arXiv:2402.08726v2 Announce Type: replace-cross Abstract: We study quantum neural networks made by parametric one-qubit gates and fixed two-qubit gates in the limit of infinite width, where the generated function is the expectation value of the sum of single-qubit observables over all the qubits. First, we prove that the probability distribution of the function generated by the untrained network with randomly initialized parameters converges in distribution to a Gaussian process whenever each measured qubit is correlated only with few other measured qubits. Then, we analytically characterize t
This research builds on fundamental theoretical work in quantum computing and machine learning, representing a continued progression in understanding the statistical properties of quantum neural networks.
Understanding the theoretical underpinnings of quantum neural networks, especially their convergence to Gaussian processes, is crucial for developing robust and predictable quantum AI.
This theoretical finding provides a mathematical framework for analyzing the behavior of certain quantum neural networks, potentially simplifying their design and improving their reliability and interpretability.
- · Quantum Machine Learning Researchers
- · Quantum Computing Hardware Developers
- · Academics in Quantum Physics & AI
- · Companies relying on opaque quantum AI models
This research provides a theoretical foundation for understanding the behavior of specific types of quantum neural networks.
Improved theoretical understanding could accelerate the development of more stable and explainable quantum artificial intelligence applications.
The development of reliable quantum AI might eventually lead to breakthroughs in materials science, drug discovery, and complex optimization problems.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG