no code implementations • 28 Dec 2023 • Matvei Anoshin, Asel Sagingalieva, Christopher Mansell, Vishal Shete, Markus Pflitsch, Alexey Melnikov
The contemporary drug design process demands considerable time and resources to develop each new compound entering the market.
no code implementations • 27 Dec 2023 • Asel Sagingalieva, Stefan Komornyik, Arsenii Senokosov, Ayush Joshi, Alexander Sedykh, Christopher Mansell, Olga Tsurkan, Karan Pinto, Markus Pflitsch, Alexey Melnikov
Predicting solar panel power output is crucial for advancing the energy transition but is complicated by the variable and non-linear nature of solar energy.
no code implementations • 4 Nov 2023 • Luca Lusnig, Asel Sagingalieva, Mikhail Surmach, Tatjana Protasevich, Ovidiu Michiu, Joseph McLoughlin, Christopher Mansell, Graziano de' Petris, Deborah Bonazza, Fabrizio Zanconati, Alexey Melnikov, Fabio Cavalli
We introduce a hybrid quantum neural network model that leverages real-world clinical data to assess non-alcoholic liver steatosis accurately.
no code implementations • 28 Jul 2023 • Nathan Haboury, Mo Kordzanganeh, Sebastian Schmitt, Ayush Joshi, Igor Tokarev, Lukas Abdallah, Andrii Kurkin, Basil Kyriacou, Alexey Melnikov
We propose a novel hybrid supervised learning approach and test it on hypothetical situations on a concrete city graph.
no code implementations • 18 Jul 2023 • Andrii Kurkin, Jonas Hegemann, Mo Kordzanganeh, Alexey Melnikov
Efficient and sustainable power generation is a crucial concern in the energy sector.
no code implementations • 21 Apr 2023 • Alexandr Sedykh, Maninadh Podapaka, Asel Sagingalieva, Karan Pinto, Markus Pflitsch, Alexey Melnikov
Finding the distribution of the velocities and pressures of a fluid (by solving the Navier-Stokes equations) is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and the design of pipeline systems.
no code implementations • 18 Apr 2023 • Arsenii Senokosov, Alexandr Sedykh, Asel Sagingalieva, Basil Kyriacou, Alexey Melnikov
This model demonstrated a record-breaking classification accuracy of 99. 21% on the full MNIST dataset, surpassing the performance of known quantum-classical models, while having eight times fewer parameters than its classical counterpart.
no code implementations • 6 Mar 2023 • Mo Kordzanganeh, Daria Kosichkina, Alexey Melnikov
In this work, we introduce a new, interpretable class of hybrid quantum neural networks that pass the inputs of the dataset in parallel to 1) a classical multi-layered perceptron and 2) a variational quantum circuit, and then the outputs of the two are linearly combined.
no code implementations • 14 Feb 2023 • Serge Rainjonneau, Igor Tokarev, Sergei Iudin, Saaketh Rayaprolu, Karan Pinto, Daria Lemtiuzhnikova, Miras Koblan, Egor Barashov, Mo Kordzanganeh, Markus Pflitsch, Alexey Melnikov
This paper introduces a set of quantum algorithms to solve the mission planning problem and demonstrate an advantage over the classical algorithms implemented thus far.
no code implementations • 1 Dec 2022 • Mo Kordzanganeh, Pavel Sekatski, Leonid Fedichkin, Alexey Melnikov
Independently, angle-embedded supervised quantum neural networks were shown to produce truncated Fourier series with a degree directly dependent on two factors: the depth of the encoding and the number of parallel qubits the encoding applied to.
no code implementations • 28 Nov 2022 • Mohammad Kordzanganeh, Markus Buchberger, Basil Kyriacou, Maxim Povolotskii, Wilhelm Fischer, Andrii Kurkin, Wilfrid Somogyi, Asel Sagingalieva, Markus Pflitsch, Alexey Melnikov
This study paves the way to understanding the optimal combination of available software and hardware for executing practical quantum algorithms.
no code implementations • 10 Nov 2022 • Asel Sagingalieva, Mohammad Kordzanganeh, Nurbolat Kenbayev, Daria Kosichkina, Tatiana Tomashuk, Alexey Melnikov
We propose a novel hybrid quantum neural network for drug response prediction, based on a combination of convolutional, graph convolutional, and deep quantum neural layers of 8 qubits with 363 layers.
no code implementations • 10 May 2022 • Asel Sagingalieva, Mo Kordzanganeh, Andrii Kurkin, Artem Melnikov, Daniil Kuhmistrov, Michael Perelshtein, Alexey Melnikov, Andrea Skolik, David Von Dollen
We test our approaches in a car image classification task and demonstrate a full-scale implementation of the hybrid quantum ResNet model with the tensor train hyperparameter optimization.