Huang, Robert

Article from CaltechAUTHORS

• Huang, Hsin-Yuan and Choi, Soonwon, el al. (2026) Vast World of Quantum Advantage; Physical Review X; Vol. 16; No. 3; 030501; 10.1103/tn89-g1xz
• Jerbi, Sofiene and Gibbs, Joe, el al. (2026) Power and limitations of learning quantum dynamics incoherently; Physical Review Research; Vol. 8; No. 2; 023141; 10.1103/s5xy-lk91
• Huang, Jerry and Lewis, Laura, el al. (2026) Predicting Adaptively Chosen Observables in Quantum Systems; PRX Quantum; Vol. 7; No. 1; 010347; 10.1103/xhn1-vnp9
• Angrisani, Armando and Schmidhuber, Alexander, el al. (2025) Classically Estimating Observables of Noiseless Quantum Circuits; Physical Review Letters; Vol. 135; No. 17; 170602; 10.1103/lh6x-7rc3
• Liu, Zheng-Hao and Brunel, Romain, el al. (2025) Quantum learning advantage on a scalable photonic platform; Science; Vol. 389; No. 6767; 1332-1335; 10.1126/science.adv2560
• Schuster, Thomas and Haferkamp, Jonas, el al. (2025) Random unitaries in extremely low depth; Science; Vol. 389; No. 6755; 92-96; 10.1126/science.adv8590
• Oh, Changhun and Chen, Senrui, el al. (2024) Entanglement-Enabled Advantage for Learning a Bosonic Random Displacement Channel; Physical Review Letters; Vol. 133; No. 23; 230604; 10.1103/physrevlett.133.230604
• Zhao, Haimeng and Lewis, Laura, el al. (2024) Learning Quantum States and Unitaries of Bounded Gate Complexity; PRX Quantum; Vol. 5; No. 4; 040306; 10.1103/prxquantum.5.040306
• Chen, Senrui and Oh, Changhun, el al. (2024) Tight Bounds on Pauli Channel Learning without Entanglement; Physical Review Letters; Vol. 132; No. 18; 180805; 10.1103/physrevlett.132.180805
• Gibbs, Joe and Holmes, Zoë, el al. (2024) Dynamical simulation via quantum machine learning with provable generalization; Physical Review Research; Vol. 6; No. 1; 013241; 10.1103/physrevresearch.6.013241
• Zhan, Yongtao and Elben, Andreas, el al. (2024) Learning Conservation Laws in Unknown Quantum Dynamics; PRX Quantum; Vol. 5; No. 1; 010350; 10.1103/prxquantum.5.010350
• Lewis, Laura and Huang, Hsin-Yuan, el al. (2024) Improved machine learning algorithm for predicting ground state properties; Nature Communications; Vol. 15; 895; 10.1038/s41467-024-45014-7
• Huang, Hsin-Yuan and Chen, Sitan, el al. (2023) Learning to Predict Arbitrary Quantum Processes; PRX Quantum; Vol. 4; No. 4; 040337; 10.1103/prxquantum.4.040337
• Chen, Sitan and Cotler, Jordan, el al. (2023) The complexity of NISQ; Nature Communications; Vol. 14; 6001; 10.1038/s41467-023-41217-6
• Caro, Matthias C. and Huang, Hsin-Yuan, el al. (2023) Out-of-distribution generalization for learning quantum dynamics; Nature Communications; Vol. 14; Art. No. 3751; PMCID PMC10322910; 10.1038/s41467-023-39381-w
• Huang, Hsin-Yuan and Tong, Yu, el al. (2023) Learning Many-Body Hamiltonians with Heisenberg-Limited Scaling; Physical Review Letters; Vol. 130; No. 20; 200403; 10.1103/physrevlett.130.200403
• Choi, Joonhee and Shaw, Adam L., el al. (2023) Preparing random states and benchmarking with many-body quantum chaos; Nature; Vol. 613; No. 7944; 468-473; 10.1038/s41586-022-05442-1
• Elben, Andreas and Flammia, Steven T., el al. (2023) The randomized measurement toolbox; Nature Reviews Physics; Vol. 5; No. 1; 9-24; 10.1038/s42254-022-00535-2
• Cotler, Jordan S. and Mark, Daniel K., el al. (2023) Emergent Quantum State Designs from Individual Many-Body Wave Functions; PRX Quantum; Vol. 4; No. 1; Art. No. 010311; 10.1103/prxquantum.4.010311
• Huang, Hsin-Yuan and Kueng, Richard, el al. (2022) Provably efficient machine learning for quantum many-body problems; Science; Vol. 377; No. 6613; Art. No. abk3333; 10.1126/science.abk3333
• Cerezo, M. and Verdon, Guillaume, el al. (2022) Challenges and opportunities in quantum machine learning; Nature Computational Science; Vol. 2; No. 9; 567-576; 10.1038/s43588-022-00311-3
• Huang, Hsin-Yuan and Broughton, Michael, el al. (2022) Quantum advantage in learning from experiments; Science; Vol. 376; No. 6598; 1182-1186; 10.1126/science.abn7293
• Huang, Hsin-Yuan (2022) Learning quantum states from their classical shadows; Nature Reviews Physics; Vol. 4; No. 2; Art. No. 81; 10.1038/s42254-021-00411-5
• Huang, Hsin-Yuan and Bharti, Kishor, el al. (2021) Near-term quantum algorithms for linear systems of equations with regression loss functions; New Journal of Physics; Vol. 23; No. 11; Art. No. 113021; 10.1088/1367-2630/ac325f
• McClean, Jarrod R. and Rubin, Nicholas C., el al. (2021) What the foundations of quantum computer science teach us about chemistry; Journal of Chemical Physics; Vol. 155; No. 15; Art. No. 150901; 10.1063/5.0060367
• Chen, Chi-Fang and Huang, Hsin-Yuan, el al. (2021) Concentration for Random Product Formulas; PRX Quantum; Vol. 2; No. 4; 040305; 10.1103/prxquantum.2.040305
• Huang, Hsin-Yuan and Kueng, Richard, el al. (2021) Efficient Estimation of Pauli Observables by Derandomization; Physical Review Letters; Vol. 127; No. 3; Art. No. 030503; 10.1103/PhysRevLett.127.030503
• Huang, Hsin-Yuan and Kueng, Richard, el al. (2021) Information-Theoretic Bounds on Quantum Advantage in Machine Learning; Physical Review Letters; Vol. 126; No. 19; Art. No. 190505; 10.1103/PhysRevLett.126.190505
• Huang, Hsin-Yuan and Broughton, Michael, el al. (2021) Power of data in quantum machine learning; Nature Communications; Vol. 12; Art. No. 2631; 10.1038/s41467-021-22539-9
• Elben, Andreas and Kueng, Richard, el al. (2020) Mixed-State Entanglement from Local Randomized Measurements; Physical Review Letters; Vol. 125; No. 20; Art. No. 200501; 10.1103/physrevlett.125.200501
• Huang, Hsin-Yuan (Robert) and Kueng, Richard, el al. (2020) Predicting Many Properties of a Quantum System from Very Few Measurements; Nature Physics; Vol. 16; No. 10; 1050-1057; 10.1038/s41567-020-0932-7