Our Company
We recognized that the future of AI lies not in scaling models, but in structuring them. QuStruct.AI was incorporated to commercialize tensor-native AI infrastructure for the Quantum-AI era.
Three walls block AI's next leap: data is exhausted, the cost curve is unsustainable, and probabilistic models cannot guarantee accuracy. Our answer is structure — tensor-native compression that reduces parameters by orders of magnitude while preserving capability, and runs on classical and quantum hardware alike.
Our team and partners come from Georgia Tech, KAUST, the University of Washington, Xanadu (Nasdaq: XNDU), Microsoft Research, and the Hon Hai Research Institute — combining deep expertise in quantum information, tensor computing, large-scale signal processing, and institutional finance.
Founder
Tsinghua University → University of Washington → Georgia Tech. Pioneer of tensor-structured parameterization for scalable AI and quantum systems. Recipient of the IEEE Signal Processing Letters Best Paper Award (2025). Published in npj Quantum Information and IEEE Transactions.
2020–2023
Establishing the theoretical upper bound, error analysis, and fundamental theory for tensor-structured AI.
2024
Pioneering distributed collaboration and natural-gradient methods that extend tensor networks to deeper, larger models.
2025
Founder Dr. Jun Qi receives the IEEE Signal Processing Letters Best Paper Award for advances in tensor-structured parameterization.
2025
Structure-aware compression achieves ~2,900× parameter reduction with near-zero accuracy loss, validated on the 156-qubit IBM Heron processor.
2026
QuStruct.AI is incorporated in Singapore to commercialize tensor-native AI infrastructure for the Quantum-AI era.
2026
Collaborative R&D programs with leading quantum and AI hardware providers, including NVIDIA via the CUDA-Q stack.
In collaboration with