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Found 5 papers, 1 papers with code
Quantum Reinforcement Learning via Policy Iteration
Quantum computing has shown the potential to substantially speed up machine learning applications, in particular for supervised and unsupervised learning.
Prospects and challenges of quantum finance
Quantum computers are expected to have substantial impact on the finance industry, as they will be able to solve certain problems considerably faster than the best known classical algorithms.
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.
Quantum algorithms for Second-Order Cone Programming and Support Vector Machines
We present a quantum interior-point method (IPM) for second-order cone programming (SOCP) that runs in time $\widetilde{O} \left( n\sqrt{r} \frac{\zeta \kappa}{\delta^2} \log \left(1/\epsilon\right) \right)$ where $r$ is the rank and $n$ the dimension of the SOCP, $\delta$ bounds the distance of intermediate solutions from the cone boundary, $\zeta$ is a parameter upper bounded by $\sqrt{n}$, and $\kappa$ is an upper bound on the condition number of matrices arising in the classical IPM for SOCP.
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 $\delta$.
