C2QIPFS Data Meets Quantum Advantage
Bridging classical computing with quantum technology for unprecedented security, efficiency, and AI integration
6 Qubits
Equal1 Bell1 Quantum Processing
PoQW
Proof of Quantum Work Consensus
C2Q API
Classic-to-Quantum Bridge
Quantum-Classical Hybrid Architecture
A revolutionary approach combining the strengths of classical and quantum computing
Hybrid Blockchain Structure
C2Q features a dual-layer architecture with a classical blockchain layer for transaction processing and a quantum layer powered by DWave's Proof of Quantum Work (PoQW) consensus mechanism.
Equal1 Bell1 Quantum Computing
Leveraging 6-qubit Equal1 Bell1 quantum computers for mining operations, bringing quantum computing out of specialized laboratories and into data centers.
C2Q API Bridge
A RESTful API that enables seamless communication between classical systems and the quantum blockchain, allowing for transaction submission, state queries, and resource contribution.
AI Resource Contribution Layer
Users can contribute both quantum resources from Bell1 and classical computing power towards AI operations, creating a distributed computing platform for artificial intelligence.
Key Features & Benefits
C2Q combines quantum security with classical efficiency to deliver unprecedented blockchain capabilities
Implements NIST-standardized post-quantum cryptography algorithms like ML-KEM for key encapsulation and CRYSTALS-Dilithium for digital signatures, ensuring security against future quantum attacks.
DWave's PoQW consensus mechanism significantly reduces energy consumption compared to traditional Proof-of-Work systems, making C2Q an environmentally responsible blockchain solution.
Seamlessly integrates with Ethereum, Bitcoin, and other blockchain networks, enabling cross-platform transactions and resource sharing.
Advanced Data Storage Architecture
A hybrid storage approach designed for the unique demands of quantum-classical computing workflows
Our storage architecture addresses the complex data management requirements of hybrid quantum-classical workflows, handling everything from large AI datasets to quantum computation results with enterprise-grade security and performance.
Key Data Types Supported
- • Large AI datasets and model parameters (multi-GB)
- • Quantum computation results and state vectors
- • Transaction metadata and blockchain records
- • Encrypted input/output data with PQC protection
- • Complex JSON configurations and circuit descriptions
Primary database for metadata, relational data, and structured information with ACID compliance.
Distributed storage solutions for large objects with cryptographic integrity verification.
Immutable integrity verification through cryptographic hashes stored on-chain.
| Solution | Use Case | Advantages | Considerations |
|---|---|---|---|
| Supabase/PostgreSQL | Metadata, relations, small objects | ACID compliance, complex queries, real-time | 1GB limit for large objects |
| Arweave | Permanent archival, critical data | Permanent storage, decentralized | Higher cost, immutable |
| Storj DCS | Large files, cost-sensitive data | Privacy-focused, cost-efficient | Performance variability |
| AWS S3 | High-performance, enterprise | Mature, predictable performance | Centralized, egress costs |
Comprehensive PQC integration using NIST-standardized algorithms for all data encryption and integrity verification.
Optimized data access patterns and indexing strategies for complex quantum and AI data structures.
Multi-layered integrity verification ensuring data authenticity across all storage components.
Industry Applications
C2Q's hybrid quantum-classical architecture enables transformative solutions across multiple sectors
C2Q transforms supply chain operations by translating NFT-based digital twin data into quantum states, enabling multi-dimensional optimization and simulation capabilities that are impossible with classical computing alone.
Expert Perspective
"By processing digital twin data through quantum algorithms, we've achieved a 32% reduction in overall logistics costs while simultaneously improving supply chain resilience. The ability to analyze thousands of variables simultaneously is a game-changer for complex global supply networks."
— Dr. Alexandra Chen, Chief Supply Chain Officer
Multi-dimensional Optimization
Quantum algorithms process digital twin data to solve complex optimization problems across inventory, transportation, and production simultaneously.
Predictive Maintenance
Quantum machine learning applied to sensor data predicts equipment failures with 78% greater accuracy than classical methods.
Real-time Supply Chain Simulation
Quantum processing enables instant "what-if" scenario testing and network-wide optimization in real-time.
Implementation Framework
Three-step process: Asset Tokenization → Quantum Translation → Insight Generation