Quantum-JEPA & world models
Combine joint-embedding predictive architectures with quantum generative models to study latent future distributions and uncertainty sampling.
R&D direction · Multimodal prediction and Physical AI
QINOUS / QUANTUM INTELLIGENCE
Quantum for AI.
A new paradigm
of intelligence.
Quantum for AI
We develop quantum AI algorithms and distributed quantum computing architectures. AI applications guide our research across hardware, error correction and native quantum algorithms.
01 / ABOUT QINOUS
Quantum Intelligence.
Physics meets mind.
Founded in July 2026, QINOUS focuses on quantum AI algorithms and distributed quantum computing hardware. We connect AI applications with native quantum algorithms and scalable computing architectures.
Our industrial path begins with integrating quantum resources into supercomputing workflows, then progresses toward quantum-native AI. Applications and algorithms advance alongside hardware and error-correction research.
Q + I + NOUS
QI stands for Quantum Intelligence. NOUS draws on the ancient Greek concept of mind and intellect. Qi in our Chinese name evokes opening, inspiration and the cultivation of intelligence. Together, they express our inquiry into intelligence through a new physical computing paradigm.
- Quantum computing
- Software & algorithms
- Artificial intelligence
02 / RESEARCH & ENGINEERING
AI-led.
Designed as a system.
Application development, native algorithms and distributed hardware form one coordinated research programme.
01
AI applications
Quantum-JEPA world models, generative sampling and scientific computing connect research with AI workloads.
APPLICATION-LED RESEARCH
02
Native quantum algorithms
Build on FQE, QRDR and enhanced quantum solvers to explore quantum linear algebra, learning and optimization.
QUANTUM-NATIVE ALGORITHMS
03
Distributed quantum
hardware
Start with superconducting systems and develop modular interconnects, quantum interfaces and error-correction co-design toward heterogeneous computing.
DISTRIBUTED QUANTUM COMPUTING
APPLICATIONS / RESEARCH OPPORTUNITIES
AI applications
Quantum-JEPA, chemistry and Earth Science guide our application research.
AI for Chemistry
Study generative modelling of protein and molecular conformations, Boltzmann sampling and molecular energy landscapes.
R&D direction · Drug discovery, materials and energy
Earth Science & scientific computing
Explore quantum–supercomputing workflows for ocean and climate tasks, simulation and scientific computation.
Application exploration · Quantum–classical workflows
METHOD / VALIDATION
Four layers.
One coordinated design.
Hardware, error correction, algorithms and applications are developed together.
Scalable hardware
Modular quantum nodes, quantum interconnects and coherent state transfer are considered from the architecture stage.
Hardware layer
Error correction
Codes, syndrome measurements and logical-qubit encoding are designed with the physical platform. Explore reinforcement learning for correction strategies.
Error-correction layer
Algorithm adaptation
Match sampling, linear algebra and optimization methods to the resources and constraints of different quantum platforms.
Algorithm layer
AI integration
Connect quantum modules to generative sampling, world-model prediction and learning pipelines through hybrid workflows.
Application layer
CLASSICAL · Preprocessing / GPU training / APIsHYBRID · Encoding / Scheduling / RoutingQUANTUM · Native algorithms / Distributed execution
ROADMAP / R&D MILESTONES
Industrial progression.
Parallel research.
Quantum–supercomputing integration provides the first application path. Algorithms, applications, hardware and correction research advance in parallel toward quantum-native AI.
2026–2027 · Foundations
Validate algorithms across simulators and available quantum platforms. Advance Quantum-JEPA, scientific applications and small-scale distributed experiments.
Planned milestone · Platform fit and baseline validation
2027–2028 · Integration
Explore multi-node interconnection and coordinated error correction. Evaluate application performance and algorithm–hardware fit.
Planned milestone · Interconnect and performance evaluation
2028–2029 and beyond
Develop a quantum-native AI algorithm library and integrated workflows, expanding toward heterogeneous networks and broader application evaluation.
Planned milestone · Native architectures and scale
R&D milestones describe the planned direction. Delivery depends on experimental results, computing resources and engineering validation.
COLLABORATION / PROPOSED WORKFLOW
A clear path from question
to evaluation.
A proposed framework for technical collaboration. The scope and acceptance criteria are defined together before a project starts.
Scope the problem
Discuss objectives, available data, current methods and constraints.
Proposed output: problem brief and evaluation criteria.
Assess feasibility
Compare candidate routes, computing resources and key uncertainties.
Proposed output: feasibility assessment and experiment plan.
Build & test
Develop a bounded proof of concept with a reproducible baseline.
Proposed output: prototype, experiment records and benchmark results.
Review next steps
Review evidence, limitations and integration requirements.
Proposed output: evaluation report and a jointly agreed next-step plan.
