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Quantum Learning
Quantum learning theory investigates how quantum mechanics affects fundamental aspects of learning, such as sample complexity, efficiency, and computational power. The goal is to characterize when and how quantum systems can provide advantages over classical approaches in learning tasks.
Related Publications:
Heisenberg-limited Hamiltonian learning without short-time control, PRX Quantum 7, 043011 (2026).
Optimal copy complexity of quantum state cloning, arXiv:2608.24484 (2026)
Bound entanglement is insufficient for an exponential quantum learning advantage, arXiv:2607.19017 (2026)
On the fundamental resource for exponential advantage in quantum channel learning, Nat. Comm. 17, 1822 (2026)
On the query complexity of unitary channel certification, npj Quantum Inf. 12, 2 (2026)
Quantum learning advantage on a scalable photonic platform, Science 389, 1332 (2025)
Exponential advantage in continuous-variable quantum state learning, arXiv:2501.17633 (2025) (npj Quantum Inf. in press)
Entanglement-enabled advantage for learning a bosonic random displacement channel, Phys. Rev. Lett. 133, 230604 (2024)
NISQ Devices
We study the presence or absence of quantum advantage in NISQ devices, the boundaries of their classical simulability, and the potential applications that emerge from these regimes. Our research particularly focuses on boson sampling, IQP sampling, and quantum error correction.
Related Publications:
Classical Algorithms for Estimating Expectation Values in Linear Optical Circuits, Phys. Rev. Lett. 137, 120602 (2026)
Classical algorithms for measurement-adaptive Gaussian circuits, PRX Quantum 7, 010329 (2026)
Logarithmic-depth quantum simulation of boson sampling, arXiv:2609.18907 (2026)
Fast classical simulation algorithms for free-fermion dynamics with magic input, arXiv:2609.40187 (2026)
On computational complexity and average-case hardness of shallow-depth boson sampling, Quantum 10, 2026 (2026)
Classical simulation of a quantum circuit with noisy magic inputs, arXiv:2601.10111 (2026)
Matrix product state approach to lossy boson sampling and noisy IQP sampling, arXiv:2510.24137 (2025)
Classical algorithms for measurement-adaptive Gaussian circuits, PRX Quantum 7, 010329 (2026)
Classical algorithms for estimating expectation values in linear optical circuits, Phys. Rev. Lett. 137, 120602 (2026).
Quantum Metrology
Quantum metrology studies how quantum resources—squeezing, entanglement, and nonclassical measurements—can boost the precision of parameter estimation beyond classical limits. By tailoring probe states, evolutions, and measurements, it aims to saturate the quantum Cramér–Rao bound and approach Heisenberg-limited scaling. Applications span interferometry, clocks, imaging, sensing of fields and forces, and distributed/networked sensing.
Related Publications:
Exponential quantum advantage in stationary signal sensing, arXiv:2610.14424 (2026)
Ultimate Information Rate for Quantum Sensing under Multilevel Relaxation, arXiv:2609.21418 (2026)
Metrological Benchmarking of Random Quantum Circuits, arXiv:2609.39226 (2026)
Advancing quantum imaging through learning theory, Nat. Comm. 17, 1124 (2026)
All-optical loss-tolerant distributed quantum sensing, npj Quantum Inf. 12, 18 (2026)
Exponential entanglement advantage in sensing correlated noise, arXiv:2410.05878 (2024)
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