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

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 …

This review aims at gathering the most relevant quantum multi-parameter estimation
methods that go beyond the direct use of the quantum Fisher information concept. We …

Quantum tomography is currently ubiquitous for testing any implementation of a quantum
information processing device. Various sophisticated procedures for state and process …

We introduce a new method called rejection filtering that we use to perform adaptive
Bayesian phase estimation. Our approach has several advantages: it is classically efficient …

Over the past few years, machine learning has emerged as a powerful computational tool to
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …

Bayesian inference is a powerful paradigm for quantum state tomography, treating
uncertainty in meaningful and informative ways. Yet the numerical challenges associated …

Quantum process tomography is a powerful tool for understanding quantum channels and
characterizing the properties of quantum devices. Inspired by recent advances using …

Projected least squares is an intuitive and numerically cheap technique for quantum state
tomography: compute the least-squares estimator and project it onto the space of states. The …

The exponential growth in Hilbert space with increasing size of a quantum system means
that accurately characterizing the system becomes significantly harder with system …