{"id":34954799,"url":"https://github.com/yucelz/q-store-examples","last_synced_at":"2026-05-15T16:02:17.203Z","repository":{"id":329037073,"uuid":"1117871104","full_name":"yucelz/q-store-examples","owner":"yucelz","description":"A database architecture that leverages quantum mechanical properties—superposition, entanglement, decoherence, and tunneling—as **core features** for exponential performance advantages in vector similarity search, relationship management, and pattern discovery.","archived":false,"fork":false,"pushed_at":"2025-12-18T17:51:06.000Z","size":143,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-12-20T14:09:35.709Z","etag":null,"topics":["q-store","quantum-computing","quatum-database"],"latest_commit_sha":null,"homepage":"https://www.q-store.tech","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/yucelz.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-12-16T23:59:34.000Z","updated_at":"2025-12-18T14:29:53.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/yucelz/q-store-examples","commit_stats":null,"previous_names":["yucelz/q-store-examples"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/yucelz/q-store-examples","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yucelz%2Fq-store-examples","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yucelz%2Fq-store-examples/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yucelz%2Fq-store-examples/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yucelz%2Fq-store-examples/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/yucelz","download_url":"https://codeload.github.com/yucelz/q-store-examples/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yucelz%2Fq-store-examples/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33071582,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-15T11:35:32.926Z","status":"ssl_error","status_checked_at":"2026-05-15T11:35:31.362Z","response_time":103,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["q-store","quantum-computing","quatum-database"],"created_at":"2025-12-26T21:58:52.110Z","updated_at":"2026-05-15T16:02:17.193Z","avatar_url":"https://github.com/yucelz.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Q-Store Examples\n\nStandalone example projects demonstrating Q-Store 3.4.3 quantum database capabilities for ML training, financial applications, and more.\n\n**Note**: This is a standalone examples repository. Q-Store 3.4.3 is installed via pip or from a local wheel file.\n\n📖 **For detailed setup instructions and troubleshooting, see [SETUP.md](SETUP.md)**\n\n## 🚀 Quick Start\n\n### Prerequisites\n\n- Python 3.8 or higher\n- pip or conda package manager\n- API keys (see [API Keys](#api-keys) section)\n\n### Installation\n\n#### Option 1: Editable Install (Recommended for Development)\n\n```bash\n# 1. Clone this repository\ngit clone https://github.com/yucelz/q-store-examples.git\ncd q-store-examples\n\n# 2. Install package in editable mode (changes are immediately reflected)\npip install -e .\n\n# Or with optional ML dependencies\npip install -e \".[ml,data,dev]\"\n\n# 3. Set up environment variables\ncp .env.example .env\n# Edit .env and add your API keys\n\n# 4. Verify installation\npython scripts/verify_installation.py\n```\n\n#### Option 2: Using requirements.txt\n\n```bash\n# 1. Clone this repository\ngit clone https://github.com/yucelz/q-store-examples.git\ncd q-store-examples\n\n# 2. Install Q-Store 3.4.3 and dependencies\npip install -r requirements.txt\n\n# 3. Install the package (required for imports to work)\npip install -e .\n\n# 4. Set up environment variables\ncp .env.example .env\n# Edit .env and add your API keys\n\n# 5. Verify installation\npython scripts/verify_installation.py\n```\n\n#### Option 3: Using conda\n\n```bash\n# 1. Clone this repository\ngit clone https://github.com/yucelz/q-store-examples.git\ncd q-store-examples\n\n# 2. Create conda environment\nconda create -n q-store-examples python=3.11\nconda activate q-store-examples\n\n# 3. Install the package\npip install -e .