Memory Systems Overview

Q-Memory System Architecture

Q-Memory is a hybrid memory system optimized for quantum ML parameter storage, combining different technologies.

Competitive Positioning

System	Storage/Cell	Technology	Endurance	Q-Store Use Case
SSD (Zarr)	32 bits/param	NAND	10⁵ writes	Default backend
DRAM	1 bit	Volatile	Unlimited	Not persistent
Phase 0	5 bits	Q-memory Base	10⁹	Small models
Phase 1	10.4 bits	Q-memory tech	5×10⁷	Medium models
Phase 2	10.4 bits	Q-memory tech	10⁹	Large models

Key Advantages

For Q-Store Integration

• 10,000× faster checkpointing vs SSD storage
• Dual-parameter encoding perfectly matches quantum gate pairs (RY, RZ)
• Near-zero training overhead (0.00003-0.0002%)
• Async execution with non-blocking I/O
• 100× lower idle power (<50mW vs 5W SSD)

System Benefits

• Non-volatile parameter persistence across power cycles
• High endurance for frequent parameter updates
• Native dual-parameter storage for quantum circuits
• Hardware acceleration with FPGA parallel writes
• Error correction with BCH ECC + quantum mitigation synergy

Q-Store Integration Architecture

Q-Store Training Loop
         ↓
┌────────────────────────────────┐
│ AsyncPhase2Wrapper             │ Background workers
│ (Non-blocking interface)       │ 4-thread pool
└────────────┬───────────────────┘
             ↓
┌────────────────────────────────┐
│ Phase 2 Array (256×256)        │
│  • 1S1R Crossbar               │ Low crosstalk
│  • FPGA Controller             │ Parallel writes
│  • Professional ADC/DAC        │ High precision
│  • BCH ECC Engine              │ Error protection
└────────────┬───────────────────┘
             ↓
┌────────────────────────────────┐
│ Dual-Parameter Cells           │
│  θ: Resistance (Q-Memory Base) │ RY gate angles
│  φ: Capacitance (Q-Memory Base)│ RZ gate angles
└────────────────────────────────┘

Capacity Analysis

Model Support

Model Size	Parameters	Cells Needed	Phase Support
Tiny (4q×3d)	24	12	Phase 0 ✓
Small (6q×3d)	36	18	Phase 0 ✓
Medium (8q×4d)	64	32	Phase 1 ✓
Large (12q×4d)	96	48	Phase 1 ✓
X-Large (16q×4d)	256	128	Phase 2 ✓
XX-Large (20q×5d)	1,000	500	Phase 2 ✓

Hybrid Storage Strategy

Q-Memory implements a two-tier storage architecture:

Tier 1: Q-Memory (Fast, Frequent)

• Purpose: Quantum layer parameters only
• Frequency: Every epoch or batch
• Latency: <1µs to 3.2µs
• Capacity: 16,384 parameter pairs
• Benefits: 10,000× faster than SSD

Tier 2: SSD (Comprehensive, Infrequent)

• Purpose: Full model state (all layers + metadata)
• Frequency: Every 10 epochs
• Latency: 50-100ms
• Capacity: Unlimited
• Benefits: Complete checkpoint preservation

Performance Metrics

Checkpoint Performance (64 quantum parameters)

Backend	Write Latency	Training Overhead	Speedup vs SSD
SSD (Zarr)	50-100 ms	0.16-0.33%	Baseline
Phase 0	6.4 µs	0.0002%	7,800-15,600×
Phase 1	3.2 µs	0.0001%	15,600-31,000×
Phase 2	<1 µs	0.00003%	50,000-100,000×

Error Correction Stack

Q-Memory implements multi-layer error protection:

1. Quantum Layer: Q-Store error mitigation (ZNE, PEC, MEM)
2. Encoding Layer: Float32 to dual-parameter conversion
3. Analog Layer: BCH(15,11) hardware ECC
4. System Layer: Crosstalk suppression (<0.5% with 1S1R)

Combined residual error: <0.2% (dominated by quantum noise, not storage)

Target Applications

1. Quantum ML Training: Fast parameter checkpointing during training
2. Quantum Circuit Optimization: Parameter storage for VQE/QAOA
3. Hybrid Quantum-Classical: State persistence across quantum-classical loops
4. Model Deployment: Persistent quantum parameter storage