no code implementations • 3 Mar 2022 • El Amine Cherrat, Iordanis Kerenidis, Anupam Prakash
Quantum computing has shown the potential to substantially speed up machine learning applications, in particular for supervised and unsupervised learning.
no code implementations • 12 Nov 2020 • Adam Bouland, Wim van Dam, Hamed Joorati, Iordanis Kerenidis, Anupam Prakash
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.
no code implementations • 19 Aug 2019 • Iordanis Kerenidis, Alessandro Luongo, Anupam Prakash
In this work we define and use a quantum version of EM to fit a Gaussian Mixture Model.
no code implementations • 19 Aug 2019 • Iordanis Kerenidis, Anupam Prakash, Dániel Szilágyi
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.
2 code implementations • NeurIPS 2019 • Iordanis Kerenidis, Jonas Landman, Alessandro Luongo, Anupam Prakash
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 $\delta$.