Pennylane: Automatic differentiation of hybrid quantum-classical computations V Bergholm, J Izaac, M Schuld, C Gogolin, MS Alam, S Ahmed, ... arXiv preprint arXiv:1811.04968, 2018 | 295 | 2018 |
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 | 162 | 2021 |
Stochastic gradient descent for hybrid quantum-classical optimization R Sweke, F Wilde, J Meyer, M Schuld, PK Fährmann, ... Quantum 4, 314, 2020 | 130 | 2020 |
A variational toolbox for quantum multi-parameter estimation JJ Meyer, J Borregaard, J Eisert npj Quantum Information 7 (1), 1-5, 2021 | 31 | 2021 |
Fisher information in noisy intermediate-scale quantum applications JJ Meyer Quantum 5, 539, 2021 | 28 | 2021 |
Encoding-dependent generalization bounds for parametrized quantum circuits MC Caro, E Gil-Fuster, JJ Meyer, J Eisert, R Sweke Quantum 5, 582, 2021 | 23 | 2021 |
Training quantum embedding kernels on near-term quantum computers T Hubregtsen, D Wierichs, E Gil-Fuster, PJHS Derks, PK Faehrmann, ... arXiv preprint arXiv:2105.02276, 2021 | 18 | 2021 |
Exploiting symmetry in variational quantum machine learning JJ Meyer, M Mularski, E Gil-Fuster, AA Mele, F Arzani, A Wilms, J Eisert arXiv preprint arXiv:2205.06217, 2022 | 7 | 2022 |
Gradients just got more flexible JJ Meyer Quantum Views 5, 50, 2021 | 2 | 2021 |
Classical surrogates for quantum learning models FJ Schreiber, J Eisert, JJ Meyer arXiv preprint arXiv:2206.11740, 2022 | | 2022 |