\n\n# 4. Set up environment variables\ncp .env.example .env\n# Edit .env and add your API keys\n\n# 5. Verify installation\npython scripts/verify_installation.py\n```\n\n#### Option 4: Using Local Wheel File\n\nIf you have the Q-Store wheel file:\n\n```bash\n# 1. Clone this repository\ngit clone https://github.com/yucelz/q-store-examples.git\ncd q-store-examples\n\n# 2. Copy the wheel file to this directory\ncp /path/to/q_store-3.4.3-cp313-cp313-manylinux_2_17_x86_64.whl .\n\n# 3. Install using make\nmake install-wheel\n\n# Or install manually:\n# pip install q_store-3.4.3-cp313-cp313-manylinux_2_17_x86_64.whl\n# pip install -r requirements.txt --no-deps\n\n# 4. Set up environment variables\ncp .env.example .env\n# Edit .env and add your API keys\n\n# 5. Verify installation\npython scripts/verify_installation.py\n```\n\n#### Option 4: Using Local Wheel File\n\nIf you have the Q-Store wheel file:\n\n```bash\n# 1. Clone this repository\ngit clone https://github.com/yucelz/q-store-examples.git\ncd q-store-examples\n\n# 2. Copy the wheel file to this directory\ncp /path/to/q_store-3.4.3-cp313-cp313-manylinux_2_17_x86_64.whl .\n\n# 3. Install using make\nmake install-wheel\n\n# Or install manually:\n# pip install q_store-3.4.3-cp313-cp313-manylinux_2_17_x86_64.whl\n# pip install -e .\n\n# 4. Set up environment variables\ncp .env.example .env\n# Edit .env and add your API keys\n\n# 5. Verify installation\npython scripts/verify_installation.py\n```\n\n#### Option 5: Minimal Installation (No ML Dependencies)\n\n```bash\n# Clone and navigate to repository\ngit clone https://github.com/yucelz/q-store-examples.git\ncd q-store-examples\n\n# Install only core dependencies (includes q-store==3.4.3)\npip install -r requirements-minimal.txt\n\n# This allows running:\n# - basic_example.py\n# - financial_example.py\n# - quantum_db_quickstart.py\n```\n\n## 🔑 API Keys\n\n### Required\n\n- **Pinecone API Key**: Required for all examples\n  - Get it from: https://www.pinecone.io/\n  - Free tier available (100K vectors)\n\n### Optional\n\n- **IonQ API Key**: Optional, enables quantum simulation features\n  - Get it from: https://ionq.com/\n  - Free credits available for new users\n\n- **Hugging Face Token**: Optional, only for gated models\n  - Get it from: https://huggingface.co/settings/tokens\n\n### Configuration\n\n```bash\n# Copy the example file\ncp .env.example .env\n\n# Edit with your favorite editor\nnano .env  # or vim, code, etc.\n\n# Add your keys:\nPINECONE_API_KEY=your_actual_key_here\nPINECONE_ENVIRONMENT=us-east-1\nIONQ_API_KEY=your_ionq_key_here  # Optional\n\n# Verify configuration\npython show_config.py\n```\n\nThe `show_config.py` script will display your current configuration and guide you on next steps.\n\n## 📚 Available Examples\n\n### 1. Basic Example (`basic_example.py`)\n\nDemonstrates core Q-Store functionality:\n- Inserting vectors with quantum contexts\n- Querying with superposition\n- Creating entangled groups\n- Quantum tunneling for exploration\n\n```bash\npython basic_example.py\n```\n\n### 2. Financial Example (`financial_example.py`)\n\nFinancial data analysis with quantum features:\n- Portfolio optimization\n- Risk correlation analysis\n- Market regime detection\n- Anomaly detection\n\n```bash\npython financial_example.py\n```\n\n### 3. Quantum Database Quickstart (`quantum_db_quickstart.py`)\n\nComprehensive tutorial covering:\n- Database initialization\n- All query modes (PRECISE, BALANCED, EXPLORATORY)\n- Advanced quantum features\n- Performance optimization\n\n```bash\npython quantum_db_quickstart.py\n```\n\n### 4. V3.2 ML Training Examples (`src/q_store_examples/examples_v3_2.py`)\n\nComplete quantum ML training demonstrations:\n- Basic quantum neural network training\n- Quantum data encoding strategies\n- Transfer learning with quantum models\n- Multiple backend comparison\n- Database-ML integration\n- Quantum autoencoder\n\n```bash\n# Run with mock backends (no