| Machine learning and the physical sciences G Carleo, I Cirac, K Cranmer, L Daudet, M Schuld, N Tishby, ... Reviews of Modern Physics 91 (4), 045002, 2019 | 2530 | 2019 |
| Quantum machine learning in feature Hilbert spaces M Schuld, N Killoran Physical review letters 122 (4), 040504, 2019 | 1461 | 2019 |
| An introduction to quantum machine learning M Schuld, I Sinayskiy, F Petruccione Contemporary Physics 56 (2), 172-185, 2015 | 1453 | 2015 |
| 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 | 1190 | 2018 |
| Evaluating analytic gradients on quantum hardware M Schuld, V Bergholm, C Gogolin, J Izaac, N Killoran Physical Review A 99 (3), 032331, 2019 | 1135 | 2019 |
| Circuit-centric quantum classifiers M Schuld, A Bocharov, KM Svore, N Wiebe Physical Review A 101 (3), 032308, 2020 | 1033 | 2020 |
| Supervised learning with quantum computers M Schuld, F Petruccione Quantum science and technology (Springer, 2018), 2018 | 696 | 2018 |
| The quest for a quantum neural network M Schuld, I Sinayskiy, F Petruccione Quantum Information Processing 13, 2567-2586, 2014 | 680 | 2014 |
| Effect of data encoding on the expressive power of variational quantum-machine-learning models M Schuld, R Sweke, JJ Meyer Physical Review A 103 (3), 032430, 2021 | 665 | 2021 |
| 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 | 596 | 2021 |
| Continuous-variable quantum neural networks N Killoran, TR Bromley, JM Arrazola, M Schuld, N Quesada, S Lloyd Physical Review Research 1 (3), 033063, 2019 | 487 | 2019 |
| Quantum embeddings for machine learning S Lloyd, M Schuld, A Ijaz, J Izaac, N Killoran arXiv preprint arXiv:2001.03622, 2020 | 382 | 2020 |
| Machine learning with quantum computers M Schuld, F Petruccione Springer 676, 163-169, 2021 | 375 | 2021 |
| Supervised quantum machine learning models are kernel methods M Schuld arXiv preprint arXiv:2101.11020, 2021 | 338 | 2021 |
| Transfer learning in hybrid classical-quantum neural networks A Mari, TR Bromley, J Izaac, M Schuld, N Killoran Quantum 4, 340, 2020 | 335 | 2020 |
| Prediction by linear regression on a quantum computer M Schuld, I Sinayskiy, F Petruccione Physical Review A 94 (2), 022342, 2016 | 322 | 2016 |
| Implementing a distance-based classifier with a quantum interference circuit M Schuld, M Fingerhuth, F Petruccione Europhysics Letters 119 (6), 60002, 2017 | 307 | 2017 |
| Stochastic gradient descent for hybrid quantum-classical optimization R Sweke, F Wilde, J Meyer, M Schuld, PK Fährmann, ... Quantum 4, 314, 2020 | 282 | 2020 |
| The future of quantum biology A Marais, B Adams, AK Ringsmuth, M Ferretti, JM Gruber, R Hendrikx, ... Journal of the Royal Society Interface 15 (148), 20180640, 2018 | 254 | 2018 |
| Is quantum advantage the right goal for quantum machine learning? M Schuld, N Killoran Prx Quantum 3 (3), 030101, 2022 | 192 | 2022 |
