Fetching the paper…
Reading the bibliography…
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data.
The perceptron: A probabilistic model for information storage and organization in the brain
Rosenblatt, F · 1958
Earlier work this paper cites.
The Nature of Statistical Learning Theory (Springer, New York, NY, USA, 1995)
Vapnik, V · 1995
Earlier work this paper cites.
Genetic algorithm with migration on topology conserving maps for optimal control of quantum systems
Amstrup, B., Toth, G. J., Szabo, G., Rabitz, H. & Loerincz, A · 1995
Earlier work this paper cites.
Universal quantum simulators
Lloyd, S · 1996
Earlier work this paper cites.
A quantum algorithm for finding the minimum
Dürr, C. & Høyer, P · 1996
Earlier work this paper cites.
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Shor, P. W · 1997
Earlier work this paper cites.
Quantum computation and quantum information (Cambridge University Press, 2000)
Nielsen, M. A. & Chuang, I. L · 2000
Earlier work this paper cites.
Quantum associative memory
Ventura, D. & Martinez, T · 2000
Earlier work this paper cites.
Quantum template matching
Sasaki, M., Carlini, A. & Jozsa, R · 2001
Earlier work this paper cites.
Evolutionary algorithms and their application to optimal control studies
Zeidler, D., Frey, S., Kompa, K.-L. & Motzkus, M · 2001
Earlier work this paper cites.
Quantum optimization for training support vector machines
Anguita, D., Ridella, S., Rivieccio, F. & Zunino, R · 2003
Earlier work this paper cites.
Machine Learning in a Quantum World , 431–442 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2006)
Aïmeur, E., Brassard, G. & Gambs, S · 2006
Earlier work this paper cites.
Trapped ion chain as a neural network: Error resistant quantum computation
Pons, M. et al · 2007
Earlier work this paper cites.
Quantum random access memory
Giovannetti, V., Lloyd, S. & Maccone, L · 2008
Earlier work this paper cites.
Realizable Hamiltonians for universal adiabatic quantum computers
Biamonte, J. D. & Love, P. J · 2008
Earlier work this paper cites.
Quantum algorithm for linear systems of equations
Harrow, A. W., Hassidim, A. & Lloyd, S · 2009
Earlier work this paper cites.
Quantum pattern recognition with liquid-state nuclear magnetic resonance
Neigovzen, R., Neves, J. L., Sollacher, R. & Glaser, S. J · 2009
Earlier work this paper cites.
Binary classification using hardware implementation of quantum annealing
Neven, H. et al · 2009
Earlier work this paper cites.
Optimal quantum learning of a unitary transformation
Bisio, A., Chiribella, G., D’Ariano, G. M., Facchini, S. & Perinotti, P · 2010
Earlier work this paper cites.
Machine learning for precise quantum measurement
Hentschel, A. & Sanders, B. C · 2010
Earlier work this paper cites.
Quantum learning algorithms for quantum measurements
Bisio, A., D’Ariano, G. M., Perinotti, P. & Sedlák, M · 2011
Earlier work this paper cites.
Toward the implementation of a quantum rbm
Denil, M. & De Freitas, N · 2011
Earlier work this paper cites.
Quantum metropolis sampling
Temme, K., Osborne, T. J., Vollbrecht, K. G., Poulin, D. & Verstraete, F · 2011
Earlier work this paper cites.
Quantum algorithm for data fitting
Wiebe, N., Braun, D. & Lloyd, S · 2012
Earlier work this paper cites.
Quantum learning without quantum memory
Sentís, G., Calsamiglia, J., Muñoz-Tapia, R. & Bagan, E · 2012
Earlier work this paper cites.
Ground-state spin logic
Whitfield, J. D., Faccin, M. & Biamonte, J. D · 2012
Earlier work this paper cites.
A quantum–quantum metropolis algorithm
Yung, M.-H. & Aspuru-Guzik, A · 2012
Earlier work this paper cites.
Robust online Hamiltonian learning
Granade, C. E., Ferrie, C., Wiebe, N. & Cory, D. G · 2012
Earlier work this paper cites.
