Research
Research Highlights
Metrics
Authored 27 research papers, including 15 as co-first or corresponding author, with a total of 753 citations (h-index: 15, i10-index: 22) according to Google Scholar.
Open-sourced packages
I am the main developer of PyClifford, an efficient Python-based simulator for Clifford-gate dominated quantum circuits. It has been widely used by the community and supported by the Unitary Fund.
A selection of my research discoveries in the quantum world.
H.-Y. Hu, A. Gu, S. Majumder, H. Ren, Y. Zhang, D. S. Wang, Y.-Z. You, Z. Minev, S. F. Yelin, A. Seif
“Demonstration of robust and efficient quantum property learning with shallow shadows.”
Nature Communications 16, 2943 (2025)
Highlight: We investigate the impact of noise in shallow shadow protocols and introduce a noise-robust shallow shadow approach powered by Bayesian learning. We theoretically prove its robustness and, for the first time, demonstrate noise-resilient classical shadows with shallow circuits on a superconducting quantum device. This method enables simultaneous prediction of multiple observables—including Pauli observables, Rényi entropy, and quantum fidelity—on real, noisy hardware. Notably, shallow shadows retain favorable sample complexity even in the presence of noise.
Media Coverage: Featured by Quantum China, Science Magazine, Phys.org, UC San Diego Today, Interesting Engineering, and Japanese Tech News.
H.-Y. Hu, S. Choi, Y.-Z. You
“Classical shadow tomography with locally scrambled quantum dynamics.”
Physical Review Research 5, 023027 (2023)
Highlight: We present the first randomized measurement scheme (classical shadows) beyond the Pauli/Clifford paradigms, enabling efficient estimation from shallow or analog quantum circuits. This opens new directions in randomized measurements, noise-robust tomography, and practical use on near-term devices.
Media Coverage: Highlighted at QIP 2022 in the Randomized Measurement Toolbox review and cited over 100 times.
D. Mark*, H.-Y. Hu*, J. Kwan, C. Kokail, S. Choi, S. F. Yelin
“Efficiently measuring d-wave pairing and beyond in quantum gas microscopes.”
arXiv:2412.13186 (Physical Review Letters, under review)
Highlight: We introduce a protocol for measuring a broad class of observables in fermionic quantum gas microscopes—most notably long-range superconducting pairing correlations—using only global controls followed by site-resolved measurements. This enables, for the first time, experimental access to d-wave pairing in optical lattices, and can be viewed as efficient parallel quantum gates on cold atom systems.
H.-Y. Hu*, M. Ma*, W. Gong, Q. Ye, Y. Tong, S. T. Flammia, S. F. Yelin
“Ansatz-free Hamiltonian learning with Heisenberg-limited scaling.”
arXiv:2502.11900 (PRX Quantum, under review)
Highlight: We address whether one can achieve Heisenberg-limited Hamiltonian learning without prior assumptions about locality or structure. This work proposes a black-box quantum algorithm that, for the first time, achieves Heisenberg-limited scaling using only real-time evolution and minimal control. We also prove a fundamental lower bound connecting system controllability with time complexity.
Media Coverage: Selected for a 30-minute-long talk at AQIS 2025 (acceptance rate: 4%).
M. Kornjača*, H.-Y. Hu*, et al.
“Large-scale quantum reservoir learning with an analog quantum computer.”
arXiv:2407.02553 (Science Advances, under review)
Highlight: Using neutral-atom analog quantum computers, we develop a general-purpose, gradient-free, scalable quantum reservoir learning algorithm. This work demonstrates the largest quantum machine learning experiment to date, with up to 108 qubits, on tasks including classification and time-series prediction. We also show comparative quantum kernel advantage via synthetic geometric benchmarks.
Media Coverage: Featured by Quantum Insider News and QuEra News.