Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data

2026-04-14 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

A study systematically evaluates five quantum encoding strategies for complex-valued Synthetic Aperture Radar (SAR) data on the MSTAR benchmark dataset. The research compares magnitude-only, joint complex, I/Q-based, preprocessed phase, and pure quantum encoding methods. Contrary to the expectation that phase information would always improve performance, the study found that in hybrid quantum-classical architectures, magnitude-only encoding achieved superior results, reaching 99.57% accuracy on a 3-class task and 71.19% on an 8-class task. However, for purely quantum architectures with 184-224 trainable parameters and no classical components, phase information proved essential, contributing up to a 21.65% accuracy improvement. These findings suggest that the utility of phase information is architecture-dependent.

Key takeaway

For AI Scientists designing quantum machine learning models for complex-valued SAR data, your choice of encoding strategy must align with your architecture. If you are using hybrid quantum-classical models, prioritize magnitude-only encoding for better performance. Conversely, if you are developing purely quantum architectures, ensure phase information is explicitly included to achieve discriminative representations and significant accuracy gains.

Key insights

Phase information's utility in quantum encoding of SAR data depends critically on the model architecture.

Principles

• Hybrid models compensate for missing phase.
• Purely quantum models require phase for discrimination.

Method

Five quantum encoding strategies (magnitude-only, joint complex, I/Q-based, preprocessed phase, pure quantum) were compared on MSTAR for SAR Automatic Target Recognition.

In practice

• Use magnitude-only encoding for hybrid QML.
• Incorporate phase for purely quantum QML.

Topics

• Quantum Encoding
• SAR Data
• Phase Information
• Hybrid Quantum-Classical Architectures
• Purely Quantum Architectures

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.