Robust classification with adiabatic quantum optimization
Denchev, V. S., Ding, N., Vishwanathan, S. & Neven, H · 2012
Earlier work this paper cites.
Investigating the performance of an adiabatic quantum optimization processor
Karimi, K. et al · 2012
Earlier work this paper cites.
Building high-level features using large scale unsupervised learning
Le, Q. V · 2013
Earlier work this paper cites.
Preconditioned quantum linear system algorithm
Clader, B. D., Jacobs, B. C. & Sprouse, C. R · 2013
Earlier work this paper cites.
Quantum algorithms for supervised and unsupervised machine learning
Lloyd, S., Mohseni, M. & Rebentrost, P · 2013
Earlier work this paper cites.
On the challenges of physical implementations of RBMs
Dumoulin, V., Goodfellow, I. J., Courville, A. & Bengio, Y · 2013
Earlier work this paper cites.
Differential evolution for many-particle adaptive quantum metrology
Lovett, N. B., Crosnier, C., Perarnau-Llobet, M. & Sanders, B. C · 2013
Earlier work this paper cites.
Parallel photonic information processing at gigabyte per second data rates using transient states
Brunner, D., Soriano, M. C., Mirasso, C. R. & Fischer, I · 2013
Earlier work this paper cites.
An introduction to quantum machine learning
Schuld, M., Sinayskiy, I. & Petruccione, F · 2014
Earlier work this paper cites.
Quantum Machine Learning: What Quantum Computing Means to Data Mining (Academic Press, New York, NY, USA, 2014)
Wittek, P · 2014
Cited alongside, same era.
Quantum principal component analysis
Lloyd, S., Mohseni, M. & Rebentrost, P · 2014
Cited alongside, same era.
Quantum support vector machine for big data classification
Rebentrost, P., Mohseni, M. & Lloyd, S · 2014
Cited alongside, same era.
Wiebe, N., Kapoor, A. & Svore, K. M · 2014
Cited alongside, same era.
Quantum learning of coherent states
Sentís, G., Guţă, M. & Adesso, G · 2014
Cited alongside, same era.
Quantum speedup for active learning agents
Paparo, G. D., Dunjko, V., Makmal, A., Martin-Delgado, M. A. & Briegel, H. J · 2014
Prediction by linear regression on a quantum computer
Schuld, M., Sinayskiy, I. & Petruccione, F · 2016
Closest in time.
Quantum speed-ups for semidefinite programming
Brandao, F. G. & Svore, K · 2016
Closest in time.
Quantum gradient descent and Newton’s method for constrained polynomial optimization
Rebentrost, P., Schuld, M., Petruccione, F. & Lloyd, S · 2016
Closest in time.
Amin, M. H., Andriyash, E., Rolfe, J., Kulchytskyy, B. & Melko, R · 2016
Closest in time.
Quantum-enhanced machine learning
Dunjko, V., Taylor, J. M. & Briegel, H. J · 2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Community detection in quantum complex networks
Faccin, M., Migdał, P., Johnson, T. H., Bergholm, V. & Biamonte, J. D · 2014
Cited alongside, same era.
A quantum approximate optimization algorithm
Farhi, E., Goldstone, J. & Gutmann, S · 2014
Cited alongside, same era.
Hamiltonian learning and certification using quantum resources
Wiebe, N., Granade, C., Ferrie, C. & Cory, D. G · 2014
Cited alongside, same era.
High-fidelity spin entanglement using optimal control
Dolde, F. et al · 2014
Cited alongside, same era.
Defining and detecting quantum speedup
Rønnow, T. F. et al · 2014
Cited alongside, same era.
Quantum inference on Bayesian networks
Low, G. H., Yoder, T. J. & Chuang, I. L · 2014
Cited alongside, same era.
Sentís, G., Bagan, E., Calsamiglia, J., Chiribella, G. & Muñoz Tapia, R · 2016
Closest in time.
Generalized coherent states, reproducing kernels, and quantum support vector machines
Chatterjee, R. & Yu, T · 2016
Closest in time.
