no code implementations • 31 May 2023 • Sohum Thakkar, Skander Kazdaghli, Natansh Mathur, Iordanis Kerenidis, André J. Ferreira-Martins, Samurai Brito
Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications.
no code implementations • 29 Mar 2023 • El Amine Cherrat, Snehal Raj, Iordanis Kerenidis, Abhishek Shekhar, Ben Wood, Jon Dee, Shouvanik Chakrabarti, Richard Chen, Dylan Herman, Shaohan Hu, Pierre Minssen, Ruslan Shaydulin, Yue Sun, Romina Yalovetzky, Marco Pistoia
Quantum machine learning has the potential for a transformative impact across industry sectors and in particular in finance.
no code implementations • 16 Sep 2022 • El Amine Cherrat, Iordanis Kerenidis, Natansh Mathur, Jonas Landman, Martin Strahm, Yun Yvonna Li
In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis.
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, 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.
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.
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 $\delta$.
no code implementations • 7 Dec 2018 • Jonathan Allcock, Chang-Yu Hsieh, Iordanis Kerenidis, Shengyu Zhang
The running times of our algorithms can be quadratically faster in the size of the network than their standard classical counterparts since they depend linearly on the number of neurons in the network, as opposed to the number of connections between neurons as in the classical case.
2 code implementations • 12 Nov 2018 • Anupama Unnikrishnan, Ian J. MacFarlane, Richard Yi, Eleni Diamanti, Damian Markham, Iordanis Kerenidis
Quantum communication networks have the potential to revolutionise information and communication technologies.
Quantum Physics
no code implementations • 22 May 2018 • Iordanis Kerenidis, Alessandro Luongo
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.
no code implementations • 27 Feb 2017 • Alex B. Grilo, Iordanis Kerenidis, Timo Zijlstra
Learning with Errors is one of the fundamental problems in computational learning theory and has in the last years become the cornerstone of post-quantum cryptography.
Quantum Physics Computational Complexity