Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms

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.

Benchmarking

Hybrid quantum neural network for drug response prediction

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.

Drug Response Prediction Quantum Machine Learning

Quantum Machine Learning for Radio Astronomy

no code implementations • 5 Dec 2021 • Mohammad Kordzanganeh, Aydin Utting, Anna Scaife

In this work we introduce a novel approach to the pulsar classification problem in time-domain radio astronomy using a Born machine, often referred to as a quantum neural network.

Astronomy BIG-bench Machine Learning +2