167 citations · 507 across the 15 of their papers we have counts for
15 papers
Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin +4
Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, a…
Hybrid Fourier Neural Operator for Surrogate Modeling of Laser Processing with a Quantum-Circuit Mixer
Mateusz Papierz, Asel Sagingalieva, Alix Benoit +3
Data-driven surrogates can replace expensive multiphysics solvers for parametric PDEs, yet building compact, accurate neural operators for three-dimensional problems remains challe…
A Fast and Generalizable Fourier Neural Operator-Based Surrogate for Melt-Pool Prediction in Laser Processing
Alix Benoit, Toni Ivas, Mateusz Papierz +3
High-fidelity simulations of laser welding capture complex thermo-fluid phenomena, including phase change, free-surface deformation, and keyhole dynamics, however their computation…
On Stopping Times of Power-one Sequential Tests: Tight Lower and Upper Bounds
Shubhada Agrawal, Ashwin Ram, Aaditya Ramdas
We present two general lower bounds for stopping times of sequential tests between arbitrary composite nulls and alternatives . The first lower bound is fo…
Predictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networks
Nayoung Lee, Minsoo Shin, Asel Sagingalieva +5
Accurate prediction and stabilization of blast furnace temperatures are crucial for optimizing the efficiency and productivity of steel production. Traditional methods often strugg…
Multi-stream physics hybrid networks for solving Navier-Stokes equations
Aleksandr Sedykh, Tatjana Protasevich, Mikhail Surmach +4
Understanding and solving fluid dynamics equations efficiently remains a fundamental challenge in computational physics. Traditional numerical solvers and physics-informed neural n…
Hybrid quantum cycle generative adversarial network for small molecule generation
Matvei Anoshin, Asel Sagingalieva, Christopher Mansell +4
The drug design process currently requires considerable time and resources to develop each new compound that enters the market. This work develops an application of hybrid quantum…
Photovoltaic power forecasting using quantum machine learning
Asel Sagingalieva, Stefan Komornyik, Arsenii Senokosov +6
Accurate forecasting of photovoltaic power is essential for reliable grid integration, yet remains difficult due to highly variable irradiance, complex meteorological drivers, site…
Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis
Luca Lusnig, Asel Sagingalieva, Mikhail Surmach +9
In the realm of liver transplantation, accurately determining hepatic steatosis levels is crucial. Recognizing the essential need for improved diagnostic precision, particularly fo…
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
Alexandr Sedykh, Maninadh Podapaka, Asel Sagingalieva +3
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 industrie…
Quantum machine learning for image classification
Arsenii Senokosov, Alexandr Sedykh, Asel Sagingalieva +2
Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by…
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…
Practical application-specific advantage through hybrid quantum computing
Michael Perelshtein, Asel Sagingalieva, Karan Pinto +7
Quantum computing promises to tackle technological and industrial problems insurmountable for classical computers. However, today's quantum computers still have limited demonstrabl…
Hybrid quantum ResNet for car classification and its hyperparameter optimization
Asel Sagingalieva, Mo Kordzanganeh, Andrii Kurkin +6
Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of milli…