AI→QC Implementation Progress

AI→QC Implementation Progress

Overview

This document tracks the implementation of AI enhancements for quantum computing, based on the research in docs/research/ai_quantum_symbiosis/.

Goal: Close the virtuous cycle where AI improves quantum hardware utilization, which in turn improves the Quantum RAG system.

Task Status

File Structure

ai_extensions/
├── __init__.py                    # Package init (exports main classes)
├── AI_TO_QC_PROGRESS.md           # This file
├── error_predictor.py             # Task 10: LSTM error prediction [DONE]
├── drift_compensation.py          # Task 11: Kalman parameter tracking [DONE]
├── adaptive_selector.py           # Task 12: System selection [DONE]
├── data_collection.py             # Training data utilities [DONE]
├── qi_integration.py              # Quantum Inspire integration [DONE]
├── collected_data/                # Runtime data from hardware runs
└── models/                        # Saved model weights
    ├── error_predictor.pt
    ├── drift_tracker.pt
    └── system_selector.pt

tests/
└── test_ai_extensions.py          # 50 tests, all passing

Task 10: Error Predictor

Research Reference

• docs/research/ai_quantum_symbiosis/02_ai_error_correction.md

Concept

Train an LSTM on historical syndrome patterns to predict where errors will occur, enabling preemptive mitigation (dynamical decoupling, adjusted gates).

Architecture

Input: syndrome_history (T × num_syndrome_bits)
       + metadata (temperature, calibration_age, etc.)
           ↓
┌─────────────────────────────┐
│  LSTM (2 layers, 64 hidden) │
└─────────────────────────────┘
           ↓
┌─────────────────────────────┐
│  Dense → Sigmoid            │
└─────────────────────────────┘
           ↓
Output: P(error at each qubit) for next cycle

Implementation Status

• [x] Create error_predictor.py with ErrorPredictor class
• [x] Create data_collection.py for training data
• [x] Create qi_integration.py for Quantum Inspire integration
• [x] Create test suite tests/test_ai_extensions.py (20/20 passing)
• [ ] Collect training data (requires hardware runs)
• [ ] Train model on real hardware data
• [ ] Measure fidelity improvement

Key Classes

• ErrorPredictor: LSTM model for syndrome prediction
• ErrorAwareRunner: Wrapper that adds protection based on predictions
• SyndromeCollector: Collects training data from hardware runs

Task 11: Drift Compensation

Research Reference

• docs/research/ai_quantum_symbiosis/03_ai_quantum_control.md

Concept

Use Kalman filter to track parameter drift over time, compensating circuits before execution.

Implementation Status

• [x] Create drift_compensation.py with KalmanParameterTracker
• [x] Create DriftCompensator high-level API
• [x] Create DriftAwareRunner wrapper
• [x] Add 13 tests for drift compensation
• [ ] Integrate with quantum_knowledge_graph.py
• [ ] Test on quantum walks
• [ ] Measure stability improvement

Task 12: Adaptive Selector

Research Reference

• docs/research/ai_quantum_symbiosis/07_integration_existing_work.md

Concept

Train a classifier to select optimal search method (Classical/Grover/HAM/Hybrid) based on query and corpus features.

Implementation Status

• [x] Create adaptive_selector.py with SystemSelector
• [x] Create FeatureExtractor for query/corpus features
• [x] Create RuleBasedSelector with domain knowledge
• [x] Create NeuralSelector (PyTorch, with fallback)
• [x] Create AdaptiveMethodSelector convenience wrapper
• [x] Add 17 tests for adaptive selection
• [ ] Train on benchmark data from real runs
• [ ] Integrate with EnhancedQuantumRAG.search()
• [ ] Measure best-of-all performance

How to Contribute (For Other Agents)

Picking Up Work

1. Check this file for current status
2. Check docs/reference/STATUS.json for task priorities
3. Read the research reference for context

Recording Progress

1. Update this file with implementation status
2. Update docs/reference/STATUS.json task status
3. Add changelog entry to docs/reference/PROJECT_TRACKER.md

Testing Changes

cd Documents/Learning/quantum_rag_demo

# Run unit tests
./quantum_env/bin/python -m pytest tests/ -v

# Test AI extensions specifically
./quantum_env/bin/python -c "from ai_extensions import error_predictor; print('OK')"

Dependencies

AI extensions require:

• torch (for neural networks)
• numpy (for Kalman filter)
• filterpy (optional, for advanced Kalman)

Install with:

./quantum_env/bin/pip install torch numpy

Changelog

Last updated: 2026-01-13