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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:
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)
On the fundamental resource for exponential advantage in quantum channel learning, arXiv:2507.11089 (2025) (Nat. Comm. accepted)
Exponential advantage in continuous-variable quantum state learning, arXiv:2501.17633 (2025)
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 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, arXiv:2509.00746 (2025) (PRX Quantum accepted)
Efficient classical algorithms for linear optical circuits, arXiv:2502.12882 (2025)
Quantum-inspired classical algorithm for molecular vibronic spectra, Nat. Phys. 20, 225 (2024)
Quantum-inspired classical algorithm for graph problems by Gaussian boson sampling, PRX Quantum 5, 020341 (2024)
Classical algorithm for simulating experimental Gaussian boson sampling, Nat. Phys. 20, 1461 (2024)
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:
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)
Quantum metrological power of continuous-variable quantum networks, Phys. Rev. Lett. 128, 180503 (2022)
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