The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

Programmable quantum simulators and quantum computers are opening unprecedented
opportunities for exploring and exploiting the properties of highly entangled complex …

Quantum technology promises to revolutionize how we learn about the physical world. An
experiment that processes quantum data with a quantum computer could have substantial …

The use of quantum computing for machine learning is among the most exciting prospective
applications of quantum technologies. However, machine learning tasks where data is …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

Predicting the properties of complex, large-scale quantum systems is essential for
developing quantum technologies. We present an efficient method for constructing an …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …

Neural network-based machine learning has recently proven successful for many complex
applications ranging from image recognition to precision medicine. However, its direct …