Intelligence Through Coherence
State · Phase · Coherence · Mind
A new computing paradigm. New possibilities for intelligence.
03 / OUR PEOPLE
Across disciplines.
Exploring together.
Application development, hardware, algorithms and error correction, supported by integrated-circuit engineering.
Zhiyuan Liu
Founder & CEO · AI Applications
Research in quantum machine learning and quantum secure direct communication. Engineering doctoral researcher in Electronic Engineering at Tsinghua University, jointly trained with the Department of Physics.
Read biography：Zhiyuan Liu
Research interests include quantum machine learning (QML) and quantum secure direct communication (QSDC). Engineering doctoral researcher in Tsinghua University’s Department of Electronic Engineering, jointly trained with the Department of Physics under Professor Guilu Long.
Previously served as a Senior Engineer at the Guangdong–Hong Kong–Macao Greater Bay Area Quantum Science Center, Deputy Director of the Industrial Embodied Intelligence Center at Tsinghua Shenzhen International Graduate School, Research Supervisor at the Beijing Academy of Quantum Information Sciences, and Head of Research Development at Qiyuan National Laboratory.
Tiefu Li
Chief Scientist · Hardware
Research in superconducting quantum computing, quantum interface devices and hybrid quantum–classical optimization. Undergraduate degree in Electronic Engineering and PhD from the Institute of Microelectronics at Tsinghua University.
Read biography：Tiefu Li
Research interests include quantum computers based on superconducting integrated circuits, quantum interface devices and hybrid quantum–classical optimization algorithms.
Undergraduate degree in Electronic Engineering and PhD from the Institute of Microelectronics at Tsinghua University. Studied under Academician Zhijian Li, Yasunobu Nakamura and Zhaoshen Cai.
Shijie Wei
Chief Algorithm Scientist · Algorithms & Error Correction
Research in quantum computing theory and algorithms. Undergraduate, master’s and doctoral degrees in Physics from Tsinghua University, under Professor Guilu Long.
Read biography：Shijie Wei
Research interests include quantum computing theory and algorithms. Received undergraduate, master’s and doctoral degrees in Physics from Tsinghua University, studying under Professor Guilu Long.
Leads the Quantum Computing Theory and Algorithms Innovation Team at the Beijing Academy of Quantum Information Sciences.
Hanjun Jiang
Integrated-Circuit Engineering Support
Research in low-power analog and RF integrated circuits, energy-efficient wireless sensing chips, and healthcare systems. Undergraduate degree from Tsinghua University and PhD from Iowa State University.
Read biography：Hanjun Jiang
Research interests include low-power analog and radio-frequency integrated circuits, energy-efficient wireless sensing chips, and systems for healthcare applications.
Undergraduate degree from Tsinghua University and PhD from Iowa State University. Tenured Professor and Vice Dean of the School of Integrated Circuits at Tsinghua University.
RESEARCH / FURTHER READING
Research foundations
Published research supports our work in devices, algorithms and quantum machine learning.
Degeneracy-breaking and long-lived multimode microwave electromechanical systems enabled by cubic silicon-carbide membrane crystals
Nature Communications 16, 1207
A multimode microwave electromechanical system based on a 3C-SiC membrane, studying long-lived phononic modes, state storage and intermode energy transfer. The paper reports a maximum slow-light group delay of 4,035 seconds.
Coherent memory for microwave photons based on long-lived mechanical excitations
npj Quantum Information 9, 80
Demonstrates capture, storage and retrieval of coherent microwave fields in mechanical excitations, including recovery of amplitude and phase information.
Evidence of scaling advantage on an NP-complete problem with enhanced quantum solvers
Nature Computational Science
Uses RSRA-enhanced quantum solvers for 1-in-3 SAT, reporting empirical scaling advantages in large-scale numerical studies with supporting superconducting-processor validation.
Quantum resonant dimensionality reduction
Physical Review Research 7, 013007
Uses quantum resonant transitions for dimensionality reduction, with numerical studies involving quantum support vector machines and quantum convolutional neural networks.
A Full Quantum Eigensolver for Quantum Chemistry Simulations
Research 2020, 1486935
Introduces the FQE algorithm using quantum gradient descent for molecular ground-state energies and electronic structures, supported by molecular numerical simulations.
A full circuit-based quantum algorithm for excited-states in quantum chemistry
Quantum 8, 1219
A non-variational circuit-based solver for molecular excited states, with molecular simulations and a superconducting-hardware experimental demonstration.
Selected academic research co-authored by Tiefu Li or Shijie Wei. Attribution and experimental conditions follow the original publications.
04 / LET’S CONNECT
Build the next
research collaboration.
For quantum AI applications, algorithm research, computing-platform integration or hardware collaboration, tell us about your task and available resources.
QINOUS / QUANTUM INTELLIGENCE
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