115 citations · 394 across the 14 of their papers we have counts for
14 papers
Soft-Quantum Algorithms
Basil Kyriacou, Mo Kordzanganeh, Maniraman Periyasamy +1
Quantum operations on pure states can be fully represented by unitary matrices. Variational quantum circuits, also known as quantum neural networks, embed data and trainable parame…
Superposed parameterised quantum circuits
Viktoria Patapovich, Maniraman Periyasamy, Mo Kordzanganeh +1
Quantum machine learning has shown promise for high-dimensional data analysis, yet many existing approaches rely on linear unitary operations and shared trainable parameters across…
TQml Simulator: optimized simulation of quantum machine learning
Viacheslav Kuzmin, Basil Kyriacou, Tatjana Protasevich +3
Hardware-efficient circuits employed in Quantum Machine Learning are typically composed of alternating layers of uniformly applied gates. High-speed numerical simulators for such c…
Information plane and compression-gnostic feedback in quantum machine learning
Nathan Haboury, Mo Kordzanganeh, Alexey Melnikov +1
The information plane (Tishby et al. arXiv:physics/0004057, Shwartz-Ziv et al. arXiv:1703.00810) has been proposed as an analytical tool for studying the learning dynamics of neura…
Pixel-wise RL on Diffusion Models: Reinforcement Learning from Rich Feedback
Mo Kordzanganeh, Danial Keshvary, Nariman Arian
Latent diffusion models are the state-of-the-art for synthetic image generation. To align these models with human preferences, training the models using reinforcement learning on h…
A supervised hybrid quantum machine learning solution to the emergency escape routing problem
Nathan Haboury, Mo Kordzanganeh, Sebastian Schmitt +6
Managing the response to natural disasters effectively can considerably mitigate their devastating impact. This work explores the potential of using supervised hybrid quantum machi…
Forecasting steam mass flow in power plants using the parallel hybrid network
Andrii Kurkin, Jonas Hegemann, Mo Kordzanganeh +1
Efficient and sustainable power generation is a crucial concern in the energy sector. In particular, thermal power plants grapple with accurately predicting steam mass flow, which…
Parallel Hybrid Networks: an interplay between quantum and classical neural networks
Mo Kordzanganeh, Daria Kosichkina, Alexey Melnikov
Quantum neural networks represent a new machine learning paradigm that has recently attracted much attention due to its potential promise. Under certain conditions, these models ap…
Quantum algorithms applied to satellite mission planning for Earth observation
Serge Rainjonneau, Igor Tokarev, Sergei Iudin +8
Earth imaging satellites are a crucial part of our everyday lives that enable global tracking of industrial activities. Use cases span many applications, from weather forecasting t…
Quantum Machine Learning: from physics to software engineering
Alexey Melnikov, Mohammad Kordzanganeh, Alexander Alodjants +1
Quantum machine learning is a rapidly growing field at the intersection of quantum technology and artificial intelligence. This review provides a two-fold overview of several key a…
An exponentially-growing family of universal quantum circuits
Mo Kordzanganeh, Pavel Sekatski, Leonid Fedichkin +1
Quantum machine learning has become an area of growing interest but has certain theoretical and hardware-specific limitations. Notably, the problem of vanishing gradients, or barre…
Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
Mohammad Kordzanganeh, Markus Buchberger, Basil Kyriacou +7
Powerful hardware services and software libraries are vital tools for quickly and affordably designing, testing, and executing quantum algorithms. A robust large-scale study of how…
Hybrid quantum neural network for drug response prediction
Asel Sagingalieva, Mohammad Kordzanganeh, Nurbolat Kenbayev +3
Cancer is one of the leading causes of death worldwide. It is caused by a variety of genetic mutations, which makes every instance of the disease unique. Since chemotherapy can hav…
Quantum Machine Learning for Radio Astronomy
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.…