Quantum algorithms, learning and verification
A quantum algorithm for the Abelian state hidden subgroup problem.
Quantum algorithms, learning and verification explore how quantum computers can outperform classical computation and how their correct functioning can be reliably certified. Our research develops rigorous quantum algorithms for a wide range of computational tasks, including combinatorial optimization, quantum machine learning, and quantum simulation, while seeking a deeper understanding of the fundamental capabilities and limitations of quantum computation. Quantum learning theory aims to learn or test properties of quantum systems from limited data. We also develop methods for benchmarking, verification, and Hamiltonian learning based on randomized measurements and classical shadows, enabling the reliable characterization of increasingly complex quantum devices.
Selected recent group publications
- Tight inapproximability of max-LINSAT and implications for decoded quantum interferometry
arXiv:2603.04540 (2026) - Abelian state hidden subgroup problem: Learning stabilizer groups and beyond
PRX Quantum 7, 020337 (2026) - Entanglement theory with limited computational resources
Nature Physics 21, 1847 (2025) - Learning quantum states of continuous variable systems
Nature Physics 21, 2002-2008 (2025) - Verifiable measurement-based quantum random sampling with trapped ions
Nature Communications 16, 106 (2025) - An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory
Science Advances 10, eadj5170 (2024) - Towards provably efficient quantum algorithms for large-scale machine-learning models
Nature Communications 15, 434 (2024) - Understanding quantum machine learning also requires rethinking generalization
Nature Communications 15, 2277 (2024) - Robustly learning the Hamiltonian dynamics of a superconducting quantum processor
Nature Communications 15, 9595 (2024) - Estimating gate-set properties from random sequences
Nature Communications 14, 5039 (2023)
Group reviews
- Mind the gaps: The fraught road to quantum advantage
Nature Physics, in press (2026) - Artificial intelligence for representing and characterizing quantum systems
Nature Reviews Physics, in press (2026) - Computational advantage of quantum random sampling
Reviews of Modern Physics 95, 035001 (2023) - Quantum certification and benchmarking
Nature Reviews Physics 2, 382-390 (2020)