Quantum algorithms for topological and geometric analysis of data
Lloyd, S., Garnerone, S. & Zanardi, P · 2016
Closest in time.
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
Closest in time.
Tomography and generative data modeling via quantum Boltzmann training
Kieferova, M. & Wiebe, N · 2016
Closest in time.
Adiabatic and Hamiltonian computing on a 2D lattice with simple 2-qubit interactions
Lloyd, S. & Terhal, B · 2016
Closest in time.
Marvian, I. & Lloyd, S · 2016
Closest in time.
Designing high-fidelity single-shot three-qubit gates: A machine-learning approach
Zahedinejad, E., Ghosh, J. & Sanders, B. C · 2016
Closest in time.
Genetic algorithms for digital quantum simulations
Las Heras, U., Alvarez-Rodriguez, U., Solano, E. & Sanz, M · 2016
Closest in time.
Quantum gate learning in qubit networks: Toffoli gate without time-dependent control
Banchi, L., Pancotti, N. & Bose, S · 2016
Closest in time.
Using recurrent neural networks to optimize dynamical decoupling for quantum memory
August, M. & Ni, X · 2016
Closest in time.
Learning in quantum control: High-dimensional global optimization for noisy quantum dynamics
Palittapongarnpim, P., Wittek, P., Zahedinejad, E., Vedaie, S. & Sanders, B. C · 2016
Closest in time.
Machine learning quantum phases of matter beyond the fermion sign problem
Broecker, P., Carrasquilla, J., Melko, R. G. & Trebst, S · 2016
Closest in time.
Quantum recommendation systems
Kerenidis, I. & Prakash, A · 2016
Closest in time.
Quantum machine learning without measurements
Alvarez-Rodriguez, U., Lamata, L., Escandell-Montero, P., Martín-Guerrero, J. D. & Solano, E · 2016
Closest in time.
Controlling adaptive quantum phase estimation with scalable reinforcement learning
Palittapongarnpim, P., Wittek, P. & Sanders, B. C · 2016
Closest in time.
Quantum generalisation of feedforward neural networks
Wan, K. H., Dahlsten, O., Kristjánsson, H., Gardner, R. & Kim, M. S · 2016
Closest in time.
Quantum perceptron models
Wiebe, N., Kapoor, A. & Svore, K. M · 2016
Closest in time.
A survey of quantum learning theory
Arunachalam, S. & de Wolf, R · 2017
Closest in time.
Quantum machine learning over infinite dimensions
Lau, H.-K., Pooser, R., Siopsis, G. & Weedbrook, C · 2017
Closest in time.
A quantum linear system algorithm for dense matrices
Wossnig, L., Zhao, Z. & Prakash, A · 2017
Closest in time.
Quantum algorithms for Gibbs sampling and hitting-time estimation
Chowdhury, A. N. & Somma, R. D · 2017
Closest in time.
Machine learning phases of matter
Carrasquilla, J. & Melko, R. G · 2017
Closest in time.
Solving the quantum many-body problem with artificial neural networks
Carleo, G. & Troyer, M · 2017
Closest in time.
Quantum enhanced inference in Markov logic networks
Wittek, P. & Gogolin, C · 2017
Closest in time.
Basic protocols in quantum reinforcement learning with superconducting circuits
Lamata, L · 2017
Closest in time.
Schuld, M., Fingerhuth, M. & Petruccione, F · 2017
Closest in time.
Inductive supervised quantum learning
Monràs, A., Sentís, G. & Wittek, P · 2017
Closest in time.
Towards quantum supremacy: enhancing quantum control by bootstrapping a quantum processor
Lu, D. et al · 2017
Closest in time.
Prediction and real-time compensation of qubit decoherence via machine learning
Mavadia, S., Frey, V., Sastrawan, J., Dona, S. & Biercuk, M. J · 2017
Closest in time.
Concrete resource analysis of the quantum linear-system algorithm used to compute the electromagnetic scattering cross section of a 2d target
Scherer, A. et al · 2017
Closest in time.