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Found 6 papers, 2 papers with code
Do you know what q-means?
The time complexity is $O\big(\frac{k^{2}}{\varepsilon^2}(\sqrt{k}d + \log(Nd))\big)$ and maintains the polylogarithmic dependence on $N$ while improving the dependence on most of the other parameters.
Quantum algorithms for SVD-based data representation and analysis
This paper narrows the gap between previous literature on quantum linear algebra and practical data analysis on a quantum computer, formalizing quantum procedures that speed-up the solution of eigenproblems for data representations in machine learning.
Quantum algorithms for spectral sums
We propose and analyze new quantum algorithms for estimating the most common spectral sums of symmetric positive definite (SPD) matrices.
Quantum Expectation-Maximization for Gaussian Mixture Models
In this work we define and use a quantum version of EM to fit a Gaussian Mixture Model.
q-means: A quantum algorithm for unsupervised machine learning
For a natural notion of well-clusterable datasets, the running time becomes $\widetilde{O}\left( k^2 d \frac{\eta^{2. 5}}{\delta^3} + k^{2. 5} \frac{\eta^2}{\delta^3} \right)$ per iteration, which is linear in the number of features $d$, and polynomial in the rank $k$, the maximum square norm $\eta$ and the error parameter $\delta$.
Quantum classification of the MNIST dataset with Slow Feature Analysis
We simulate the quantum classifier (including errors) and show that it can provide classification of the MNIST handwritten digit dataset, a widely used dataset for benchmarking classification algorithms, with $98. 5\%$ accuracy, similar to the classical case.
