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Research Project

Quantum-Inspired
Neural Networks

Hybrid classical-quantum architecture for enhanced learning and decision-making. Open research platform for Industry 4.0, robotics, and autonomous systems.

3
Modes
Hybrid
Architecture
Open
Research
Quantum Memory Network
Active
Input
Classical Data
Encode
|ψ₀⟩
|ψ₁⟩
|ψ₂⟩
Memory
Quantum Memory Layer
Neural
Output
Enhanced Prediction
3 qubits
99.2% fidelity
⚡ 0.8ms
Quantum Circuit - 3 Qubits

Three Experimentation Modes

From theoretical algorithms to real quantum hardware deployment

Theoretical

Algorithm development and mathematical proofs. Design quantum-inspired memory mechanisms and neural architectures.

Simulated

Classical simulation and validation. Test algorithms on standard hardware before quantum deployment.

Real Hardware

Quantum device deployment. Run experiments on actual quantum computers and hybrid systems.

Research Specifications

Hybrid architecture combining classical deep learning with quantum-inspired memory

Technical Specifications

Architecture
Hybrid
Memory Type
Quantum-Inspired
Applications
Industry 4.0
License
Dual
Collaboration
Open
Focus
Predictive AI
RESEARCH FOCUS

Bridging Theory & Practice

Three Modes

Theoretical, simulated, and real-hardware modes. Move from ideas to quantum devices seamlessly.

Industry 4.0 Focus

Integrations for telemetry, observability, and asset management. Research meets production.

Hybrid Architecture

Combine classical neural networks with quantum-inspired memory operations for enhanced performance.

Dual License

Open for academic research, commercial licensing available for industrial applications.

Academic Partnerships

Collaborate with universities and research institutions. Publish findings, advance the field.

NeuralOS Integration

Deploy quantum-augmented models to edge devices. Run experiments on real hardware.

RESEARCH APPLICATIONS

From Lab to Industry

Quantum-augmented neural networks for academic research and industrial deployment

Academic Research

Explore quantum-inspired algorithms, publish papers, and advance the theoretical foundations of quantum machine learning. QMANN provides three experimentation modes (theoretical, simulated, real-hardware) for comprehensive research workflows. Open collaboration with universities and research institutions worldwide.

Theoretical Mode: Algorithm development with mathematical frameworks and proofs
Simulation Mode: Validate algorithms with quantum circuit simulators (Qiskit, Cirq)
Real-Hardware: Deploy to IBM Quantum, Google Quantum AI, and IonQ devices
Open Source: Free for academic use with full access to research codebase
QiskitCirqIBM QuantumOpen Source

Predictive Maintenance

Deploy quantum-augmented models for industrial predictive maintenance. QMANN enhances anomaly detection, failure prediction, and remaining useful life (RUL) estimation for manufacturing equipment, turbines, and robotics. Reduce downtime by 60% with quantum-enhanced pattern recognition.

Anomaly Detection: Quantum-enhanced pattern recognition for early failure detection
RUL Estimation: Predict remaining useful life with hybrid quantum-classical models
Telemetry Integration: Connect to SCADA, MES, and IoT sensor networks
Edge Deployment: Run on NeuralOS for real-time inference on factory floor
Industry 4.0SCADANeuralOSEdge AI

Optimization & Planning

Solve complex optimization problems with quantum-inspired algorithms. QMANN accelerates supply chain optimization, route planning, resource allocation, and scheduling for logistics, manufacturing, and autonomous systems. Achieve 10-100x speedup on combinatorial optimization tasks.

Supply Chain: Optimize inventory, warehousing, and distribution networks
Route Planning: Multi-vehicle routing for delivery fleets and autonomous drones
Resource Allocation: Optimize manufacturing schedules and workforce planning
Hybrid Solver: Combine quantum annealing with classical optimization
QAOAVQELogisticsScheduling

AI Decision Support

Enhance autonomous systems with quantum-augmented decision-making. QMANN improves reinforcement learning, multi-agent coordination, and strategic planning for robotics, autonomous vehicles, and smart manufacturing. Deploy on NeuralOS for real-time edge inference.

Reinforcement Learning: Quantum-enhanced Q-learning for robotics control
Multi-Agent Systems: Coordinate robot fleets with quantum game theory
Strategic Planning: Long-horizon planning for autonomous vehicles and drones
Real-Time Inference: Deploy to edge devices with NeuralOS for low-latency decisions
Q-LearningMulti-AgentRoboticsNeuralOS

Join the Research

Explore quantum-augmented neural networks. Open for academic collaboration and industrial partnerships.