Research Interests

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. We show that the accuracy of the proposed algorithm is enhanced by a higher-dimensional mapping of the disorder pattern and using two neural networks, each properly trained for the given task. 

■Tangyou Huang, Yue Ban, E. Ya. Sherman and Xi Chen, Physical Review Applied 17, 024040 (2022).

 We propose and investigate enhanced 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. Through numerical simulations, we demonstrate that our approach enables highly accurate and faithful process characterization. We further validate our method experimentally using superconducting quantum gates, achieving at least an order-of-magnitude fidelity improvement over standard QPT. Our results provide a practical and precise method for assessing quantum gate fidelity and enhancing QPT on a given hardware.

■ Tangyou Huang, et al,  Phys. Rev. Lett. 135, 230601 (2025); arXiv:2505.07725 (2025).

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).

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).

Optimal Control by Variational Quantum Algorithms

In this work, we employ the hybrid framework that integrates digital quantum simulation with classical optimization to achieve optimal engineering of quantum many-body systems. To evaluate the overall performance of this method, we introduce a general metric termed control optimality, which accounts for constraints on both classical and quantum components. As a concrete example, we investigate the time-optimal control for perfect state transfer in a one-dimensional spin model using the variational quantum algorithm, closely approaching the quantum speed limit. 

■T.Y. Huang*, J.J. Zhu and Z.Y Ni, arxiv: 2505.23373 (2025).