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Hybrid quantum cycle generative adversarial network for small molecule generation
The contemporary drug design process demands considerable time and resources to develop each new compound entering the market.
Photovoltaic power forecasting using quantum machine learning
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.
Hybrid quantum image classification and federated learning for hepatic steatosis diagnosis
We introduce a hybrid quantum neural network model that leverages real-world clinical data to assess non-alcoholic liver steatosis accurately.
A supervised hybrid quantum machine learning solution to the emergency escape routing problem
We propose a novel hybrid supervised learning approach and test it on hypothetical situations on a concrete city graph.
Forecasting the steam mass flow in a powerplant using the parallel hybrid network
Efficient and sustainable power generation is a crucial concern in the energy sector.
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
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.
Quantum machine learning for image classification
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.
Parallel Hybrid Networks: an interplay between quantum and classical neural networks
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.
Quantum algorithms applied to satellite mission planning for Earth observation
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.
An exponentially-growing family of universal quantum circuits
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.
Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
This study paves the way to understanding the optimal combination of available software and hardware for executing practical quantum algorithms.
Hybrid quantum neural network for drug response prediction
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.
Hybrid quantum ResNet for car classification and its hyperparameter optimization
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.
