Noisy Intermediate-Scale Quantum (NISQ) systems and associated programming interfaces
make it possible to explore and investigate the design and development of quantum …

For the solution of time-dependent nonlinear differential equations, we present variational
quantum algorithms (VQAs) that encode both space and time in qubit registers. The …

We propose a physics-informed quantum algorithm to solve nonlinear and multidimensional
differential equations (DEs) in a quantum latent space. We suggest a strategy for building …

Machine learning models capable of handling the large data sets collected in the financial
world can often become black boxes expensive to run. The quantum computing paradigm …

Clinical trials are necessary for assessing the safety and efficacy of treatments. However,
trial timelines are severely delayed with minimal success due to a multitude of factors …

We can learn from analyzing quantum convolutional neural networks (QCNNs) that: 1)
working with quantum data can be perceived as embedding physical system parameters …

Parameterized quantum circuits as machine learning models are typically well described by
their representation as a partial Fourier series of the input features, with frequencies …

Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific
computing problems, ranging from the solution of Partial Differential Equations to data …

Quantum machine learning techniques are commonly considered one of the most promising
candidates for demonstrating practical quantum advantage. In particular, quantum kernel …

Quantum generative modeling (QGM) relies on preparing quantum states and generating
samples from these states as hidden—or known—probability distributions. As distributions …