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

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 …

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

Quantum error mitigation has been proposed as a means to combat unwanted and
unavoidable errors in near-term quantum computing without the heavy resource overheads …

Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

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

We propose a method for detecting bipartite entanglement in a many-body mixed state
based on estimating moments of the partially transposed density matrix. The estimates are …

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

The experimental realization of increasingly complex synthetic quantum systems calls for the
development of general theoretical methods to validate and fully exploit quantum resources …

Entanglement is a key feature of many-body quantum systems. Measuring the entropy of
different partitions of a quantum system provides a way to probe its entanglement structure …