tensor-network-quantum-federated

name

tensor-network-quantum-federated

description

Privacy-aware federated learning combining tensor-network compression with quantum-enhanced processing for medical diagnosis. Use when building multi-institutional medical AI systems that need: (1) MPC-secured aggregation, (2) small-qubit quantum processing on compressed features, (3) tensor-network frontends (MPS/TTN/MERA). Addresses the dual challenge of communication overhead and qubit limitations.

Tensor-Network Quantum Federated Learning

Architecture Overview

[Client A] ── MPS/TTN/MERA ──┐
[Client B] ── MPS/TTN/MERA ──┤ → MPC Aggregation → QEP → Diagnosis
[Client C] ── MPS/TTN/MERA ──┘

Three-Layer Design

Layer 1: Tensor-Network Frontend (Client-Side)

Compresses high-dimensional medical images into compact latent representations.

Frontend	Compression	Best For
MPS	Linear scaling	1D sequences, time-series
TTN	Logarithmic scaling	2D images (recommended)
MERA	Multi-scale	Hierarchical features

Key insight: TTN+QEP combination shows the most balanced overall profile.

Layer 2: MPC-Secured Aggregation (Server-Side)

• Multi-party computation protects aggregated latents
• Communication cost ∝ latent dimension (not original image size)
• Tensor-network compression directly reduces MPC overhead

Layer 3: Quantum-Enhanced Processor (Post-Aggregation)

• Quantum-state embedding of aggregated latents
• Observable-based readout for classification
• Stable when qubit count ≈ latent dimension
• Degrades under noise vs. noiseless simulation

Design Principles

Co-Design Requirement

Representation compression, quantum refinement, and privacy deployment must be optimized jointly, not independently.

Qubit-Latent Matching

QEP stability requires qubit count sufficiently matched to latent dimension:

• Too few qubits → information bottleneck
• Too many qubits → noise amplification on NISQ devices

Dual Role of Compression

Tensor-network compression serves two purposes:

1. Enables small-qubit quantum processing
2. Reduces MPC communication overhead

Implementation Checklist

1. Choose tensor-network frontend based on data modality
2. Set latent dimension to match available qubit count
3. Configure MPC protocol for chosen latent dimension
4. Train QEP with noise models matching target hardware
5. Validate end-to-end on PneumoniaMNIST or similar benchmark

Performance Notes

• QEP effect is frontend-dependent, not uniform across architectures
• Noisy conditions degrade QEP performance relative to noiseless
• TTN frontend recommended as starting point for medical imaging
• Communication cost governed by latent dimension, not original data size

Related Papers in Knowledge Graph

• ID 250: Quantum-Enhanced Processing with Tensor-Network Frontends
• ID 260: Adaptive Hybrid Quantum-Classical Feature Fusion
• ID 261: QML for Medical Image Classification Review