Maria Schuld
Maria Schuld
Quantum Machine Learning research lead at Xanadu
Zweryfikowany adres z xanadu.ai
Cytowane przez
Cytowane przez
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
Quantum machine learning in feature Hilbert spaces
M Schuld, N Killoran
Physical review letters 122 (4), 040504, 2019
An introduction to quantum machine learning
M Schuld, I Sinayskiy, F Petruccione
Contemporary Physics 56 (2), 172-185, 2015
Evaluating analytic gradients on quantum hardware
M Schuld, V Bergholm, C Gogolin, J Izaac, N Killoran
Physical Review A 99 (3), 032331, 2019
Circuit-centric quantum classifiers
M Schuld, A Bocharov, KM Svore, N Wiebe
Physical Review A 101 (3), 032308, 2020
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
The quest for a quantum neural network
M Schuld, I Sinayskiy, F Petruccione
Quantum Information Processing 13, 2567-2586, 2014
Supervised learning with quantum computers
M Schuld, F Petruccione
Springer, 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
Continuous-variable quantum neural networks
N Killoran, TR Bromley, JM Arrazola, M Schuld, N Quesada, S Lloyd
Physical Review Research 1 (3), 033063, 2019
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
Prediction by linear regression on a quantum computer
M Schuld, I Sinayskiy, F Petruccione
Physical Review A 94 (2), 022342, 2016
Implementing a distance-based classifier with a quantum interference circuit
M Schuld, M Fingerhuth, F Petruccione
Europhysics Letters 119 (6), 60002, 2017
Quantum embeddings for machine learning
S Lloyd, M Schuld, A Ijaz, J Izaac, N Killoran
arXiv preprint arXiv:2001.03622, 2020
Transfer learning in hybrid classical-quantum neural networks
A Mari, TR Bromley, J Izaac, M Schuld, N Killoran
Quantum 4, 340, 2020
Stochastic gradient descent for hybrid quantum-classical optimization
R Sweke, F Wilde, J Meyer, M Schuld, PK Fährmann, ...
Quantum 4, 314, 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
Supervised quantum machine learning models are kernel methods
M Schuld
arXiv preprint arXiv:2101.11020, 2021
Simulating a perceptron on a quantum computer
M Schuld, I Sinayskiy, F Petruccione
Physics Letters A 379 (7), 660-663, 2015
Quantum gradient descent and Newton’s method for constrained polynomial optimization
P Rebentrost, M Schuld, L Wossnig, F Petruccione, S Lloyd
New Journal of Physics 21 (7), 073023, 2019
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