18 citations · 22 across the 18 of their papers we have counts for
12 papers · 2 filters
A provable quantum advantage for approximate optimization via decoded quantum interferometry
Maximilian J. Kramer, Elies Gil-Fuster, Benjamin D. M. Jones +2
Decoded quantum interferometry (DQI) is a novel paradigm for tackling approximate optimization problems on quantum computers. This framework comes with strong performance guarantee…
Adaptivity is all you need: Optimal stabilizer learning using just single-copy measurements
L. Bittel, J. Eisert, W. Gong +2
Stabilizer states are central to quantum computing, underlying quantum error correction, benchmarking, and efficient classical simulation. Yet their learnability exhibits a strikin…
Testing quantum Gaussianity with constant sample complexity
Mahtab Yaghubi Rad, Ricard Puig, Saksham Hassanandani +4
Efficiently testing whether a quantum state possesses a given structure is both a fundamental and a practical task in quantum information. Among the most important structured famil…
The power of oracle access: Optimal sample and query complexity of the abelian state hidden subgroup problem
Yuhan Liu, Jose Carrasco, Jens Eisert +1
In the quest to identify further quantum algorithms exhibiting superpolynomial speed-ups, a recurring theme is that the complexity of a problem is largely shaped by the input acces…
Efficient learning of Clifford disentanglers and typical -doped unitaries with exponentially more gates
Gerard Aguilar, Sofiene Jerbi, Jens Eisert +1
Highly entangled and highly non-stabilizer quantum states need not be hard to learn. We give efficient algorithms for testing and recovering hidden tensor-product structure in unkn…
Cautious optimism for deep parameterized quantum circuits
Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performa…
Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods
Elies Gil-Fuster, Matan Ninio, Lennart Bittel +4
Optimization problems are among the leading candidates for industrially relevant quantum advantage. Decoded quantum interferometry (DQI) has been proposed to tackle approximate opt…
Provable learning separation for predicting time-evolution of quantum many-body systems
Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert +2
Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantu…
Optimal stellar rank approximation of squeezed cat states with photon catalysis
Julian K. Nauth, Nathan Walk, Ananga M. Datta +4
Non-Gaussian quantum states and operations constitute essential resources for achieving quantum computational advantage and enabling quantum error correction in bosonic platforms.…
Optimal algorithmic complexity of inference in quantum kernel methods
Elies Gil-Fuster, Seongwook Shin, Sofiene Jerbi +2
Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of inference: evaluating a trained mode…
A PAC-Bayesian approach to generalization for quantum models
Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert +3
Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacit…
Simulation of noisy quantum circuits using frame representations
Janek Denzler, Jose Carrasco, Jens Eisert +1
One of the core research questions in the theory of quantum computing is to find out to what precise extent the classical simulation of a noisy quantum circuits is possible and whe…