F. J. Schreiber, J. Eisert, J. J. Meyer,
Classical surrogates for quantum learning models,
Physical Review Letters 131 (10), 100803, 2023,
https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.131.100803.
M. Hinsche, M. Ioannou, A. Nietner, J. Haferkamp, Y. Quek, D. Hangleiter, J. P. Seifert, J. Eisert,
One T Gate Makes Distribution Learning Hard,
Physical Review Letters 130, 240602, 2023,
https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.130.240602.
D. Magano, M. Murça,
Simplifying a classical-quantum algorithm interpolation with quantum singular value transformations,
Physical Review A 106 (6), 062419, 2022,
https://journals.aps.org/pra/pdf/10.1103/PhysRevA.106.062419.
M. Murça, D. Magano, Y. Omar,
Making the cut: two methods for breaking down a quantum algorithm,
https://arxiv.org/abs/2305.10485.
R. Sweke, E. Recio, S. Jerbi, E. Gil-Fuster, B. Fuller, J. Eisert, J. J. Meyer,
Potential and limitations of random fourier features for dequantizing quantum machine learning,
https://arxiv.org/pdf/2309.11647.
J. Eisert,
A note on lower bounds to variational problems with guarantees,
https://arxiv.org/pdf/2301.06142.
C. Bertoni, J. Haferkamp, M. Hinsche, M. Ioannou, J. Eisert, H. Pashayan,
Shallow shadows: Expectation estimation using low-depth random Clifford circuits,
Physical Review Letters 133 (2), 020602, 2024,
https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.133.020602.
N. Pirnay, R. Sweke, J. Eisert, J. P. Seifert,
Superpolynomial quantum-classical separation for density modeling,
Physical Review A 107 (4), 042416, 2023,
https://journals.aps.org/pra/pdf/10.1103/PhysRevA.107.042416.
E. Gil-Fuster, J. Eisert, C. Bravo-Prieto,
Understanding quantum machine learning also requires rethinking generalization,
Nature Communications 15 (1), 2277, 2024,
https://www.nature.com/articles/s41467-024-45882-z.
S. Cichy, P. K. Faehrmann, S. Khatri, J. Eisert,
Perturbative gadgets for gate-based quantum computing: Nonrecursive constructions without subspace restrictions,
Physical Review A 109 (5), 052624, 2024,
https://journals.aps.org/pra/abstract/10.1103/PhysRevA.109.052624.
N. Pirnay, V. Ulitzsch, F. Wilde, J. Eisert, and J. P. Seifert,
An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory,
Science Advances, Vol 10, Issue 11, 2024,
https://www.science.org/doi/10.1126/sciadv.adj5170.
B. Németh, B. Kövér, B. Kulcsár, R. B. Miklósi, A. Gilyén,
On variants of multivariate quantum signal processing and their characterizations,
https://arxiv.org/pdf/2312.09072.
J. J. Meyer, M. Mularski, E. Gil-Fuster, A. A. Mele, F. Arzani, A. Wilms, J. Eisert,
Exploiting symmetry in variational quantum machine learning,
PRX Quantum 4 (1), 010328, 2023,
https://journals.aps.org/prxquantum/pdf/10.1103/PRXQuantum.4.010328.
E. Gil-Fuster, J. Eisert, V. Dunjko,
On the expressivity of embedding quantum kernels,
Machine Learning: Science and Technology 5 (2), 025003, 2024,
https://iopscience.iop.org/article/10.1088/2632-2153/ad2f51/pdf.
Y. Quek, D. Stilck França, S. Khatri, J. J. Meyer, J. Eisert,
Exponentially tighter bounds on limitations of quantum error mitigation,
Nature Physics, 1–11, 2024,
https://www.nature.com/articles/s41567-024-02536-7.
E. Onorati, J. Kitzinger, J. Helsen, M. Ioannou, A. H. Werner, I. Roth, J. Eisert,
Noise-mitigated randomized measurements and self-calibrating shadow estimation,
https://arxiv.org/pdf/2403.04751.
J. Liu, M. Liu, J. P. Liu, Z. Ye, Y. Wang, Y. Alexeev, J. Eisert, L. Jiang,
Towards provably efficient quantum algorithms for large-scale machine-learning models,
Nature Communications 15 (1), 434, 2024,
https://www.nature.com/articles/s41467-023-43957-x.
J. Liu, F. Wilde, A. A. Mele, L. Jiang, J. Eisert,
Stochastic noise can be helpful for variational quantum algorithms,
https://arxiv.org/pdf/2210.06723.
J. J. Meyer, S. Khatri, D. S. França, J. Eisert, P. Faist,
Quantum metrology in the finite-sample regime,
https://arxiv.org/pdf/2307.06370.
B. Bakó, A Glos, Ö. Salehi, Z. Zimborás,
Prog-QAOA: Framework for resource-efficient quantum optimization through classical programs,
https://arxiv.org/abs/2209.03386.