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Q-Memory Docs

Applications Overview

Q-Memory Applications

Section titled “Q-Memory Applications”

Q-Memory memory technology enables breakthrough performance in quantum computing and machine learning workloads through ultra-high density storage and in-memory analog computation.

Quantum Computing Applications

Section titled “Quantum Computing Applications”

Q-Memory provides unique capabilities for quantum computing systems:

Key Applications

Section titled “Key Applications”
  • Quantum State Storage: Store classical representations of quantum states with 3000× better density than DRAM
  • QRAM Implementation: Quantum Random Access Memory with >10⁶ addressable states
  • Hybrid Algorithms: Accelerate VQE, QAOA, and other variational algorithms by 100-500×
  • Parameter Optimization: In-memory analog computation eliminates classical bottleneck

Performance Highlights

Section titled “Performance Highlights”
  • VQE: 100-200× faster iterations, 500-2000× less energy
  • QAOA: 10× faster than quantum+DRAM approach
  • QSVM: 10× training speedup, 100× energy reduction

Learn more about Quantum Computing →

Machine Learning Applications

Section titled “Machine Learning Applications”

Q-Memory revolutionizes ML training and inference through extreme density and analog computation:

Key Applications

Section titled “Key Applications”
  • Neural Network Weight Storage: 52× compression for model parameters
  • In-Memory Matrix Multiplication: 10-100× faster than GPU
  • Training Acceleration: 50-450× speedup for ResNet, BERT, GPT models
  • Edge AI Deployment: Ultra-low power inference

Performance Highlights

Section titled “Performance Highlights”
  • ResNet-50 Training: 50× faster, 100× less energy
  • BERT Fine-tuning: 20× faster than other GPUs
  • LLaMA-70B: Train in 2.6 days vs. 21 days on GPU cluster
  • DQN (RL): 25× throughput, 280× energy reduction

Learn more about ML Training →

System Architectures

Section titled “System Architectures”

Hybrid Quantum-Classical System

Section titled “Hybrid Quantum-Classical System”
┌─────────────────────────┐
│   Quantum Processor     │
│   (50-100 qubits)       │
└──────────┬──────────────┘
           ↓
┌─────────────────────────┐
│ Quantum-Classical I/F   │
└──────────┬──────────────┘
           ↓
┌─────────────────────────┐
│   Q-Memory Memory System    │
│  • State buffers        │
│  • Parameter storage    │
│  • Analog compute       │
└──────────┬──────────────┘
           ↓
┌─────────────────────────┐
│  Classical Processor    │
└─────────────────────────┘

ML Training Accelerator

Section titled “ML Training Accelerator”
┌─────────────────────────┐
│     Host CPU/GPU        │
│  Dataset | Optimizer    │
└──────────┬──────────────┘
           ↓
┌─────────────────────────┐
│  Q-Memory Accelerator   │
│  • Weight storage       │
│  • Analog MVM           │
│  • Gradient compute     │
│  • In-place updates     │
└─────────────────────────┘

Core Advantages

Section titled “Core Advantages”

Ultra-High Density

Section titled “Ultra-High Density”
  • 13 bits per cell (8,192 analog levels)
  • 52× compression for neural network weights
  • 3000× density vs. DRAM for quantum states

In-Memory Computation

Section titled “In-Memory Computation”
  • 10 ns matrix-vector multiply (constant time)
  • Zero data movement energy cost
  • Analog physics performs computation

Energy Efficiency

Section titled “Energy Efficiency”
  • 0.4 pJ per bit storage
  • 50 nJ per 1K×1K MVM operation
  • 10-500× reduction vs. GPU/DRAM

Speed

Section titled “Speed”
  • 50 ns read, 200 ns write
  • 10× faster than sequential digital compute
  • Parallel execution across entire array

Market Applications

Section titled “Market Applications”

AI/ML Training

Section titled “AI/ML Training”
  • Data center training clusters
  • Large language model development
  • Computer vision pipelines
  • Reinforcement learning

AI/ML Inference

Section titled “AI/ML Inference”
  • Edge devices (autonomous vehicles)
  • Mobile AI applications
  • Real-time video processing
  • IoT endpoints

Quantum Computing

Section titled “Quantum Computing”
  • Quantum state preparation
  • Variational algorithms
  • Quantum chemistry simulations
  • Quantum machine learning

Scientific Computing

Section titled “Scientific Computing”
  • Drug discovery
  • Materials simulation
  • Weather modeling
  • Genomics

Getting Started

Section titled “Getting Started”
  1. Understand the Architecture: Learn how Q-Memory stores 13 bits per cell
  2. Review Benchmarks: See performance data for your workload
  3. Explore Use Cases: Find applications similar to your needs
  4. Plan Integration: Design system architecture with Q-Memory
Section titled “Related Documentation”