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[2506.16332] Feedback-driven recurrent quantum neural network universality
Summary
This paper explores the capabilities of feedback-driven recurrent quantum neural networks, demonstrating their potential for real-time computation and approximation of state-space systems in quantum reservoir computing.
Why It Matters
As quantum computing continues to evolve, understanding the capabilities of quantum neural networks is crucial for advancing machine learning applications. This research highlights how feedback mechanisms can enhance quantum reservoir computing, making it more accessible and effective for real-time data processing, which is vital in various fields including AI and data science.
Key Takeaways
- Feedback-driven quantum neural networks can process temporal data effectively.
- These networks offer a logarithmic growth in qubit requirements relative to approximation accuracy.
- The study confirms the universality of quantum recurrent neural networks with linear readouts.
Quantum Physics arXiv:2506.16332 (quant-ph) [Submitted on 19 Jun 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:Feedback-driven recurrent quantum neural network universality Authors:Lukas Gonon, Rodrigo Martínez-Peña, Juan-Pablo Ortega View a PDF of the paper titled Feedback-driven recurrent quantum neural network universality, by Lukas Gonon and 1 other authors View PDF HTML (experimental) Abstract:Quantum reservoir computing uses the dynamics of quantum systems to process temporal data, making it particularly well-suited for machine learning with noisy intermediate-scale quantum devices. Recent developments have introduced feedback-based quantum reservoir systems, which process temporal information with comparatively fewer components and enable real-time computation while preserving the input history. Motivated by their promising empirical performance, in this work, we study the approximation capabilities of feedback-based quantum reservoir computing. More specifically, we are concerned with recurrent quantum neural networks, which are quantum analogues of classical recurrent neural networks. Our results show that regular state-space systems can be approximated using quantum recurrent neural networks without the curse of dimensionality and with the number of qubits only growing logarithmically in the reciprocal of the prescribed approximation accuracy. Notably, our analysis demonstrates that quantum recurrent neural networks are universal with linear reado...