Quantum Machine Learning AI Agent: Democratizing Quantum Computing for Real-World Applications
An IBM Global Mentorship Program 2025 Project: Automating Quantum Code Generation Without Prior Expertise
Why Quantum Machine Learning Needs an AI Assistant
Quantum Machine Learning (QML) combines quantum computing’s processing power with machine learning’s predictive capabilities. Yet three significant barriers prevent wider adoption:
-
Specialized knowledge requirements (Qiskit framework, quantum circuit design) -
High experimental iteration costs (manual parameter tuning) -
Complex implementation pipelines (data preprocessing → quantum encoding → result evaluation)
This IBM Global Mentorship Program 2025 project addresses these challenges through an autonomous QML AI agent that:
✔️ Generates optimized Qiskit code on demand
✔️ Self-corrects runtime errors
✔️ Achieves user-defined accuracy benchmarks
Core Functionality: How Your Quantum Programming Assistant Operates
🖥️ Multi-Access Interface Options
| Interaction Mode | Best For | Launch Method |
|---|---|---|
| Jupyter Notebook | Traditional Python developers | Run Agent_Graph.ipynb |
| LangGraph Studio | Visual workflow debugging | Execute langgraph dev in terminal |
📊 Flexible Data Ingestion Pipeline
# Supported dataset inputs:
1. sklearn.default("iris", "wine", "diabetes")
2. UCI/OpenML(repository_id=123)
3. Google Drive/Sheets(shareable_link)
4. Local CSV(automatic temp storage)
🔄 Dual-Optimization Engine Architecture
graph TD
A[Code Execution] -->|Error Detected| B[Error Correction Loop]
B -->|Max 4 attempts| C[Code Regeneration]
C --> A
D[Accuracy Evaluation] -->|Below Benchmark| E[Parameter Optimization Loop]
E -->|Max 3 cycles| F[Parameter Adjuster]
F --> C
- •
Self-Healing Loop: Auto-fixes code errors through iterative regeneration - •
Performance Loop: Dynamically tunes quantum parameters to exceed accuracy targets
⚙️ Six-Node Processing Workflow
-
Input Parser: Detects dataset type (classification/regression) and target variable -
Param Optimizer: Recommends quantum circuit parameters (algorithm, qubits, feature map) -
Code Generator: Produces executable Qiskit pipeline (preprocessing → modeling → evaluation) -
Code Executor: Runs code via Python REPL and captures outputs/errors -
Benchmark Evaluator: Compares results against user-defined accuracy threshold -
Output Handler: Returns optimized code + performance report
Step-by-Step Implementation Guide
Environment Configuration (Windows Example)
# 1. Clone repository
git clone https://github.com/Bluestone-456/IBM-GRM-Project.git
cd IBM-GRM-Project
# 2. Create virtual environment
python -m venv qml-agent-env
qml-agent-env\Scripts\Activate.ps1
# 3. Install dependencies
pip install -r requirements.txt
# 4. Configure API keys (.env file)
GOOGLE_API_KEY=<your_gemini_key>
LANGSMITH_API_KEY=<your_langsmith_key>
Case Study: Iris Flower Classification
Input Specifications:
{
"dataset": "iris",
"benchmark_score": 0.95
}
Execution Log:
>>> Phase 1: Data Parsing
Identified sklearn dataset - Target column: 'target'
Problem type: Multiclass classification (3 species)
>>> Phase 2: Parameter Initialization
Recommended quantum configuration:
- Algorithm: QSVC
- Feature Map: ZZFeatureMap
- Quantum Kernel: FidelityQuantumKernel
- Qubits: 4
>>> Phase 3: Code Generation (Attempt 1)
Error: StatevectorSampler.run() takes 2 positional arguments but 5 were given
✅ Self-repair: Adjusted quantum kernel initialization
>>> Phase 4: Code Execution (Attempt 2)
Test accuracy: 96.67%
🔥 Benchmark exceeded (Target: 95%)
Output Code Snippet:
# Quantum classification core
feature_map = ZZFeatureMap(feature_dimension=4, reps=1)
sampler = StatevectorSampler()
fidelity_kernel = FidelityQuantumKernel(feature_map=feature_map)
qsvc = QSVC(quantum_kernel=fidelity_kernel, tol=1e-3)
qsvc.fit(X_train, y_train) # Train quantum model
Technical Architecture Deep Dive
Repository Structure
.
├── QML_AI_Agent/
│ ├── Graph.py # Workflow orchestration
│ └── utils/
│ ├── dataset_functions.py # Data loader
│ ├── Prompts.py # LLM instruction templates
│ └── State.py # Workflow state management
├── langgraph.json # Visual workflow configuration
└── Agent_Graph.ipynb # Interactive notebook
Technology Stack
| Component | Version | Role |
|---|---|---|
| Python | ≥3.11 | Execution environment |
| Qiskit | 1.4.3 | Quantum circuit construction |
| LangGraph | Latest | Stateful workflow management |
| Gemini | 2.0-flash | Code generation & error analysis |
Performance Evaluation: Real-World Testing
Wine Classification Task
Input Parameters:
- •
Dataset: sklearn wine dataset - •
Target Accuracy: 0.85
Optimization Trajectory:
| Attempt | Algorithm | Feature Map | Accuracy | Status |
|---|---|---|---|---|
| 1 | QSVC | PauliFeatureMap | 79.3% | Below target |
| 2 | QSVC | ZFeatureMap | 83.1% | Approaching target |
| 3 | VQC | ZZFeatureMap | 87.5% | ✅ Target exceeded |
Key Parameter Adjustments:
- Optimizer: COBYLA → SPSA
+ Qubits: 6 → 8
- Circuit depth: 2 → 3
Frequently Asked Questions
What quantum knowledge is required to use this tool?
None. The system generates complete Qiskit code – users only provide datasets and accuracy targets.
What dataset sizes are supported?
Optimal performance with <300 samples and <20 features. Diabetes dataset (442 samples) runs in ~35 minutes.
Why the 8-qubit limit?
Quantum simulators on consumer hardware become memory-intensive beyond 8 qubits. Future versions will support cloud quantum hardware.
How to import proprietary data?
-
Export to CSV -
Upload to Google Drive -
Share public link
# Agent handles ingestion automatically
data = pd.read_csv("drive_link.csv")
Can I customize preprocessing?
Specify requirements in the problem description field:
"Use first 100 samples only"
"Drop 'timestamp' column"
"Discretize age into 3 bins"
Current Limitations & Development Roadmap
Operational Boundaries
-
Dataset scale: Performance degrades beyond 300 samples -
Error recovery: Complex errors may exhaust 4 self-repair attempts -
Algorithm support: Currently optimized for supervised learning
Version Development Plan
2025 Q3: Real hardware execution (IBM Quantum/AWS Braket)
2025 Q4: Unsupervised learning integration (quantum clustering)
2026 Q1: Batch processing mode (multiple dataset queuing)
Conclusion: Accelerating Quantum Accessibility
This IBM Global Mentorship Program initiative delivers three transformative benefits:
- •
Education: Enables quantum exploration without quantum physics prerequisites - •
Research: Generates baseline QML code for experimental comparison - •
Industry: Validates quantum advantage potential for specific datasets
“
“We’re not building a universal solution – we’re creating quantum ‘ignition systems.’ When you see a quantum model outperform classical algorithms in 15 minutes, that’s when true discovery begins.”
Project Repository:
https://github.com/Bluestone-456/IBM-GRM-Project
