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Phase-Aware Quantum–Classical Feedback Compression in RIS: Trainability and Performance Limits
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DOI：10.1109/ojvt.2026.3721274.png)
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Abstract
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This paper considers a multi-antenna base station (BS) communicating with a single-antenna user through a reconfigurable intelligent surface (RIS). In large RIS deployments, feeding back the high-dimensional phase vector $\boldsymbol{\theta }\in [0,2\pi)^{N}$, where $N$ is the number of RIS elements, poses a major scalability challenge. Conventional codebook-based compression is limited to fixed linear subspaces. To address this, we propose Phase-Aware Quantum RIS (PAQ-RIS), a hybrid quantum–classical autoencoder that compresses RIS phase profiles using variational quantum circuits. Phase shifts are angle-encoded into quantum states, processed by parameterized quantum circuits, and reconstructed by a classical decoder. Results show that PAQ-RIS improves beamforming accuracy by up to 14.5% over classical autoencoders for an RIS with $N=512$ elements using $q=9$ qubits at a feedback compression ratio of approximately 19%. For $N=1024$, the framework continues to provide performance gains, achieving beamforming accuracy improvements of up to 11.33%. However, for larger RIS configurations with $N=2048$ elements, the performance gain decreases, with the best improvement dropping to 4.44% due to barren plateaus and near-Haar-random latent states, establishing the practical scalability limits of quantum-assisted compression.
Keywords:
Barren plateau
quantum computation
MISO systems
quantum autoencoder
RIS
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