  ...  Additionally, we show that trajectories that are generated from the QNODE that are close in its latent space have similar quantum dynamics while preserving the physics of the training system.  ...  DISCUSSION In this work, we propose the latent neural ODE for quantum dynamics: QNODE which is capable of learning and generating quantum trajectories of closed and open quantum systems without any prior  ... 
arXiv:2110.10721v2 fatcat:h44pvy4apvamrkl65x3f3kk36y

Experimental quantum hamiltonian learning using a silicon photonic chip and a nitrogen-vacancy electron spin in diamond

Stefano Paesani, Jianwei Wang, Raffaele Santagati, Sebastian Knauer, Andreas A Gentile, Nathan Wiebe, Maurangelo Petruzzella, Anthony Laing, John G. Rarity, Jeremy L. O'Brien, Mark G. Thompson
2017 2017 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC)  
Fig. 1 . 1 The Silicon photonics quantum simulator in a) is used to learn the Hamiltonian dynamics of the diamond NV electron spin in b).  ...  An integrated reconfigurable scheme allows to simulate the quantum dynamics of the spin system and the likelihoods required for the QHL protocol are calculated via projective measurements ̂.  ... 
doi:10.1109/cleoe-eqec.2017.8087392 fatcat:xl6fer4zcvbvxpoczitwoyxjma

A quantum generative model for multi-dimensional time series using Hamiltonian learning [article]

Haim Horowitz, Pooja Rao, Santosh Kumar Radha
2022 arXiv   pre-print
We then use the learned model to generate out-of-sample time series and show that it captures unique and complex features of the learned time series.  ...  We propose using the inherent nature of quantum computers to simulate quantum dynamics as a technique to encode such features.  ...  Learnability of a general probability distribution by the given ansatz. (left) Example of a learned distribution, (right) Average cost of learning 50 random distributions FIG. S6.  ... 
arXiv:2204.06150v1 fatcat:n64nnb6wp5fbrnwoumwrym6jmy

Quantum-assisted associative adversarial network: Applying quantum annealing in deep learning [article]

Max Wilson, Thomas Vandal, Tad Hogg, Eleanor Rieffel
2019 arXiv   pre-print
The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies, and is also applied to the LSUN dataset bedrooms class for the Chimera  ...  Evaluated using the Fréchet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model  ...  Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.  ... 
arXiv:1904.10573v1 fatcat:pue7olofxbg6zgh2gafkqjdogi

Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices [article]

Keisuke Fujii, Kohei Nakajima
2020 arXiv   pre-print
Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning.  ...  Quantum systems have an exponentially large degree of freedom in the number of particles and hence provide a rich dynamics that could not be simulated on conventional computers.  ...  Quantum circuit learning In the split of reservoir computing, dynamics of a physical system is not fine-tuned but natural dynamics of the system is harnessed for machine learning tasks.  ... 
arXiv:2011.04890v1 fatcat:ptqhpxhrefgn3g4dgj4qz7onga

Machine learning approach for quantum non-Markovian noise classification [article]

Stefano Martina, Stefano Gherardini, Filippo Caruso
2022 arXiv   pre-print
In this paper, machine learning and artificial neural network models are proposed for quantum noise classification in quantum dynamics affected by external noise.  ...  As a result, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using data sets collected from realizations of the quantum system dynamics.  ...  Grant Agreement No. 828946 (PATHOS), and from University of Florence through the project Q-CODYCES.  ... 
arXiv:2101.03221v2 fatcat:dmc3gauh3vc4jo64pyypen6psi

Quantum-assisted associative adversarial network: applying quantum annealing in deep learning

Max Wilson, Thomas Vandal, Tad Hogg, Eleanor G. Rieffel
2021 Quantum Machine Intelligence  
The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies.  ...  of quantum-assisted learning.  ...  Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon.  ... 
doi:10.1007/s42484-021-00047-9 fatcat:fosotnbbb5eela72ouos5o4hem

Introducing Non-Linearity into Quantum Generative Models [article]

Kaitlin Gili, Mykolas Sveistrys, Chris Ballance
2022 arXiv   pre-print
The evolution of an isolated quantum system is linear, and hence quantum algorithms are reversible, including those that utilize quantum circuits as generative machine learning models.  ...  With gradient-based training, we show that while both models can easily learn a trivial uniform probability distribution, on a more challenging class of distributions, the QNBM achieves an almost 3x smaller  ...  Additionally, the authors would like to recognize William Simon for feedback on an early version of the manuscript.  ... 
arXiv:2205.14506v1 fatcat:xkyscdfgvzb4rew3cygoj7daum

Non-Markovian Dynamical Maps: Numerical Processing of Open Quantum Trajectories

Javier Cerrillo, Jianshu Cao
2014 Physical Review Letters  
The concept underlying the approach can be generalized to physical observables with the goal of learning and manipulating the trajectories of an open quantum system.  ...  The initial stages of the evolution of an open quantum system encode the key information of its underlying dynamical correlations, which in turn can predict the trajectory at later stages.  ...  This information can be used to learn about the underlying dynamics of the system in order to generate a set of transfer tensors for propagation to longer time scales.  ... 
doi:10.1103/physrevlett.112.110401 pmid:24702332 fatcat:ncuyf4fzifgb5kamwpwgezu6r4

Expressibility and trainability of parameterized analog quantum systems for machine learning applications [article]

Jirawat Tangpanitanon, Supanut Thanasilp, Ninnat Dangniam, Marc-Antoine Lemonde, Dimitris G. Angelakis
2020 arXiv   pre-print
Parameterized quantum evolution is the main ingredient in variational quantum algorithms for near-term quantum devices.  ...  In digital quantum computing, it has been shown that random parameterized quantum circuits are able to express complex distributions intractable by a classical computer, leading to the demonstration of  ...  FIG. 3 . 3 Machine learning with a driven analog quantum processor: (a) A table demonstrating a real-world application of generative modeling tasks in machine learning.  ... 
arXiv:2005.11222v1 fatcat:z5oznefkgrgljdqaqh4abxmrhy

Quantum Boltzmann Machine

Mohammad H. Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, Roger Melko
2018 Physical Review X  
Inspired by the success of Boltzmann Machines based on classical Boltzmann distribution, we propose a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian  ...  We also discuss the possibility of using quantum annealing processors like D-Wave for QBM training and application.  ...  This research was partially supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Engage grant.  ... 
doi:10.1103/physrevx.8.021050 fatcat:dog7dao3mvh3jfraltnzokn3rm

Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines [article]

Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian, Fabian Chudak, William G. Macready, Jason Rolfe, Evgeny Andriyash
2016 arXiv   pre-print
For these hard problems, QA gradient estimates are more accurate, and allow for faster learning.  ...  We characterize the sampling distribution of QA hardware, and show that in many cases, the quantum distributions differ significantly from classical Boltzmann distributions.  ...  the performance of QA using B(s s s|θ), for reference).  ... 
arXiv:1611.04528v1 fatcat:6rt5auq4b5capafe5qjc5hsvai

Quantum entanglement recognition [article]

Jun Yong Khoo, Markus Heyl
2021 arXiv   pre-print
The central element is a protocol for the generation of statistical images from quantum many-body states, with which we perform image classification by means of convolutional neural networks.  ...  Our developed scheme provides a generally applicable strategy for quantum entanglement recognition in both equilibrium and nonequilibrium quantum matter.  ...  Based on the statistical images generated out of quantum many-body states, we perform a conventional image recognition task using a convolutional neural network.  ... 
arXiv:2007.14397v2 fatcat:we5s23e7dfc6hf7qaisdvijete
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