API keys needed)\npython src/q_store_examples/examples_v3_2.py\n\n# Run with real Pinecone and IonQ backends\n# Option 1: Using .env file (recommended)\n# Make sure your .env file has PINECONE_API_KEY and IONQ_API_KEY set\npython src/q_store_examples/examples_v3_2.py --no-mock\n\n# Option 2: Using environment variables\nexport PINECONE_API_KEY=\"your-pinecone-key\"\nexport IONQ_API_KEY=\"your-ionq-key\"\nexport PINECONE_ENVIRONMENT=\"us-east-1\"\nexport IONQ_TARGET=\"simulator\"\n\npython src/q_store_examples/examples_v3_2.py --no-mock\n\n# Option 3: Using command-line arguments (overrides .env)\npython src/q_store_examples/examples_v3_2.py --no-mock \\\n  --pinecone-api-key YOUR_PINECONE_KEY \\\n  --pinecone-env us-east-1 \\\n  --ionq-api-key YOUR_IONQ_KEY \\\n  --ionq-target simulator\n\n# Available IonQ targets:\n# - simulator (free, default)\n# - ionq_simulator \n# - qpu.aria-1 (requires credits)\n# - qpu.forte-1 (requires credits)\n```\n\n**Priority Order:** Command-line args → Environment variables → .env file → Defaults\n\n### 5. V3.3 High-Performance ML Training Examples (`src/q_store_examples/examples_v3_3.py`)\n\n**NEW** - 24-48x faster training with algorithmic optimization:\n- SPSA gradient estimation (2 circuits instead of 96)\n- Hardware-efficient quantum layers (33% fewer parameters)\n- Adaptive gradient optimization\n- Circuit caching and batching\n- Performance tracking and comparison\n- Real-time speedup analysis\n\n```bash\n# With mock backends (default - for testing)\npython src/q_store_examples/examples_v3_3.py\n\n# With real IonQ/Pinecone backends\npython src/q_store_examples/examples_v3_3.py --no-mock\n\n# With specific credentials\npython src/q_store_examples/examples_v3_3.py --no-mock \\\n  --ionq-api-key YOUR_KEY \\\n  --pinecone-api-key YOUR_KEY\n\n# See all options\npython src/q_store_examples/examples_v3_3.py --help\n```\n\n**Performance Improvements:**\n- 🚀 **48x fewer circuits** with SPSA (2 vs 96 per batch)\n- ⚡ **33% fewer parameters** with hardware-efficient ansatz\n- 💾 **Circuit caching** eliminates redundant compilations\n- 🔄 **Batch execution** enables parallel quantum jobs\n- 📊 **Performance tracking** shows real-time speedup metrics\n\n**Priority Order:** Command-line args → Environment variables → .env file → Defaults\n\n### 6. V3.4 Performance-Optimized ML Training Examples (`src/q_store_examples/examples_v3_4.py`)\n\n**LATEST** - 8-10x faster than v3.3.1 through true parallelization:\n- **IonQBatchClient**: Single API call for all circuits (12x faster submission)\n- **IonQNativeGateCompiler**: GPi/GPi2/MS native gates (30% faster execution)\n- **SmartCircuitCache**: Template-based caching (10x faster preparation)\n- **CircuitBatchManagerV34**: Orchestrates all optimizations together\n- Production training workflow with full v3.4 features\n- Configuration guide and performance evolution analysis\n\n```bash\n# ============================================================================\n# BASIC USAGE\n# ============================================================================\n\n# 1. Mock mode (default - safe testing, no API calls needed)\npython src/q_store_examples/examples_v3_4.py\n\n# 2. Real IonQ/Pinecone backends (uses .env file)\npython src/q_store_examples/examples_v3_4.py --no-mock\n\n# ============================================================================\n# CONFIGURATION OPTIONS\n# ============================================================================\n\n# Option 1: Using .env file (RECOMMENDED)\n# Make sure your .env file has:\n#   PINECONE_API_KEY=your-pinecone-key\n#   IONQ_API_KEY=your-ionq-key\n#   PINECONE_ENVIRONMENT=us-east-1\n#   IONQ_TARGET=simulator\n\npython src/q_store_examples/examples_v3_4.py --no-mock\n\n# Option 2: Using environment variables\nexport PINECONE_API_KEY=\"your-pinecone-key\"\nexport IONQ_API_KEY=\"your-ionq-key\"\nexport PINECONE_ENVIRONMENT=\"us-east-1\"\nexport IONQ_TARGET=\"simulator\"\n\npython src/q_store_examples/examples_v3_4.py --no-mock\n\n# Option 3: Using command-line arguments (overrides .env and env vars)\npython src/q_store_examples/examples_v3_4.py --no-mock \\\n  --pinecone-api-key YOUR_PINECONE_KEY \\\n  --pinecone-env us-east-1 \\\n  --ionq-api-key YOUR_IONQ_KEY \\\n  --ionq-target simulator\n\n# ============================================================================\n# IONQ TARGET OPTIONS\n# ============================================================================\n\n# Simulator (free, default)\npython src/q_store_examples/examples_v3_4.py --no-mock --ionq-target simulator\n\n# IonQ Harmony QPU (requires credits)\npython src/q_store_examples/examples_v3_4.py --no-mock --ionq-target qpu.harmony\n\n# IonQ Aria QPU (requires credits)\npython src/q_store_examples/examples_v3_4.py --no-mock --ionq-target qpu.aria-1\n\n# ============================================================================\n# ADVANCED USAGE\n# ============================================================================\n\n# Show all available options\npython src/q_store_examples/examples_v3_4.py --help\n\n# Full example with all parameters\npython src/q_store_examples/examples_v3_4.py \\\n  --no-mock \\\n  --pinecone-api-key pk-xxxxx \\\n  --pinecone-env us-east-1 \\\n  --ionq-api-key xxxxxxxx \\\n  --ionq-target simulator\n```\n\n**What Each Example Demonstrates:**\n\n| Example | Focus | Key Feature |\n|---------|-------|-------------|\n| **Example 1** | IonQBatchClient | True batch submission (1 API call vs 20) |\n| **Example 2** | IonQNativeGateCompiler | Native gate compilation (GPi/GPi2/MS) |\n| **Example 3** | SmartCircuitCache | Template-based circuit caching |\n| **Example 4** | CircuitBatchManagerV34 | All optimizations integrated |\n| **Example 5** | Production Training | Complete training workflow with v3.4 |\n| **Example 6** | Configuration Guide | 4 config scenarios for different use cases |\n| **Example 7** | Performance Evolution | v3.2 → v3.3 → v3.3.1 → v3.4 comparison |\n\n**Performance Targets:**\n- 📊 **Batch time**: 35s (v3.3.1) → 4s (v3.4) = **8.75x faster**\n- ⚡ **Circuits/sec**: 0.57 (v3.3.1) → 5.0 (v3.4) = **8.8x throughput**\n- 🚀 **Training time**: 29.6 min (v3.3.1) → 3.75 min (v3.4) = **7.9x faster**\n\n**Key Innovations:**\n```\nBatch API:     20 circuits → 1 API call     = 12x faster submission\nNative Gates:  GPi/GPi2/MS gates            = 30% faster execution  \nSmart Cache:   Template reuse               = 10x faster preparation\n─────────────────────────────────────────────────────────────────────\nCombined:      All optimizations together   = 8-10x overall speedup\n```\n\n**Migration from v3.3.1:**\n```python\n# Just add one line to your existing config:\nconfig = TrainingConfig(\n    # ... all your existing v3.3.1 settings ...\n    enable_all_v34_features=True  # 🔥 Enable v3.4 optimizations\n)\n# That's it! Fully backward compatible.\n```\n\n**Priority Order:** Command-line args → Environment variables → .env file → Defaults\n\n### 7. ML Training Example (`ml_training_example.py`)\n\nMachine learning integration:\n- Model embedding storage\n- Training data selection\n- Curriculum learning\n- Hard negative mining\n\n```bash\npython ml_training_example.py\n```\n\n### 8. Connection Tests\n\nVerify Pinecone and IonQ connections:\n\n```bash\n# Option 1: Using .env file (recommended)\n# Ensure your .env has PINECONE_API_KEY and IONQ_API_KEY set\npython test_pinecone_ionq_connection.py\npython test_cirq_adapter_fix.py\n\n# Option 2: Set environment variables explicitly\nexport PINECONE_API_KEY=\"your-key\"\nexport IONQ_API_KEY=\"your-key\"\n\npython test_pinecone_ionq_connection.py\npython test_cirq_adapter_fix.py\n```\n\nThese tests will:\n- ✅ Initialize Pinecone client and create test indexes\n- ✅ Configure IonQ backend (simulator and QPU)\n- ✅ Execute quantum circuits on IonQ\n- ✅ Run small training session with real backends\n- ✅ Verify Pinecone index creation during training\n\n### 9. TinyLlama React Training (`tinyllama_react_training.py`)\n\nComplete LLM fine-tuning workflow:\n- React code dataset generation\n- Quantum-enhanced data sampling\n- LoRA fine-tuning\n- Curriculum learning\n\n```bash\n# Option 1: Automated workflow\n./run_react_training.sh\n\n# Option 2: Step-by-step\npython react_dataset_generator.py\npython tinyllama_react_training.py\n\n# See REACT_QUICK_REFERENCE.md for details\n```\n\n## 📖 Documentation\n\n| Document | Description |\n|----------|-------------|\n| **REACT_QUICK_REFERENCE.md** | Quick start for React training |\n| **REACT_TRAINING_WORKFLOW.md** | Detailed React training guide |\n| **TINYLLAMA_TRAINING_README.md** | TinyLlama fine-tuning guide |\n| **IMPROVEMENTS_SUMMARY.md** | Code improvements and comparisons |\n\n\n\n## 🔧 Configuration\n\n### Environment Variables\n\nAll configuration is done through `.env` file:\n\n```bash\n# Required\nPINECONE_API_KEY=your_key\nPINECONE_ENVIRONMENT=us-east-1\n\n# Optional\nIONQ_API_KEY=your_ionq_key\nIONQ_TARGET=simulator\n\n# ML Training (optional)\nHUGGING_FACE_TOKEN=your_token\nOUTPUT_DIR=./models\n```\n\n### Custom Settings\n\nEdit configuration in each example file:\n\n```python\n# Example: tinyllama_react_training.py\nconfig = TrainingConfig(\n    max_samples=1000,\n    num_train_epochs=3,\n    use_quantum_sampling=True,\n    use_curriculum_learning=True\n)\n```\n\n## 🧪 Testing\n\n### Verify Installation\n\n```bash\n# Test Q-Store installation\npython verify_installation.py\n\n# Check your configuration (.env file)\npython show_config.py\n\n# Test React integration\npython verify_react_integration.py\n\n# Test TinyLlama setup\npython verify_tinyllama_example.py\n\n# Verify v3.2 components\ncd ..\npython verify_v3_2.py\ncd examples\n```\n\n### Test Quantum Backends\n\n```bash\n# Quick Cirq adapter test\npython test_cirq_adapter_fix.py\n\n# Comprehensive Pinecone + IonQ test\npython test_pinecone_ionq_connection.py\n```\n\n### Run Unit Tests\n\n```bash\n# Install dev dependencies\npip install pytest pytest-cov black isort flake8 mypy\n\n# Run tests\npytest\n\n# With coverage\npytest --cov=. --cov-report=html\n```\n\n## 💡 Usage Tips\n\n### GPU Support\n\nFor CUDA GPU support:\n\n```bash\n# Install PyTorch with CUDA\npip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n\n# Verify GPU availability\npython -c \"import torch; print('GPU Available:', torch.cuda.is_available())\"\n```\n\n### Memory Management\n\nFor large datasets or limited RAM:\n\n```python\n# Reduce batch size\nconfig = TrainingConfig(\n    per_device_train_batch_size=1,  # Smaller batches\n    gradient_accumulation_steps=16,  # Accumulate gradients\n    max_samples=500                   # Limit dataset size\n)\n```\n\n### Development Mode\n\nFor development with the latest Q-Store:\n\n```bash\n# If you have Q-Store source code, install in editable mode\ncd /path/to/q-store\npip install -e .\n\n# Or use the latest from PyPI\npip install --upgrade q-store\n\n# Now run examples with the updated version\n```\n\n## 🐛 Troubleshooting\n\n### Common Issues\n\n| Issue | Solution |\n|-------|----------|\n| `ModuleNotFoundError: q_store` | Install Q-Store: `pip install q-store==3.4.3` |\n| `PINECONE_API_KEY not found` | Create `.env` file with your API key |\n| `ImportError: transformers` | Install ML dependencies: `pip install -r requirements.txt` |\n| `CUDA out of memory` | Reduce batch size or use CPU |\n| `Dataset file not found` | Run dataset generator first |\n| `'list' object has no attribute 'measurements'` | Fixed in latest version - Cirq adapter updated |\n| `Pinecone index not created` | Ensure API key is valid, check `--no-mock` flag |\n\n### Debug Mode\n\nEnable verbose logging:\n\n```bash\n# Set environment variable\nexport LOG_LEVEL=DEBUG\n\n# Or in .env file\necho \"LOG_LEVEL=DEBUG\" \u003e\u003e .env\n```\n\n### Getting Help\n\n1. Check the documentation files in this directory\n2. Review the parent [Q-Store README](../README.md)\n3. Open an issue on GitHub\n4. Review existing