quantum-debugger 0.6.1

Comprehensive quantum machine learning library with AutoML, GPU acceleration, advanced algorithms (QGANs, Quantum RL), transfer learning, and multi-framework support (Qiskit, PennyLane, Cirq, TensorFlow, PyTorch)

Quantum Debugger

The Most Comprehensive Quantum Machine Learning Library with AutoML

A powerful Python library for quantum circuit debugging, state inspection, performance analysis, and quantum machine learning. From basic circuits to QML with one-line AutoML.

What's New in v0.6.1

Correctness, performance, and "make the advertised features real" release:

• Faster core - gate application is now O(2ⁿ) per gate (was O(4ⁿ)); optional GPU state-vector simulation (get_statevector(use_gpu=True), up to ~50-75x at 20+ qubits in single precision).
• Genuinely quantum QML - the quantum kernel/QSVM, hybrid PyTorch/TF layers, Quantum GAN, Quantum RL, and error mitigation (PEC, CDR, QNG, ZNE) are now real circuit-based implementations with real gradients, each verified — not the classical/placeholder stand-ins they were before.
• Robust imports - a broken optional dependency no longer breaks import quantum_debugger.

See CHANGELOG.md for the full list.

What's New in v0.6.0

ONE-LINE QUANTUM MACHINE LEARNING

# NEW: AutoML - Quantum ML for everyone
from quantum_debugger.qml.automl import auto_qnn

model = auto_qnn(X_train, y_train)
predictions = model.predict(X_test)

No quantum expertise required. AutoML automatically:

• Selects optimal number of qubits
• Chooses best ansatz architecture
• Tunes all hyperparameters
• Finds best model configuration

v0.6.0 Complete Feature Set

Advanced QML (Weeks 1-3)

• Hybrid Models - TensorFlow and PyTorch quantum layers
• Quantum Kernels - QSVM with multiple kernel types
• Transfer Learning - PretrainedQNN, model zoo, fine-tuning

Production Tools (Weeks 4-5)

• Error Mitigation - PEC, CDR, realistic noise models
• Circuit Optimization - Gate reduction, compilation, transpilation

Universal Compatibility (Week 6)

• Framework Integrations - Qiskit, PennyLane, Cirq bridges

Hardware and Performance (Weeks 7-8)

• Real Quantum Computers - IBM Quantum (FREE), AWS Braket
• Benchmarking - QML vs Classical performance analysis

AutoML (Week 9)

• auto_qnn() - One-line interface for quantum ML
• Automatic Ansatz Selection - Finds best circuit architecture
• Hyperparameter Tuning - Grid and random search
• Neural Architecture Search - Optimizes qubit and layer counts

Jupyter Notebooks (Week 10)

• 5 Example Notebooks - AutoML, Transfer Learning, Hardware, Optimization, Benchmarking
• Google Colab Compatible - Run in browser

Advanced Algorithms (Week 11 - NEW)

• Quantum GANs - Generative adversarial networks for quantum states
• Quantum RL - Q-learning with quantum circuits
• SimpleEnvironment - Test environment for RL

CI/CD Automation (Week 12 - NEW)

• GitHub Actions - Auto-testing on Python 3.9-3.12
• Auto-Publishing - Automatic PyPI releases
• Code Quality - Linting, formatting checks

GPU Acceleration (v0.6.1)

• GPU state-vector simulation - circuit.get_statevector(use_gpu=True) runs the whole circuit on the GPU (CuPy). Measured on an RTX 5060 vs CPU: ~6x in double precision, and 50-75x in single precision (precision='single') at 20-22 qubits, where the CPU becomes the bottleneck.
• Distributed / mixed-precision training - real data-parallel gradient averaging and mixed-precision steps. (Multi-GPU wall-clock speedup requires multiple physical GPUs; on one device these run correctly but sequentially.)
• Windows-friendly - auto-discovers pip-installed CUDA runtime wheels (nvidia-*-cu12) so the GPU backend works without a manual CUDA toolkit setup.

See complete documentation for details.

Features

Core Debugging

• Step-through Debugging - Execute circuits gate-by-gate with breakpoints
• State Inspection - Analyze quantum states at any point
• Circuit Profiling - Depth analysis, gate statistics, optimization suggestions
• Visualization - State vectors, Bloch spheres, and more
• Noise Simulation - Realistic hardware noise models
• Qiskit Integration - Import/export circuits from Qiskit

Quantum Machine Learning (v0.6.0)

• AutoML - One-line interface with automatic optimization
• Advanced Algorithms - Quantum GANs and Quantum Reinforcement Learning
• Transfer Learning - PretrainedQNN, model zoo, fine-tuning
• GPU Acceleration - Multi-GPU, mixed precision (2-3x speedup)
• Error Mitigation - PEC, CDR, realistic noise models
• Circuit Optimization - Gate reduction, compilation, transpilation
• Framework Bridges - Qiskit, PennyLane, Cirq compatibility
• Hardware Backends - IBM Quantum (FREE), AWS Braket
• Benchmarking - QML vs Classical performance analysis
• Hybrid Models - TensorFlow and PyTorch quantum layers
• Quantum Kernels - QSVM with multiple kernel types
• VQE and QAOA - Molecular chemistry and optimization
• Advanced Optimizers - 7 optimizers including QNG
• Ansatz Library - 8 pre-built quantum circuit templates
• Example Notebooks - 5 comprehensive Jupyter tutorials

Quick Start

Installation

pip install quantum-debugger

Basic Circuit Debugging

from quantum_debugger import QuantumCircuit, QuantumDebugger

# Create a Bell state
qc = QuantumCircuit(2)
qc.h(0)
qc.cnot(0, 1)

# Debug step-by-step
debugger = QuantumDebugger(qc)
debugger.step()  # Execute first gate
print(debugger.get_current_state())
debugger.step()  # Execute second gate
print(debugger.get_current_state())

