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

During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

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

The ability to control multidimensional quantum systems is central to the development of
advanced quantum technologies. We demonstrate a multidimensional integrated quantum …

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

We present the problem of approximating the time-evolution operator $ e^{-i\hat {H} t} $ to
error $\epsilon $, where the Hamiltonian $\hat {H}=(\langle G|\otimes\hat {\mathcal {I}})\hat …

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

The usual way to reveal properties of an unknown quantum state, given many copies of a
system in that state, is to perform measurements of different observables and to analyse the …

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

Gate set tomography (GST) is a protocol for detailed, predictive characterization of logic
operations (gates) on quantum computing processors. Early versions of GST emerged …