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Boltzmann Machines constitute a class of neural networks with applications to image reconstruction, pattern classification and unsupervised learning in general.
A learning algorithm for boltzmann machines
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Cooling schedules for optimal annealing
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Unsupervised learning
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Synchronous boltzmann machines can be universal approximators
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Robustness of adiabatic quantum computation
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Colossus was the first electronic digital computer
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Mathematical foundation of quantum annealing
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Multimodal learning with deep boltzmann machines
Srivastava, N. & Salakhutdinov, R. R · 2012
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Building high-level features using large scale unsupervised learning
Le, Q. V · 2013
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Thermally assisted quantum annealing of a 16-qubit problem
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Big & personal: data and models behind netflix recommendations
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On the challenges of physical implementations of rbms
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Prati, E · 2017
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Quantum-assisted learning of hardware-embedded probabilistic graphical models
Benedetti, M., Realpe-Gómez, J., Biswas, R. & Perdomo-Ortiz, A · 2017
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Low rank non-negative matrix factorization with d-wave 2000q
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Abnormal traffic pattern detection in real-time financial transactions
Rastatter, S., Moe, T., Gangopadhyay, A. & Weaver, A · 2019
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Theis, L., Oord, A. v. d. & Bethge, M · 2015
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Searching for quantum speedup in quasistatic quantum annealers
Amin, M. H · 2015
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Application of quantum annealing to training of deep neural networks
Adachi, S. H. & Henderson, M. P · 2015
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Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning
Benedetti, M., Realpe-Gómez, J., Biswas, R. & Perdomo-Ortiz, A · 2016
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Computational multiqubit tunnelling in programmable quantum annealers
Boixo, S. et al · 2016
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Benchmarking quantum hardware for training of fully visible boltzmann machines
Korenkevych, D. et al · 2016
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Reconstructing quantum states with generative models
Carrasquilla, J., Torlai, G., Melko, R. G. & Aolita, L · 2019
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Near-term quantum-classical associative adversarial networks
Anschuetz, E. R. & Zanoci, C · 2019
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Coherent transport of quantum states by deep reinforcement learning
Porotti, R., Tamascelli, D., Restelli, M. & Prati, E · 2019
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Reinforcement learning based control of coherent transport by adiabatic passage of spin qubits
Porotti, R., Tamascelli, D., Restelli, M. & Prati, E · 2019
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Technical description of the d-wave quantum processing unit (2019)
Systems, D.-W · 2019
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Quantum-assisted genetic algorithm
King, J. et al · 2019
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Reverse quantum annealing approach to portfolio optimization problems
Venturelli, D. & Kondratyev, A · 2019
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Leveraging quantum annealing for election forecasting
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Digitally stimulated raman passage by deep reinforcement learning
Paparelle, I., Moro, L. & Prati, E · 2020
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