| Quantum machine learning in feature hilbert spaces M Schuld, N Killoran Physical review letters 122 (4), 040504, 2019 | 1362 | 2019 |
| Evaluating analytic gradients on quantum hardware M Schuld, V Bergholm, C Gogolin, J Izaac, N Killoran Physical Review A 99 (3), 032331, 2019 | 1060 | 2019 |
| Pennylane: Automatic differentiation of hybrid quantum-classical computations V Bergholm, J Izaac, M Schuld, C Gogolin, S Ahmed, V Ajith, MS Alam, ... arXiv preprint arXiv:1811.04968, 2018 | 1031 | 2018 |
| Quantum circuits with many photons on a programmable nanophotonic chip JM Arrazola, V Bergholm, K Brádler, TR Bromley, MJ Collins, I Dhand, ... Nature 591 (7848), 54-60, 2021 | 565 | 2021 |
| Quantum generative adversarial networks PL Dallaire-Demers, N Killoran Physical Review A 98 (1), 012324, 2018 | 498 | 2018 |
| Quantum natural gradient J Stokes, J Izaac, N Killoran, G Carleo Quantum 4, 269, 2020 | 480 | 2020 |
| Continuous-variable quantum neural networks N Killoran, TR Bromley, JM Arrazola, M Schuld, N Quesada, S Lloyd Physical Review Research 1 (3), 033063, 2019 | 460 | 2019 |
| Strawberry fields: A software platform for photonic quantum computing N Killoran, J Izaac, N Quesada, V Bergholm, M Amy, C Weedbrook Quantum 3, 129, 2019 | 347 | 2019 |
| Quantum embeddings for machine learning S Lloyd, M Schuld, A Ijaz, J Izaac, N Killoran arXiv preprint arXiv:2001.03622, 2020 | 346 | 2020 |
| Transfer learning in hybrid classical-quantum neural networks A Mari, TR Bromley, J Izaac, M Schuld, N Killoran Quantum 4, 340, 2020 | 310 | 2020 |
| Converting non-classicality into entanglement N Killoran, FES Steinhoff, MB Plenio Physical Review Letters 116, 080402, 2016 | 215 | 2016 |
| Generating and designing DNA with deep generative models N Killoran, LJ Lee, A Delong, D Duvenaud, BJ Frey arXiv preprint arXiv:1712.06148, 2017 | 194 | 2017 |
| Extracting entanglement from identical particles N Killoran, M Cramer, MB Plenio Physical Review Letters 112, 150501, 2014 | 190 | 2014 |
| Estimating the gradient and higher-order derivatives on quantum hardware A Mari, TR Bromley, N Killoran Physical Review A 103 (1), 012405, 2021 | 177 | 2021 |
| Is quantum advantage the right goal for quantum machine learning? M Schuld, N Killoran Prx Quantum 3 (3), 030101, 2022 | 169 | 2022 |
| Resource theory of superposition T Theurer, N Killoran, D Egloff, MB Plenio Physical review letters 119 (23), 230401, 2017 | 153 | 2017 |
| Machine learning method for state preparation and gate synthesis on photonic quantum computers JM Arrazola, TR Bromley, J Izaac, CR Myers, K Brádler, N Killoran Quantum Science and Technology 4 (2), 024004, 2019 | 152 | 2019 |
| Applications of near-term photonic quantum computers: software and algorithms TR Bromley, JM Arrazola, S Jahangiri, J Izaac, N Quesada, AD Gran, ... Quantum Science and Technology 5 (3), 034010, 2020 | 130 | 2020 |
| Gaussian boson sampling using threshold detectors N Quesada, JM Arrazola, N Killoran Physical Review A 98 (6), 062322, 2018 | 125 | 2018 |
| Coherent control of quantum systems as a resource theory JM Matera, D Egloff, N Killoran, MB Plenio Quantum Science and Technology 1 (1), 01LT01, 2016 | 122 | 2016 |
Maria SchuldQuantum Machine Learning research lead at Xanaduایمیل تأیید شده در xanadu.ai
