Resources

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Aïmeur, E., Brassard, G., & Gambs, S. (2006). Machine learning in a quantum world. In Advances in Artificial Intelligence (pp. 431-442). Springer Berlin Heidelberg.

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Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., & LeCun, Y. (2014). The Loss Surface of Multilayer Networks. arXiv preprint arXiv:1412.0233.

Cohen, E., & Tamir, B. (2014). D-Wave and predecessors: From simulated to quantum annealing. International Journal of Quantum Information, 12(03), 1430002.

Daoyi Dong; Chunlin Chen; Hanxiong Li; Tarn, T., "Quantum Reinforcement Learning," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on , vol.38, no.5, pp.1207,1220, Oct. 2008

Denchev, V. S., Ding, N., Vishwanathan, S. V. N., & Neven, H. (2012). Robust classification with adiabatic quantum optimization. arXiv preprint arXiv:1205.1148.

DiVincenzo, D. P. (2000). The physical implementation of quantum computation. arXiv preprint quant-ph/0002077.

Epstein, C. (2012). Adiabatic quantum computing: An overview. Quantum Complexity Theory, 6, 845.

Faber, J., & Giraldi, G. A. (2002). Quantum models for artificial neural networks.

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Johnson, M. W., Amin, M. H. S., Gildert, S., Lanting, T., Hamze, F., Dickson, N., ... & Rose, G. (2011). Quantum annealing with manufactured spins. Nature, 473(7346), 194-198.

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Kielpinski, D., Monroe, C., & Wineland, D. J. (2002). Architecture for a large-scale ion-trap quantum computer. Nature, 417(6890), 709-711.

Knill, E., Laflamme, R., & Milburn, G. J. (2001). A scheme for efficient quantum computation with linear optics. nature, 409(6816), 46-52.

Lanting, T., Przybysz, A. J., Smirnov, A. Y., Spedalieri, F. M., Amin, M. H., Berkley, A. J., ... & Rose, G. (2014). Entanglement in a quantum annealing processor. Physical Review X, 4(2), 021041.

Leuenberger, M. N., & Loss, D. (2001). Quantum computing in molecular magnets. Nature, 410(6830), 789-793.

Lloyd, S., Mohseni, M., & Rebentrost, P. (2013). Quantum algorithms for supervised and unsupervised machine learning. arXiv preprint arXiv:1307.0411.

Lloyd, S., Mohseni, M., & Rebentrost, P. (2014). Quantum principal component analysis. Nature Physics, 10(9), 631-633.

Mehta, P., & Schwab, D. J. (2014). An exact mapping between the Variational Renormalization Group and Deep Learning. arXiv preprint arXiv:1410.3831.

McGeoch, C. C., & Wang, C. (2013, May). Experimental evaluation of an adiabiatic quantum system for combinatorial optimization. In Proceedings of the ACM International Conference on Computing Frontiers (p. 23). ACM.

Mirani, L., & Lichfield, G. (2014, April 15). Why nobody can tell whether the world?s biggest quantum computer is a quantum computer. Retrieved July 20, 2015, from http://qz.com/194738/why-nobody-can-tell-whether-the-worlds-biggest-quantum-computer-is-a-quantum-computer/

Neven, H., Denchev, V. S., Rose, G., & Macready, W. G. (2009). Training a large scale classifier with the quantum adiabatic algorithm. arXiv preprint arXiv:0912.0779.

Neven, H., Denchev, V. S., Drew-Brook, M., Zhang, J., Macready, W. G., & Rose, G. (2009). NIPS 2009 demonstration: Binary classification using hardware implementation of quantum annealing. Quantum, 1-17.

Neven, H., Denchev, V. S., Rose, G., & Macready, W. G. (2008). Training a binary classifier with the quantum adiabatic algorithm. arXiv preprint arXiv:0811.0416.

Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation & Quantum Information Systems. Cambridge university press.

