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Quantum Advantage in Variational Bayes Inference
Variational Bayes (VB) inference algorithm is used widely to estimate both the parameters and the unobserved hidden variables in generative statistical models.
Quantum Approximation of Normalized Schatten Norms and Applications to Learning
Efficient measures to determine similarity of quantum states, such as the fidelity metric, have been widely studied.
Ansatz-Independent Variational Quantum Classifier
Next, we propose a variational circuit realization (VCR) for designing efficient quantum circuits for a given unitary operator.
Quantum Expectation-Maximization Algorithm
Clustering algorithms are a cornerstone of machine learning applications.
A Quantum Extension of Variational Bayes Inference
Variational Bayes (VB) inference is one of the most important algorithms in machine learning and widely used in engineering and industry.
Deterministic Quantum Annealing Expectation-Maximization Algorithm
Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates.
Relaxation of the EM Algorithm via Quantum Annealing for Gaussian Mixture Models
We propose a modified expectation-maximization algorithm by introducing the concept of quantum annealing, which we call the deterministic quantum annealing expectation-maximization (DQAEM) algorithm.
Relaxation of the EM Algorithm via Quantum Annealing
The EM algorithm is a novel numerical method to obtain maximum likelihood estimates and is often used for practical calculations.
