A three-qubit Qiskit circuit encodes hand-set priors and conditional probabilities for network spikes, vulnerabilities, and false alarms, and statevector post-selection recovers the same Bayesian posteriors that went in.
Autoencoder-based learning of Quantum phase transitions in the two-component Bose-Hubbard model
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abstract
This paper investigates the use of autoencoders and machine learning methods for detecting and analyzing quantum phase transitions in the Two-Component Bose-Hubbard Model. By leveraging deep learning models such as autoencoders, we investigate latent space representations, reconstruction error analysis, and cluster distance calculations to identify phase boundaries and critical points. The study is supplemented by dimensionality reduction techniques such as PCA and t-SNE for latent space visualization. The results demonstrate the potential of autoencoders to describe the dynamics of quantum phase transitions.
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quant-ph 1years
2025 1verdicts
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Quantum Bayesian inference with Suport vector states for intrusion detection
A three-qubit Qiskit circuit encodes hand-set priors and conditional probabilities for network spikes, vulnerabilities, and false alarms, and statevector post-selection recovers the same Bayesian posteriors that went in.