Q-Store Quantum ML Integration

Q-Memory Integration with Q-Store

Q-Memory provides native integration with Q-Store, an async quantum machine learning framework, enabling ultra-fast parameter checkpointing and model persistence for hybrid quantum-classical neural networks.

Q-Store Overview

Q-Store is an async-first quantum ML framework that trains hybrid quantum-classical neural networks using real quantum hardware.

Key Features

• Async quantum execution: 10-20× parallel circuit submissions
• Quantum layers: QuantumFeatureExtractor, QuantumNonlinearity, QuantumPooling
• Hardware backends: IonQ, AWS Braket
• Classical integration: Seamless integration with PyTorch/TensorFlow

Parameter Storage Optimization

Quantum Parameter Characteristics

• Type: Rotation angles for quantum gates (RY, RZ, RX)
• Format: Float32 in range [-π, π]
• Quantity: 64-16,384 parameters per model
• Update frequency: Every epoch or batch
• Precision requirements: ~0.1 rad (NISQ-compatible)

Q-Memory Advantage

• Native dual-parameter encoding: Store RY, RZ pairs in single cell
• Fast writes: 100ns per cell vs 50-100ms SSD
• Non-volatile: Survives power cycles
• High endurance: 10⁹ cycles for frequent updates
• Low power: <50mW idle vs 5-10W SSD

Integration Architecture

Q-Store Training Loop (Async)
         ↓
┌────────────────────────────────────┐
│ Quantum Layer Forward Pass         │ Execute on IonQ hardware
│ 64-16,384 quantum parameters       │ 10-20× parallel circuits
└────────────┬───────────────────────┘
             ↓
┌────────────────────────────────────┐
│ Parameter Extraction               │ Extract trainable params
│ (θ_y, θ_z) rotation angle pairs    │ Float32 values
└────────────┬───────────────────────┘
             ↓
┌────────────────────────────────────┐
│ AsyncPhase2Wrapper                 │ Non-blocking write
│ Background thread pool (4 workers) │ Zero training overhead
└────────────┬───────────────────────┘
             ↓
┌────────────────────────────────────┐
│ Q-Memory Phase 2 Array             │
│ • Encode to (θ, φ) levels          │ 5-11 bit encoding
│ • Write to crossbar row            │ <1µs parallel write
│ • BCH ECC protection               │ Error correction
└────────────────────────────────────┘

Hybrid Storage Strategy

Q-Memory implements a two-tier approach for optimal performance:

Tier 1: Q-Memory (Quantum Parameters Only)

# Fast checkpoint every epoch
quantum_params = model.get_quantum_parameters()  # 64 params
await async_phase2.write_quantum_parameters_async(
    layer_id=epoch,
    parameters=quantum_params
)
# Latency: 3.2µs (Phase 1) or <1µs (Phase 2)
# Overhead: 0.0001% of training time

Tier 2: SSD Zarr (Full Model State)

# Comprehensive checkpoint every 10 epochs
if epoch % 10 == 0:
    await zarr_storage.save_checkpoint_async(
        epoch,
        model.state_dict()  # All layers + optimizer
    )
# Latency: 50-100ms
# Overhead: 0.16-0.33% (only when triggered)

Performance Comparison

Checkpoint Speed (64 quantum parameters)

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

Fast Restart Performance

Operation	SSD Only	Q-Memory Hybrid	Speedup
Load quantum params	5-10 sec	0.2 sec	25-50×
Resume training	Immediate	Immediate	Same
Total restart time	5-10 sec	0.2 sec	25-50×

Dual-Parameter Encoding

Q-Memory Phase 1+ supports native dual-parameter storage optimized for quantum gates:

Parameter Pair Encoding

# Quantum circuit with paired gates
circuit.ry(theta_y, qubit=0)  # RY gate
circuit.rz(theta_z, qubit=0)  # RZ gate

# Store both in single dual-parameter cell
# θ (resistance): theta_y → 5-bit base level
# φ (capacitance): theta_z → 6-bit base level

# Result: 2× parameter density vs Phase 0
# 64 parameters → 32 cells (Phase 1) vs 64 cells (Phase 0)

Precision Analysis

Representation	Bits	Precision	Quantum ML Impact
Q-Store (float32)	32	~10⁻⁷ rad	Baseline
Phase 0 (5-bit)	5	0.098 rad	Sufficient ✓
Phase 1 θ (5-bit)	5	0.098 rad	Sufficient ✓
Phase 1 φ (6-bit)	6	0.049 rad	Excellent ✓

Assessment: Q-Memory precision sufficient for NISQ quantum ML (typical gradient noise 0.1-0.5 rad >> quantization error)

