Description
Abstract
Federated Learning (FL) is a decentralized machine learning paradigm where multiple clients train a shared model without centralizing data. Quantum Federated Learning (QFL) is an emerging field that leverages quantum computing for distributed learning tasks, providing potential advantages in security, privacy, and computational efficiency.
Currently, the Classiq Library lacks an implementation for Quantum Federated Learning, making it an ideal feature addition. This implementation will serve as a valuable resource for researchers and developers interested in quantum-enhanced federated learning strategies.
Motivation
Quantum Machine Learning (QML) has shown promise in various fields, but real-world applications require scalable and privacy-preserving training methods. Federated Learning enables training models across distributed data sources while maintaining data privacy. Given that Classiq already includes structured problem-solving examples (e.g., hybrid variational circuits), adding QFL will enhance the library’s collection of quantum AI implementations.
By introducing Quantum Federated Learning (QFL) on Classiq, this feature will:
- Enable distributed quantum machine learning with secure data privacy.
- Benchmark quantum models trained across multiple nodes.
- Compare performance with classical FL architectures.
Proposed Solution
We propose implementing Quantum Federated Learning (QFL) in Classiq by leveraging:
Quantum Variational Circuits for local model training on each client.
Quantum Gradient Updates using distributed quantum nodes.
Secure Quantum Aggregation using entanglement-based communication.
Classiq’s circuit optimization tools to enhance computational efficiency.
The implementation will include:
- A QFL problem definition using Classiq’s quantum circuit design tools.
- A quantum-enhanced model training loop with variational circuits.
- Execution on simulators and quantum devices to compare accuracy and performance.
- Example use cases in privacy-preserving machine learning, such as medical AI and finance.
Technical Details
Quantum Federated Learning Process
- Client-side Quantum Model Training
Each client trains a local quantum model using Variational Quantum Circuits (VQC).
Qubits encode local datasets for training updates.
- Quantum Gradient Aggregation
Clients send quantum-encoded gradient updates to the central server.
The server applies quantum-safe aggregation methods.
- Global Model Update & Synchronization
The global model is updated and redistributed among clients.
Secure quantum channels facilitate communication.
Input Example
python
qfl_model = {
"clients": ["Node_1", "Node_2", "Node_3"],
"local_models": ["Quantum NN", "Variational QClassifier"],
"aggregation_method": "Quantum Secure Sum"
}
Team Details
@Yuvan010
@sriram03psr
@ManjulaGandhi
@sgayathridevi