Randomized measurement protocols such as classical shadows represent powerful
resources for quantum technologies, with applications ranging from quantum state …

Recent developments have led to the possibility of embedding machine learning tools into
experimental platforms to address key problems, including the characterization of the …

In quantum information theory, the accurate estimation of observables is pivotal for quantum
information processing, playing a crucial role in computational and communication …

In recent years, informationally complete measurements have attracted considerable
attention, especially in the context of classical shadows. In the particular case of …

Classical shadow tomography, harnessing randomized informationally complete (IC)
measurements, provides an effective avenue for predicting many properties of unknown …

Quantum machine learning algorithms are expected to play a pivotal role in quantum
chemistry simulations in the immediate future. One such key application is the training of a …

We propose a method to provide unbiased estimators of multiple observables with low
statistical error by utilizing informationally (over) complete measurements and tensor …

Characterization of quantum states is a fundamental requirement in quantum science and
technology. As a promising framework, shadow tomography shows significant efficiency in …

Several proposals aiming to demonstrate quantum advantage on near-term quantum
computers rely on the optimization of variational circuits. These approaches include, for …

Quantum extreme learning machines (QELMs) leverage untrained quantum dynamics to
efficiently process information encoded in input quantum states, avoiding the high …