Min-Hsiu Hsieh

謝明修
Director, Hon Hai (Foxconn) Quantum Computing Research Center
Research vision

Building reliable quantum computation from theory to useful systems

My research asks how rigorous quantum theory can become reliable, verifiable, and useful computation, moving from fundamental limits to systems and workflows that can be tested in practice.

Research map

How the topics connect

The four research areas form a cycle: foundations define limits, hardware implements reliable systems, software computes under constraints, and applications feed new questions back.

Reliable quantum systems

The shared target of the cycle.

01

Foundations & Theory

Defines

Establishes limits, resource tradeoffs, and information-theoretic questions that guide the program.

02

Hardware Enabling Technology

Implements

Turns those limits into architectures, error-correcting structures, and benchmarks for reliable quantum computation.

03

Software with Enhanced Advantage

Computes

Builds algorithms, learning protocols, and verification tools that operate under realistic system constraints.

04

Real-World Applications

Deploys

Uses domain workflows to test what is useful, expose bottlenecks, and send new constraints back into the program.

Research categories

Four connected research areas

These areas organize the group's work from enabling hardware-level reliability to software advantage, real-world workflows, and the mathematical foundations underneath them.

Defines

Foundations & Theory

Exploring the fundamental limits, information-theoretic principles, and physical structures that make quantum information processing possible.

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Implements

Hardware Enabling Technology

Building the physical and architectural foundation for reliable, scalable quantum computation through error correction, benchmarking, and system-aware design.

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Deploys

Real-World Applications

Translating quantum capabilities into practical workflows for simulation, sensing, communication, and domain problems where structure matters.

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Computes

Software with Enhanced Advantage

Developing algorithms, quantum learning protocols, and verification tools that turn quantum resources into measurable computational advantages.

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Recent outputs

Recent and representative publications

A compact entry point into the publication record, with emphasis on active themes across fault tolerance, learning, and quantum information.

2026 / Preprint
Linear-Time Encodable and Decodable Quantum Error-Correcting Codes
2026 / Nature Reviews Physics
Artificial intelligence for representing and characterizing quantum systems
2025 / Nature Communications
Almost Optimal Geometrically Local Quantum LDPC Codes in any Dimension
2025 / Quantum Machine Intelligence
Quantum-Train: rethinking hybrid quantum-classical machine learning in the model compression perspective
Full list
Browse journals, conferences, talks, books, and arXiv preprints.