q-photonas-quantum-nas

name	q-photonas-quantum-nas
description	Q-PhotoNAS: Hybrid Quantum Neural Architecture Search framework for photonic quantum-classical models using genetic algorithm and learnable phase encoding
category	ai_collection

Q-PhotoNAS Quantum Neural Architecture Search

Description

Q-PhotoNAS methodology — Hybrid Quantum Neural Architecture Search (QNAS) framework for photonic quantum computing. Combines genetic algorithm-based architecture search with learnable phase encoding to automatically design effective hybrid photonic quantum-classical models. Addresses the challenge that existing approaches rely on manually tuned architectures that fail to account for collaboration between classical preprocessing, phase encoding, and photonic circuit structure.

Activation Keywords

• Q-PhotoNAS
• quantum neural architecture search
• photonic quantum NAS
• 光子量子神经架构搜索
• hybrid quantum classical NAS
• photonic circuit search
• learnable phase encoding
• quantum photonic architecture

Core Concepts

Photonic Quantum Computing Platform

• Photonic qubits: Quantum information encoded in optical modes
• Phase encoding: Data mapped to optical phases for quantum processing
• Hardware constraints: Physical limitations on circuit depth, connectivity, and measurement

Neural Architecture Search (NAS)

• Search space: Possible combinations of classical preprocessing, phase encoding, and photonic circuit structures
• Search strategy: Genetic algorithm with evolution-based optimization
• Evaluation metric: Model accuracy subject to hardware compatibility constraints

Learnable Phase Encoding

• Traditional fixed phase encoding → learnable parameters
• Jointly optimizes encoding strategy with quantum circuit structure
• Enables adaptive data representation optimized for specific tasks

Usage Patterns

Pattern 1: Automated Photonic QML Architecture Design

1. Define search space: preprocessing layers × phase encoding × circuit depth
2. Initialize population of random architectures
3. Evaluate each: train → measure accuracy → check hardware compatibility
4. Apply genetic operators: selection, crossover, mutation
5. Iterate until convergence → output best architecture
6. Deploy on photonic quantum hardware

Pattern 2: Learnable Phase Encoding Optimization

1. Parameterize phase encoding as trainable variables
2. Jointly optimize encoding + circuit parameters
3. Use gradient-based or evolution-based optimization
4. Validate encoding quality on held-out data

Implementation Guidelines

Search Space Design

Architecture = {
    preprocessing: [classical layers, activation functions],
    phase_encoding: [encoding_type, learnable_parameters],
    photonic_circuit: [depth, connectivity, measurement_basis]
}

Genetic Algorithm Configuration

• Population size: 50-200 architectures
• Selection: Tournament or rank-based
• Crossover: Structure-preserving recombination
• Mutation: Add/remove layers, modify encoding, change connectivity
• Elitism: Preserve top-k architectures across generations

Hardware Compatibility Constraints

• Circuit depth ≤ device coherence limit
• Connectivity matches photonic chip topology
• Measurement compatible with available detectors

Error Handling

Search Space Explosion

• Apply hierarchical search: coarse structure → fine tuning
• Use early stopping for poor-performing architectures
• Prune dominated architectures during evolution

Hardware Mismatch

• Validate architecture constraints before training
• Use penalty terms in fitness function for violations
• Maintain feasibility throughout search process

Training Instability

• Use weight initialization strategies compatible with quantum layers
• Apply learning rate scheduling
• Monitor gradient flow through quantum-classical boundary

References

• arXiv:2605.22097 - Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
• Neural Architecture Search (NAS) literature
• Photonic quantum computing platforms

arXiv Reference

• Paper: Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
• ID: 2605.22097
• Date: 2026-05-21
• Authors: Farah Elnakhal, Alberto Marchisio, Nouhaila Innan