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1. Machine Learning (ML)/AI for quantum science and technology
1. Machine Learning (ML)/AI for quantum science and technology
Machine learning (ML) has become a key enabling layer in quantum science and technology, providing scalable approaches to infer models, estimate parameters, and learn control policies from noisy experimental data (see Rev. Mod. Phys. 91, 045002 (2019)). In recent years, we leveraged deep learning to achieve quantum control in stochastic (randomized) environments [1]. I have also explored the use of generative AI for error/noise characterization, including atomic force sensors [2] and quantum process tomography on a superconducting processor [3]. Going forward, I will continue to develop ML-driven methods for quantum technologies, with an emphasis on error mitigation and robust optimal control.
Machine-Learning-Assisted Quantum Control in a Random Environment
In this work, we introduce proof of the concept and analyze a neural-network-based (convolutional neural networks) machine-learning algorithm for achieving feasible high-fidelity quantum control of a particle in random environment.
■[1] Tangyou Huang, Yue Ban, E. Ya. Sherman and Xi Chen, Physical Review Applied 17, 024040 (2022).
Quantum Force Sensing by Digital Twinning of Atomic Bose-Einstein Condensates
We propose a novel application of digital twinning for quantum force sensing in atomic BECs. Our findings demonstrate a significant advancement in sensitivity, achieving an order of magnitude improvement over conventional protocols in detecting a weak force of approximately 10^-25 N.
■[2] Tangyou Huang, Zhongcheng Yu, Zhongyi Ni, Xiaoji Zhou, and Xiaopeng Li, Communications Physics 7, 172 (2024), arXiv: 2307.00484 (2023).
Quantum Process Tomography with Digital Twins of Error Matrices
Quantum Process Tomography with Digital Twins of Error Matrices
We propose an enhanced quantum process tomography (QPT) for multi-qubit systems by integrating the error matrix in a digital twin of the identity process matrix, enabling statistical refinement of SPAM error learning and improving QPT precision. We further validate our method experimentally using superconducting quantum gates, achieving at least an order-of-magnitude fidelity improvement over standard QPT.
■ [3] Tangyou Huang, et al, Phys. Rev. Lett. 135, 230601 (2025); arXiv:2505.07725 (2025).
2.Quantum information, computing and algorithms
2.Quantum information, computing and algorithms
Quantum optimal control by variational quantum algorithms
In this context, using the hybrid quantum-classical algorithm, we put forward its use for optimal quantum control. We simulate the wave-packet expansion of a trapped quantum particle on a quantum device with a finite number of qubits. The combination of digital quantum simulation and hybrid circuit learning opens up new prospects for quantum optimal control.
■ Tangyou Huang, Yongcheng Ding, Léonce Dupays, Yue Ban, Man-Hong Yung, Adolfo del Campo and Xi Chen, Physical Review Research 5, 023173 (2023).
■T.Y. Huang*, J.J. Zhu and Z.Y Ni, arxiv: 2505.23373 (2025).
Engineering Fault-tolerant Bosonic Codes with Quantum Lattice Gates
We introduce a new universal quantum gate set composed of only one type of gate element, which we call the quantum lattice gate, to engineer bosonic code states for fault-tolerant quantum computing. Our proposal is particularly well-suited for superconducting circuit architectures with Josephson junctions, offering an alternative path to bulit continous-variable and fault-tolerant quantum computing.
■Lingzhen Guo, Tangyou Huang* and Lei Du*, Communications Physics, 8, 414(2025). arxiv:2410.17069 (2024).
3. Shortcuts to Adiabaticity and optimal control
3. Shortcuts to Adiabaticity and optimal control
Shortcuts to adiabaticity for nonlinear quantum systems with variational principle
Inspired by approaches based on the variational approximation (VA) similar to those developed in nonlinear optics we derive a generalized Ermakov equation, including a term induced by the self-interaction term in the GP equation. The objective is to further elaborate shortcut-to-adiabaticity for the adiabatic expansion/compression in BEC. This allows us to manipulate nonlinear dynamics of BEC solitons by means of the Feshbach resonance and many-body dynamics in power-law potentials.
■Tangyou Huang, Boris A. Malomed and Xi Chen, Chaos 30, 053131 (2020).
■Tangyou Huang, Jia Zhang, Jing Li and Xi Chen, Physical Review A 102, 053313 (2020).
■Tangyou Huang, Michele Modugno and Xi Chen, Physical Review A 104, 063313 (2021).
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