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The goal of generative machine learning is to model the probability distribution underlying a given data set.
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Generic entanglement can be generated efficiently
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Sampling from the Thermal Quantum Gibbs State and Evaluating Partition Functions with a Quantum Computer
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Simulating chemistry using quantum computers
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Quantum Metropolis Sampling
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Quantum algorithm for data fitting
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A quantum–quantum Metropolis algorithm
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Guest Column: The Quantum PCP Conjecture
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Deep Learning Approaches for Link Prediction in Social Network Services
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Quantum algorithms for supervised and unsupervised machine learning
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Quantum principal component analysis
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A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem
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Generative Adversarial Networks
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Symmetry-protected topological order and negative-sign problem for SO ( n ) \mathrm{SO(}n) bilinear-biquadratic chains
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Quantum support vector machine for big data classification
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P. Wittek · 2014
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Read the fine print
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Digital quantum simulation of fermionic models with a superconducting circuit
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An artificial neuron implemented on an actual quantum processor
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A quantum-inspired classical algorithm for recommendation systems
E. Tang · 2019
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Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
J. Tubiana, S. Cocco, and R. Monasson · 2019
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Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
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Generative training of quantum Boltzmann machines with hidden units
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Time Series Prediction Using Restricted Boltzmann Machines and Backpropagation
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Adam: A Method for Stochastic Optimization
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Quantum enhancements for deep reinforcement learning in large spaces
S. Jerbi, L. M. Trenkwalder, H. Poulsen Nautrup, H. J. Briegel, and V. Dunjko · 2021
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Quantum Earth Mover’s Distance: A New Approach to Learning Quantum Data
B. T. Kiani, G. D. Palma, M. Marvian, Z.-W. Liu, and S. Lloyd · 2021
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Quantum Energy Landscape and VQA Optimization
J. Kim and Y. Oz · 2021
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Variational quantum simulations of stochastic differential equations
K. Kubo, Y. O. Nakagawa, S. Endo, and S. Nagayama · 2021
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Generalized quantum circuit differentiation rules
O. Kyriienko and V. E. Elfving · 2021
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Resonant quantum principal component analysis
Z. Li, Z. Chai, Y. Guo, W. Ji, M. Wang, F. Shi, Y. Wang, S. Lloyd, and J. Du · 2021
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A rigorous and robust quantum speed-up in supervised machine learning
Y. Liu, S. Arunachalam, and K. Temme · 2021
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Quantum Natural Gradient for Variational Bayes
A. Lopatnikova and M.-N. Tran · 2021
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Estimating the gradient and higher-order derivatives on quantum hardware
A. Mari, T. R. Bromley, and N. Killoran · 2021
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A grand unification of quantum algorithms
J. M. Martyn, Z. M. Rossi, A. K. Tan, and I. L. Chuang · 2021
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Fisher information in noisy intermediate-scale quantum applications
J. J. Meyer · 2021
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Entanglement devised barren plateau mitigation
T. L. Patti, K. Najafi, X. Gao, and S. F. Yelin · 2021
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Emulation of cosmological mass maps with conditional generative adversarial networks
N. Perraudin, S. Marcon, A. Lucchi, and T. Kacprzak · 2021
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Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions
J. Romero and A. Aspuru-Guzik · 2021
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Effect of data encoding on the expressive power of variational quantum-machine-learning models
M. Schuld, R. Sweke, and J. J. Meyer · 2021
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Layerwise learning for quantum neural networks
A. Skolik et al · 2021
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Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
A. Skolik, J. R. McClean, M. Mohseni, P. van der Smagt, and M. Leib · 2021
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Quantum principal component analysis only achieves an exponential speedup because of its state preparation assumptions
E. Tang · 2021
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Large gradients via correlation in random parameterized quantum circuits
T. Volkoff and P. J. Coles · 2021
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Noise-induced barren plateaus in variational quantum algorithms
S. Wang, E. Fontana, M. Cerezo, K. Sharma, A. Sone, L. Cincio, and P. J. Coles · 2021
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Adaptive variational quantum dynamics simulations
Y.-X. Yao, N. Gomes, F. Zhang, C.-Z. Wang, K.-M. Ho, T. Iadecola, and P. P. Orth · 2021
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Low-depth quantum state preparation
X.-M. Zhang, M.-H. Yung, and X. Yuan · 2021
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Variational quantum Boltzmann machines
C. Zoufal, A. Lucchi, and S. Woerner · 2021
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Error bounds for variational quantum time evolution
C. Zoufal, D. Sutter, and S. Woerner · 2021
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