Dual-Parameter Storage
Up to 10.4 bits per cell - Independent θ (resistance) and φ (capacitance) parameters
Perfect for storing paired quantum gate angles (RY, RZ)
Q-Memory Documentation
Revolutionary dual-parameter hardware memory design optimized for quantum machine learning parameter storage
What is Q-Memory?
Section titled “What is Q-Memory?”Q-Memory is an advanced hybrid memory system that combines different technologies to create dual-parameter storage optimized for quantum machine learning applications. The system stores quantum circuit parameters through independent control of two physical parameters.
Key Features
Section titled “Key Features”Ultra-Fast Checkpointing
Write: 100ns per cell, Read: 50ns
10,000× faster quantum parameter persistence vs SSD storage
Non-Volatile
10 years data retention
Persistent quantum parameter storage without power
High Endurance
Up to 10⁹ write cycles
Handles frequent parameter updates during quantum training
Technology Comparison
Section titled “Technology Comparison”| Memory Type | Bits/Cell | Technology | Endurance | Speed | Q-Store Compatible |
|---|---|---|---|---|---|
| NAND Flash | 3-4 bits | Floating gate | 3K-10K | Slow (ms) | Limited |
| SSD (Zarr) | 32/param | NAND | 100K writes | Medium (50ms) | Default |
| Q-Memory Phase 0 | 5 bits | ReRAM | 1B cycles | Fast (100ns) | Yes |
| Q-Memory Phase 1 (Hybrid) | 10.4 bits | Q-Memory tech | 50M cycles | Fast (100ns) | Excellent |
| Q-Memory Phase 2 (Production) | 10.4 bits | Q-Memory tech | 1B cycles | Ultra-fast (sub-µs) | Optimal |
Key Performance Highlights
Section titled “Key Performance Highlights”- 10,000× faster quantum parameter checkpointing vs SSD storage
- 2× parameter density with dual-parameter encoding (Phase 1+)
- 100× lower idle power consumption (under 50mW vs 5W SSD)
- Near-zero training overhead (0.0001%) for quantum ML workloads
Intellectual Property
Section titled “Intellectual Property”Q-Memory technology comprises 12 distinct patentable inventions across Phase 0, Phase 1, and Phase 2, representing significant innovations in quantum-classical hybrid computing, analog memory systems, and quantum machine learning infrastructure.
Documentation Sections
Section titled “Documentation Sections”Architecture
Three-phase development roadmap and system architecture
Memory Systems
Crossbar arrays, FPGA controllers, and dual-parameter storage
Applications
Quantum ML integration and parameter storage strategies
Benchmarks
Performance comparisons, checkpoint speeds, and benchmarks
Guides
Quick start, integration and implementation examples