O’Gorman, B., Babbush, R., Perdomo-Ortiz, A., Aspuru-Guzik, A., & Smelyanskiy, V. (2015). Bayesian network structure learning using quantum annealing. The European Physical Journal Special Topics, 224(1), 163-188.

Oskin, M., Chong, F. T., Chuang, I. L., & Kubiatowicz, J. (2003, June). Building quantum wires: the long and the short of it. In Computer Architecture, 2003. Proceedings. 30th Annual International Symposium on (pp. 374-385). IEEE.

Paul, A., & Venkatasubramanian, S. (2014). Why does Deep Learning work?-A perspective from Group Theory. arXiv preprint arXiv:1412.6621.

Paparo, G. D., Dunjko, V., Makmal, A., Martin-Delgado, M. A., & Briegel, H. J. (2014). Quantum speedup for active learning agents. Physical Review X, 4(3), 031002.

Pudenz, K. L., Albash, T., & Lidar, D. A. (2014). Error-corrected quantum annealing with hundreds of qubits. Nature communications, 5.

Pudenz, K. L., & Lidar, D. A. (2013). Quantum adiabatic machine learning. Quantum information processing, 12(5), 2027-2070.

Rebentrost, P., Mohseni, M., & Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical review letters, 113(13), 130503.

Rigetti, C. T. (2009). Quantum gates for superconducting qubits

Russell, S., Norvig, P. (1995). Artificial Intelligence: A modern approach. Prentice-Hall, Egnlewood Cliffs, 25.

Serway, R., Moses, C., & Moyer, C. (2004). Chapter 12 Superconductivity in "Modern physics". Cengage Learning. Retrieved from http://www.cengage.com/resource_uploads/static_resources/0534493394/4891/SerwayCh12-Superconductivity.pdf

Shin, S. W., Smith, G., Smolin, J. A., & Vazirani, U. (2014). How" Quantum" is the D-Wave Machine?. arXiv preprint arXiv:1401.7087.

Schuld, M., Sinayskiy, I., & Petruccione, F. (2014). An introduction to quantum machine learning. Contemporary Physics, (ahead-of-print), 1-14.

Schuld, M., Sinayskiy, I., & Petruccione, F. (2014). The quest for a quantum neural network. Quantum Information Processing, 13(11), 2567-2586.

Smolin, J. A., & Smith, G. (2013). Classical signature of quantum annealing. arXiv preprint arXiv:1305.4904.

Trugenberger, C. A. (2001). Probabilistic quantum memories. Physical Review Letters, 87(6), 067901.

Prentice-Hall, Egnlewood Cliffs, . Tarrataca, L., & Wichert, A. (2013). Intricacies of quantum computational paths. Quantum information processing,&nbsp12(2), 1365-1378.

Tarrataca, L., & Wichert, A. (2011). Tree search and quantum computation. Quantum Information Processing. 10(4), 475-500.

Vazirani, U. (2013, August 11). Quantum Mechanics and Quantum Computation. Retrieved March 15, 2015, from https://www.edx.org/course/quantum-mechanics-quantum-computation-uc-berkeleyx-cs-191x#.VQiAV2TF9yc

Ventura, D., & Martinez, T. (2000). Quantum associative memory. Information Sciences, 124(1), 273-296.

Wiebe, N., Kapoor, A., & Svore, K. (2014). Quantum nearest-neighbor algorithms for machine learning. arXiv preprint arXiv:1401.2142.

Wiebe, N., Kapoor, A., & Svore, K. M. (2014). Quantum Deep Learning. arXiv preprint arXiv:1412.3489.

Wittek, P. (2014). Quantum Machine Learning: What Quantum Computing Means to Data Mining (First edition.). San Diego, CA: Academic Press.

Ying, M. (2010). Quantum computation, quantum theory and AI. Artificial Intelligence, 174(2), 162-176.

Zwiebach, B. (2015, January 10). Mastering Quantum Mechanics. Retrieved March 17, 2015, from https://www.edx.org/course/mastering-quantum-mechanics-mitx-8-05x#.VQiBbmTF9yc