| Quantum boltzmann machine MH Amin, E Andriyash, J Rolfe, B Kulchytskyy, R Melko Physical Review X 8 (2), 021050, 2018 | 906 | 2018 |
| Observation of topological phenomena in a programmable lattice of 1,800 qubits AD King, J Carrasquilla, J Raymond, I Ozfidan, E Andriyash, A Berkley, ... Nature 560 (7719), 456-460, 2018 | 422 | 2018 |
| Quantum variational autoencoder A Khoshaman, W Vinci, B Denis, E Andriyash, H Sadeghi, MH Amin Quantum Science and Technology 4 (1), 014001, 2018 | 256 | 2018 |
| Systems and methods for creating and using quantum Boltzmann machines MHS Amin, E Andriyash, J Rolfe US Patent 11,062,227, 2021 | 112 | 2021 |
| Dvae++: Discrete variational autoencoders with overlapping transformations A Vahdat, W Macready, Z Bian, A Khoshaman, E Andriyash International conference on machine learning, 5035-5044, 2018 | 105 | 2018 |
| Systems and methods for problem solving, useful for example in quantum computing F Hamze, AD King, J Raymond, AP Roy, R Israel, E Andriyash, ... US Patent 9,881,256, 2018 | 87 | 2018 |
| Wall-crossing from supersymmetric galaxies E Andriyash, F Denef, DL Jafferis, GW Moore Journal of High Energy Physics 2012 (1), 1-16, 2012 | 84 | 2012 |
| Global warming: Temperature estimation in annealers J Raymond, S Yarkoni, E Andriyash Frontiers in ICT 3, 23, 2016 | 82 | 2016 |
| Sampling from a set of spins with clamping F Hamze, J King, E Andriyash, C McGeoch, J Raymond, J Rolfe, ... US Patent 9,588,940, 2017 | 76 | 2017 |
| A path towards quantum advantage in training deep generative models with quantum annealers W Winci, L Buffoni, H Sadeghi, A Khoshaman, E Andriyash, MH Amin Machine Learning: Science and Technology 1 (4), 045028, 2020 | 73 | 2020 |
| Dvae#: Discrete variational autoencoders with relaxed boltzmann priors A Vahdat, E Andriyash, W Macready Advances in Neural Information Processing Systems 31, 2018 | 68 | 2018 |
| Bound state transformation walls E Andriyash, F Denef, DL Jafferis, GW Moore Journal of High Energy Physics 2012 (3), 1-65, 2012 | 56 | 2012 |
| Benchmarking quantum hardware for training of fully visible boltzmann machines D Korenkevych, Y Xue, Z Bian, F Chudak, WG Macready, J Rolfe, ... arXiv preprint arXiv:1611.04528, 2016 | 51 | 2016 |
| Systems and methods for training generative machine learning models JT Rolfe, AH Khoshaman, A Vahdat, MH Amin, EA Andriyash, ... US Patent App. 16/968,465, 2020 | 46 | 2020 |
| Degeneracy, degree, and heavy tails in quantum annealing AD King, E Hoskinson, T Lanting, E Andriyash, MH Amin Physical Review A 93 (5), 052320, 2016 | 41 | 2016 |
| Pixelvae++: Improved pixelvae with discrete prior H Sadeghi, E Andriyash, W Vinci, L Buffoni, MH Amin arXiv preprint arXiv:1908.09948, 2019 | 40 | 2019 |
| Boosting integer factoring performance via quantum annealing offsets E Andriyash, Z Bian, F Chudak, M Drew-Brook, AD King, WG Macready, ... D-Wave Technical Report Series 14 (2016), 52, 2016 | 38 | 2016 |
| Can quantum Monte Carlo simulate quantum annealing? E Andriyash, MH Amin arXiv preprint arXiv:1703.09277, 2017 | 35 | 2017 |
| Ample D4-D2-D0 Decay E Andriyash, GW Moore arXiv preprint arXiv:0806.4960, 2008 | 26 | 2008 |
| Re-equilibrated quantum sampling MH Amin, EA Andriyash US Patent 10,346,508, 2019 | 22 | 2019 |