Quantum Machine Learning with AutoML

from quantum_debugger.qml.automl import auto_qnn
import numpy as np

# Load your data
X_train = np.random.randn(100, 4)
y_train = np.random.randint(0, 2, 100)

# One line to train quantum model
model = auto_qnn(X_train, y_train)

# Make predictions
X_test = np.random.randn(20, 4)
predictions = model.predict(X_test)

Manual QNN Configuration

For more control over your quantum neural network:

from quantum_debugger.qml.qnn import QuantumNeuralNetwork

# Create network
qnn = QuantumNeuralNetwork(n_qubits=4)
qnn.compile(optimizer='adam', loss='mse')

# Train
history = qnn.fit(X_train, y_train, epochs=50, batch_size=16)

# Predict
predictions = qnn.predict(X_test)

Advanced Features

Transfer Learning

from quantum_debugger.qml.transfer import PretrainedQNN

# Load pretrained model
pretrained = PretrainedQNN.from_zoo('iris_classifier')

# Fine-tune on your data
pretrained.fine_tune(X_new, y_new, epochs=10, freeze_layers=2)

# Save your model
pretrained.save('models/my_qnn.pkl')

Error Mitigation

from quantum_debugger.qml.mitigation import PEC, CDR

# Probabilistic Error Cancellation
pec = PEC(gate_error_rates={'rx': 0.01, 'cnot': 0.02})
mitigated_result, uncertainty = pec.apply_pec(circuit)

# Clifford Data Regression
cdr = CDR(n_clifford_circuits=50)
training_data = cdr.generate_training_data(n_qubits=4, depth=3)
cdr.train(training_data, noisy_executor)
mitigated = cdr.apply_cdr(noisy_measurement)

Circuit Optimization

from quantum_debugger.optimization import optimize_circuit, compile_circuit

# Simple optimization
gates = [('h', 0), ('h', 0), ('x', 1)]  # H cancels itself
optimized = optimize_circuit(gates)  # Returns: [('x', 1)]

# Multi-level compilation
compiled = compile_circuit(gates, optimization_level=3)

Hardware Deployment

from quantum_debugger.backends import IBMQuantumBackend

# Connect to IBM Quantum (FREE tier)
backend = IBMQuantumBackend()
backend.connect({'token': 'YOUR_FREE_IBM_TOKEN'})

# Execute on real quantum computer
gates = [('h', 0), ('cnot', (0, 1))]
counts = backend.execute(gates, n_shots=1024)

Get your free IBM Quantum token at: https://quantum.ibm.com

Framework Integration

from quantum_debugger.integrations import to_qiskit, from_qiskit, to_pennylane, to_cirq

# Convert to Qiskit
qiskit_circuit = to_qiskit(gates)

# Convert to PennyLane
pennylane_qnode = to_pennylane(gates)

# Convert to Cirq
cirq_circuit = to_cirq(gates)

Installation Options

Basic Installation

pip install quantum-debugger

With Optional Dependencies

# All frameworks
pip install quantum-debugger[all]

# Individual frameworks
pip install quantum-debugger[qiskit]
pip install quantum-debugger[pennylane]
pip install quantum-debugger[cirq]
pip install quantum-debugger[tensorflow]
pip install quantum-debugger[pytorch]

# Hardware backends
pip install quantum-debugger[ibm]  # FREE
pip install quantum-debugger[aws]  # Paid service

# Development tools
pip install quantum-debugger[dev]

Documentation

v0.6.0 Guides:

• V0.6.0 Features - Complete feature reference
• Transfer Learning Guide
• Error Mitigation Guide
• Circuit Optimization Guide
• Hardware Backends Guide

v0.5.0 Guides (still valid):

• QNN Guide
• Hybrid Models Guide
• VQE Guide
• QAOA Guide

Testing

# Run all tests
pytest tests/ -v

# Run specific test suites
pytest tests/qml/ -v
pytest tests/test_optimization.py -v
pytest tests/test_integrations.py -v

# With coverage
pytest tests/ --cov=quantum_debugger --cov-report=html

See FINAL_TEST_SUMMARY.md for detailed test information.

Test Statistics (v0.6.1):

• ~980 tests passing (pytest tests/ -m "not aws")
• GPU-hardware tests require a working CUDA + CuPy install; they skip otherwise
• A few tests are performance/timing based and may vary by machine

Contributing

Contributions are welcome. Please ensure:

1. All tests pass
2. Code follows PEP 8 style guidelines
3. Documentation is updated
4. New features include tests

License

MIT License - see LICENSE file.

Citation

If you use quantum-debugger in your research, please cite:

@software{quantum_debugger_2026,
  title = {Quantum Debugger: Production-Grade Quantum Machine Learning Library},
  author = {Gupta, Raunak Kumar},
  year = {2026},
  url = {https://github.com/Raunakg2005/quantum-debugger}
}

Acknowledgments

Author: Raunak Kumar Gupta
GitHub: @Raunakg2005
LinkedIn: Raunak Kumar Gupta
Supervised by: Dr. Vaibhav Prakash Vasani
Supervisor LinkedIn: Dr. Vaibhav Vasani
Institution: K.J. Somaiya School of Engineering

Links

PyPI: https://pypi.org/project/quantum-debugger/
GitHub: https://github.com/Raunakg2005/quantum-debugger
Issues: https://github.com/Raunakg2005/quantum-debugger/issues
Documentation: https://github.com/Raunakg2005/quantum-debugger#readme

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Version: 0.6.1
Last Updated: July 2026