#  Hong-Ye Hu (扈鸿业) 

 



#  Quantum Information + Quantum Control + Machine Learning 

 Harvard Quantum Initiative Research Fellow 

Advancing quantum science through **precise atom control**, **machine learning**, and **engineered innovation**.



 

 

 

       ![background](/sites/g/files/omnuum8601/files/styles/hwp_21_9__1920x825/public/2025-06/Screenshot%202025-06-15%20at%2010.58.06.png?itok=WexG8o2l) 

 

 



 

 



 

## **About Me**

   ![image](/sites/g/files/omnuum8601/files/styles/hwp_1_1__360x360_scale/public/2025-06/DCP_156.jpg?itok=3zKYZIIx) 

 

I am currently a research fellow at the **Harvard Quantum Initiative (HQI)**, specializing in quantum information theory and machine learning I work closely with experimentalists across a variety of quantum platforms—including **neutral atom tweezer arrays**, **optical lattices**, and **superconducting qubits**—where I have designed experimental protocols that have been successfully implemented for a broad range of quantum information processing tasks.

My interests lie in both near-term and fault-tolerant applications of programmable quantum systems including 1. **analog and digital quantum simulation**; 2. **quantum statistical learning**, such as Hamiltonian learning, device benchmarking, and quantum state learning; 3. **quantum optimal control** for efficient gate design; 4. **machine learning for quantum information science**, such as automated error correction code design and efficient decoders for quantum error correction codes.

By combining tools from quantum information theory, quantum optimal control, and machine learning, I aim to advance the capabilities of quantum technologies and the understanding of quantum many-body systems.

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## **Research Topics**

- **Quantum Simulation:** Analog and digital simulation of many-body quantum systems using programmable quantum platforms such as neutral atom arrays, optical lattices, and superconducting circuits.
- **Quantum Statistical Learning**: Scalable methods for Hamiltonian learning, device characterization, randomized benchmarking, and quantum state tomography.
- **Quantum Optimal Control**: Design and implementation of optimal pulse sequences for high-fidelity gate operations, especially under experimental constraints in neutral atoms, optical lattices, and superconducting platforms.
- **Machine Learning for Quantum Information**: Development of ML-based tools for efficient learning and control of quantum systems, which includes automated QEC code design and efficient decoding algorithms.
- **Efficient Quantum Information Processing with Atom Arrays**: Leveraging the flexibility and programmability of Rydberg atom arrays for scalable and expressive quantum computation.
- **Randomized Measurement Toolbox**: Designing and applying randomized measurement protocols for efficient state and process estimation, error mitigation, and hybrid analog-digital quantum computing.
- **Noise-Robust Quantum Protocols**: Developing methods that remain accurate and sample-efficient under realistic noise, including error mitigation strategies.
- **Foundations and Applications of Quantum Information Theory**: Theoretical understanding of quantum entanglement, computational complexity, and information-theoretic limits of near-term quantum devices
- **Quantum Machine Learning Theory**: Theoretical understanding of quantum advantage in learning tasks, including unconditional quantum advantage rooted in quantum entanglement, nonlocality, and contextuality.

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## **Honors**

- **Fellow** of Harvard Quantum Initiative
- Nominee of **UC's President Dissertation Year Fellow** by the Physics Department (2021)
- Chair's Challenge Award recipient, UCSD Physics Department. (2018)
- **Honor title**: ​Weiming scholar, Peking University. (2013-2016)
- **Honor title**: College Graduate Excellence Award of Beijing City. Ministry of Education. (2016)
- **Gold Medal**, China Undergraduate Physics Tournament, Peking University Team (2013)

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## **Community Service**

I am an active reviewer for:

- Nature Communications
- npj Quantum Information
- Physical Review X Quantum
- Physical Review Letters
- Physical Review Research
- Quantum
- Machine Learning Science and Technology
- Quantum Science and Technology
- TQC conference
- AQIS conference
- QIP conference

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## My Career

- **Sept. 2022 - Present**: [Harvard Quantum Initiative](https://quantum.harvard.edu/) & [Harvard-MIT Center for Ultracold Atoms](https://cua.mit.edu/) (Postdoc Fellow)
- **Jun. 2022 - Aug. 2022**: [QuEra Computing Inc.](https://www.quera.com/) (Quantum Algorithm Consultant).
- **Jun. 2021 - Jun. 2022**: [NASA Quantum AI](https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/nasa-quail/) and USRA (Feynman Quantum Academy Intern)
- **Mar. 2016 - Mar. 2022**: PhD, [University of California, San Diego](https://ucsd.edu/), USA
- **Sept. 2016 - Mar. 2018**: [Salk Institute for Biological Studies](https://www.salk.edu/) (Research assistant)
- **Sept. 2012- Jun. 2016**: BS, [Peking University](https://english.pku.edu.cn/), China



 

##  Favorite Quotes 

 



   

*"To learn, read. To know, write, To master, teach"*

 

--- Hindu proverb