Performance-First Architecture
10-20x faster quantum ML training
Async execution pipeline, smart caching and batching, native gate compilation for hardware
Q-Store Documentation
Seamlessly integrate quantum computing with classical ML workflows - 10-20x faster training without quantum physics expertise
What is Q-Store?
Section titled “What is Q-Store?”Q-Store is a revolutionary quantum-native database and machine learning framework that seamlessly integrates quantum computing capabilities with classical machine learning workflows. It enables developers and researchers to leverage quantum hardware (IonQ, IBM Quantum, etc.) and high-performance simulators for real-world ML applications without requiring deep quantum physics expertise.
The Problem We Solve
Section titled “The Problem We Solve”Traditional quantum ML frameworks face critical challenges:
- 🐌 Performance Bottlenecks: Sequential circuit submission causes 10-100x slower training
- 🔧 Complexity Barrier: Steep learning curve requiring quantum physics expertise
- 🔌 Hardware Lock-in: Tight coupling to specific quantum providers
- 💾 Data Management: No production-ready storage for quantum ML training
- 🎯 Classical Overhead: 100% CPU/GPU computation
Our Solution
Section titled “Our Solution”Quantum-First ML Layers
** better quantum computation**
Replace classical layers with quantum circuits, natural quantum nonlinearity, information-theoretic optimal pooling
Hardware-Agnostic Design
Support several quantum providers
High-performance simulators (qsim, Lightning), seamless simulator ↔ hardware switching, plugin architecture
Production Storage Architecture
Battle-tested Zarr + Parquet stack
Async writes (never block training), efficient checkpointing and metrics, Pinecone integration for meta-learning
Documentation Sections
Section titled “Documentation Sections”Getting Started
Installation, quick start guide, and version history
Core Components
State Manager, Circuit Builder, Entanglement Registry, and Tunneling Engine
Applications
Financial modeling, ML training, recommendations, and scientific computing
IonQ Integration
SDK integration and quantum hardware optimization
Production Guide
Error handling, monitoring, batch operations, and connection pooling
Advanced Topics
ML training performance optimization and best practices