Model Capacity Support

Model Architecture	Parameters	Q-Memory Phase	Array Utilization
Tiny (4q×3d)	24	Phase 0	12 cells (0.02%)
Small (6q×3d)	36	Phase 0	18 cells (0.03%)
Medium (8q×4d)	64	Phase 1	32 cells (0.05%)
Large (12q×4d)	96	Phase 1	48 cells (0.07%)
X-Large (16q×4d)	256	Phase 2	128 cells (0.2%)
XX-Large (20q×5d)	1,000	Phase 2	500 cells (0.8%)
Maximum	16,384	Phase 2	8,192 cells (12.5%)

Use Case Examples

VQE (Variational Quantum Eigensolver)

Application: Quantum chemistry, finding molecular ground states

Q-Memory Benefits:

• Fast parameter updates during energy minimization
• Non-volatile storage of converged parameters
• Low power for extended optimization runs
• Typical usage: 32-128 parameters (Phase 1 optimal)

QAOA (Quantum Approximate Optimization)

Application: Combinatorial optimization problems

Q-Memory Benefits:

• Rapid checkpoint during parameter sweeps
• Persistent storage of optimal solutions
• High endurance for many optimization iterations
• Typical usage: 64-256 parameters (Phase 1-2)

Quantum Neural Networks

Application: Quantum machine learning classifiers

Q-Memory Benefits:

• Near-zero overhead quantum layer checkpointing
• Fast model restarts for inference
• Seamless Q-Store integration
• Typical usage: 64-1,024 parameters (Phase 1-2)

Real-World Training Example

Scenario: Fashion MNIST classification with QuantumFeatureExtractor

Model Configuration:
  • Architecture: 8 qubits, depth=4
  • Parameters: 64 (32 RY + 32 RZ)
  • Training: 100 epochs, 30 batches/epoch
  • Hardware: IonQ quantum processor

Performance with Q-Memory Phase 2:
  • Total training time: 50 minutes
    - Quantum execution: 47.5 min (95%)
    - Classical ops: 2.4 min (4.8%)
    - Q-Memory checkpoints: 0.0032 sec (0.00006%)

  • Checkpoint overhead: NEGLIGIBLE ✓
  • Fast restart: 0.2 sec vs 8 sec SSD (40× faster) ✓
  • Power savings: 100× less idle power ✓

Error Correction Synergy

Q-Memory integrates with Q-Store’s quantum error mitigation:

Multi-Layer Protection

1. Quantum Layer (Q-Store): ZNE, PEC, MEM error mitigation
2. Encoding Layer: Float32 → dual-parameter conversion
3. Analog Layer (Q-Memory): BCH(15,11) hardware ECC
4. System Layer: 1S1R selectors for crosstalk suppression

Combined Error Budget:

• Quantum gate errors: 0.1-1% (dominant)
• Parameter quantization: 0.0004 rad (negligible)
• Analog storage errors: <0.001% (with ECC)
• Total system error: <0.2% (dominated by quantum, not storage)

Async Execution Benefits

Q-Memory’s async interface enables zero-blocking integration:

# Non-blocking parameter checkpoint
async def train_epoch_async(model, data, epoch):
    # Training loop (existing Q-Store code)
    for batch in data:
        predictions = await model.forward_async(batch)
        loss = loss_fn(predictions, targets)
        await model.backward_async(loss)
        optimizer.step()

    # Non-blocking checkpoint (runs in background)
    checkpoint_future = async_phase2.write_quantum_parameters_async(
        layer_id=epoch,
        parameters=model.get_quantum_parameters()
    )

    # Continue immediately, no waiting
    # Checkpoint completes in parallel with next epoch

Result: Training proceeds at full speed with automatic parameter persistence

Deployment Requirements

Software

• Q-Store v4.1.0+ (pip install qstore)
• Python 3.9+ with asyncio
• Quantum hardware access
• Q-Memory Python SDK

Hardware Options

• Phase 0: Simulation only (hardware in fabrication)
• Phase 1: 4×4 prototype array (validated)
• Phase 2: 256×256 production array (recommended)

Integration Steps

1. Install Q-Store and Q-Memory SDK
2. Initialize async Phase 2 wrapper
3. Configure Q-Store backend to use Q-Memory
4. Run training with automatic checkpointing
5. Monitor performance statistics

Benefits Summary

Performance

• 10,000-100,000× faster parameter checkpointing
• 25-50× faster model restart vs SSD
• 0.00003% training overhead (effectively zero)

Efficiency

• 100× lower idle power consumption
• 2× parameter density with dual encoding
• Native quantum parameter format (no conversion overhead)

Reliability

• Non-volatile persistence across power cycles
• 10⁹ cycle endurance for frequent updates
• Multi-layer error correction for data integrity