issues and discussions\n\n## 🎯 Next Steps\n\n1. **Run Basic Example**: Start with `basic_example.py`\n2. **Try React Training**: Use the automated workflow\n3. **Experiment**: Modify configs and try different strategies\n4. **Build Your Own**: Use examples as templates\n5. **Contribute**: Share improvements and new examples\n\n## 📊 Performance Benchmarks\n\n### Dataset Sizes\n\n- **Minimal**: 500-1,000 samples (fast, for testing)\n- **Medium**: 1,000-5,000 samples (balanced)\n- **Large**: 5,000-10,000+ samples (best results)\n\n### Training Times (approximate)\n\n- **Dataset Generation**: 10-30 seconds\n- **Database Loading**: 1-3 minutes\n- **Quantum Sampling Demo**: 30 seconds\n- **Full Training**: 30-90 minutes (with GPU)\n\n## 🤝 Contributing\n\nContributions are welcome! To add new examples:\n\n1. Follow the existing code structure\n2. Add documentation\n3. Include requirements\n4. Test thoroughly\n5. Submit a pull request\n\n## 📄 License\n\nMIT License - see parent repository for details\n\n## 🔗 Related Resources\n\n- [Q-Store Main Repository](https://github.com/yucelz/q-store)\n- [Quantum Database Design](../quantum_db_design_v2.md)\n- [Pinecone Documentation](https://docs.pinecone.io/)\n- [IonQ Documentation](https://ionq.com/docs/)\n- [Transformers Documentation](https://huggingface.co/docs/transformers/)\n\n## 📞 Support\n\n- GitHub Issues: [q-store/issues](https://github.com/yucelz/q-store/issues)\n- Documentation: [examples/](https://github.com/yucelz/q-store/tree/main/examples)\n\n---\n\n**Ready to start?** Check your configuration:\n\n```bash\npython show_config.py\n```\n\nIf all checks pass, you're ready to explore quantum-enhanced machine learning! 🚀\n\n**Quick Start:**\n```bash\n# V3.2 - Standard quantum ML training\n# With mock backends (safe, no API calls)\npython src/q_store_examples/examples_v3_2.py\n\n# With real Pinecone + IonQ (uses your .env configuration)\npython src/q_store_examples/examples_v3_2.py --no-mock\n\n# V3.3 - High-performance quantum ML training (24-48x faster!)\n# With mock backends (safe, no API calls)\npython src/q_store_examples/examples_v3_3.py\n\n# With real Pinecone + IonQ (uses your .env configuration)\npython src/q_store_examples/examples_v3_3.py --no-mock\n\n# V3.3.1 - Corrected batch gradient training (True SPSA parallelization)\n# With mock backends (safe, no API calls)\npython src/q_store_examples/examples_v3_3_1.py\n\n# With real Pinecone + IonQ (uses your .env configuration)\npython src/q_store_examples/examples_v3_3_1.py --no-mock\n\n# V3.4 - Performance optimized (8-10x faster than v3.3.1!) ⚡ RECOMMENDED\n# With mock backends (safe, no API calls)\npython src/q_store_examples/examples_v3_4.py\n\n# With real Pinecone + IonQ (uses your .env configuration)\npython src/q_store_examples/examples_v3_4.py --no-mock\n\n# With specific API keys (overrides .env)\npython src/q_store_examples/examples_v3_4.py --no-mock \\\n  --pinecone-api-key YOUR_PINECONE_KEY \\\n  --ionq-api-key YOUR_IONQ_KEY\n\n# ============================================================================\n# PERFORMANCE COMPARISON\n# ============================================================================\n# v3.2:   Parameter Shift (960 circuits/batch) - Baseline\n# v3.3:   SPSA (20 circuits/batch) - 48x fewer circuits\n# v3.3.1: Parallel SPSA (20 circuits/batch, parallel) - Correct implementation\n# v3.4:   Batch API + Native Gates + Caching - 8-10x faster than v3.3.1!\n#\n# Recommended: Start with v3.4 for best performance! 🚀\n# ============================================================================\n```\n**docs/sphinx**\n\n```bash\n# Install documentation dependencies\npip install sphinx sphinx-rtd-theme\ncd docs/sphinx\nsphinx-build -b html . _build/html\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyucelz%2Fq-store-examples","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyucelz%2Fq-store-examples","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyucelz%2Fq-store-examples/lists"}