~2,900×
Parameter Reduction
11.69M → 4,035 parameters in a structure-aware compressed model
Founder's IEEE Signal Processing Letters Best Paper Award · 2025
Redefining AI Efficiency Through Structured Quantum Intelligence
We build tensor-native AI infrastructure that compresses models by over 50× with near-zero accuracy loss — deployable across GPU, CPU, and QPU.
In collaboration with
IBM
NVIDIA
Quantinuum
Georgia Tech
KAUST
U. Washington
Xanadu
Microsoft Research
Hon Hai (Foxconn)
IBM
NVIDIA
Quantinuum
Georgia Tech
KAUST
U. Washington
Xanadu
Microsoft Research
Hon Hai (Foxconn)
For AI infrastructure teams
Build Models, Not Just Train Them
Structure-aware tensor decomposition generates compressed models natively — no post-hoc pruning, no accuracy tradeoff.
Learn more
For edge deployment
Powerful AI on Constrained Hardware
Deploy production-grade models on CPUs and edge devices with up to 90% reduction in compute and memory.
Learn more
For enterprise & finance
Deterministic AI for Mission-Critical
Zero logical capability loss. Tensor-native compression delivers guaranteed accuracy for high-stakes financial and enterprise systems.
Learn more
For quantum-ready teams
Native Quantum Advantage, Today
TensorDual-VQC bridges GPU and QPU workloads, resolves barren plateaus, and runs on real NISQ hardware including the 156-qubit IBM Heron.
Learn more
Core Technology
Structure-Aware Model Generation
Unlike pruning or distillation — which are forms of lossy compression — TensorHyper generates AI models natively through tensor decomposition. The result: extreme compression ratios with near-zero accuracy loss, deployable across GPU, CPU, and QPU.
>50×
Parameter Compression
Up to
90%
Compute & Memory Reduction
~100%
Accuracy Retention
Acceleration
10×
Combinatorial Search & Sampling
Validated experiment: 11.69M → 4,035 parameters (~2,900× reduction) on a structure-aware compressed model.
Coming Soon
GPU · CPU · QPU on a single tensor representation
Heterogeneous Computing
QuStruct AI Platform
A unified infrastructure for running AI workloads across GPU, CPU, and QPU. Manage tensor model deployment, monitor compression benchmarks, and seamlessly transition from classical to quantum-native compute — all from one control plane.
Proven Performance
Our tensor-native models have been validated against state-of-the-art benchmarks.
~100%
Accuracy Retention
Compressed model matches — and in some runs exceeds — the uncompressed baseline
Stable
Deep VQC Training
Residual optimization keeps gradient variance from collapsing — no barren plateau
156-qb
IBM Heron Validation
TensorDual-VQC runs on real superconducting hardware, not just simulators
Sources: structure-aware compression experiment (BP §2.4) and 156-qubit IBM Heron validation (BP §2.6 / §5).
Explore Our AI Models
Structure-aware tensor decomposition
TensorHyper
GPU-native tensor network infrastructure. Validated at ~2,900× parameter reduction with zero logical capability loss — the highest compression-accuracy ratio measured in the industry.
Unified GPU + QPU runtime · Resolves barren plateau
TensorDual-VQC
Quantum-native variant bridging GPU and QPU. Resolves barren plateaus, enables stable deep-circuit training, and runs on real quantum hardware including the 156-qubit IBM Heron — delivering the first scalable quantum advantage.
Milestones
From theory to scalable quantum AI.
2020–2023
Theoretical Foundation
Establishing the theoretical upper bound, error analysis, and fundamental theory for tensor-structured AI.
2024
Distributed & Natural Gradient
Pioneering distributed collaboration and natural-gradient methods that extend tensor networks to deeper, larger models.
2025
IEEE SPL Best Paper Award
Founder Dr. Jun Qi receives the IEEE Signal Processing Letters Best Paper Award for advances in tensor-structured parameterization.
2025
TensorHyper Validated on IBM Heron
Structure-aware compression achieves ~2,900× parameter reduction with near-zero accuracy loss, validated on the 156-qubit IBM Heron processor.
2026
QuStruct.AI Founded in Singapore
QuStruct.AI is incorporated in Singapore to commercialize tensor-native AI infrastructure for the Quantum-AI era.
2026
Joint R&D with IBM, NVIDIA & Quantinuum
Collaborative R&D programs with leading quantum and AI hardware providers, including NVIDIA via the CUDA-Q stack.
Designed for efficiency-driven verticals.
Finance & BankingDefense & AerospaceHealthcare & Life SciencesEnergy & UtilitiesManufacturingAutonomous SystemsTelecommunicationsCybersecurityResearch & AcademiaEdge Computing
Tensor-native compression is built for industries where efficiency, accuracy, and determinism are non-negotiable.
Three Walls Blocking AI's Next Leap
Scaling Wall
As high-quality internet data becomes scarce, simply scaling compute and model size to drive performance improvements is hitting a bottleneck.
Learn moreEconomic Wall
The operating costs of top-tier models remain prohibitively high, leaving the per-interaction cost of AI services significantly higher than traditional search.
Learn moreTrust Wall
The inherent nature of probabilistic prediction means AI cannot guarantee 100% accuracy, limiting its deep application in high-value fields.
Learn moreThe First Scalable Quantum Advantage
TensorDual-VQC
Bridge GPU and QPU Workloads
TensorDual-VQC resolves barren plateaus, eliminates exponential parameter scaling, and enables stable training in deep quantum circuits. Validated on the 156-qubit IBM Heron processor — running today on NISQ devices, without waiting for fault-tolerant hardware.
Our Company
Founded in Singapore in 2026 to build the computational infrastructure for the Quantum-AI era.
We recognized that the future of AI lies not in scaling models, but in structuring them. Our team and partners come from Georgia Tech, KAUST, U. Washington, Xanadu, Microsoft Research, and Hon Hai Research Institute — combining deep expertise in quantum information, tensor computing, large-scale signal processing, and institutional finance.
Meet the teamDr. Jun Qi
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.
Partners & Ecosystem
We conduct joint research and development with IBM, NVIDIA (via CUDA-Q), and Quantinuum, and collaborate with world-class institutions including Georgia Tech, KAUST, the University of Washington, Xanadu (Nasdaq: XNDU), Microsoft Research, and the Hon Hai Research Institute.
