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

The detection of entanglement provides a definitive proof of quantumness. Its ascertainment
might be challenging for hot or macroscopic objects, where entanglement is typically weak …

Quantum reservoir computing (QRC) is a low-complexity learning paradigm that combines
the inherent dynamics of input-driven many-body quantum systems with classical learning …

Quantum state reconstruction is an essential element in quantum information processing.
However, efficient and reliable reconstruction of non-trivial quantum states in the presence …

Quantum machine learning is a rapidly advancing discipline that leverages the features of
quantum mechanics to enhance the performance of computational tasks. Quantum reservoir …

Quantum measurements affect the state of the observed systems via back-action. While
projective measurements extract maximal classical information, they drastically alter the …

Estimating properties of a quantum state is an indispensable task in various applications of
quantum information processing. To predict properties in the postprocessing stage, it is …

Combining the benefits of quantum computing and artificial neural networks, quantum
reservoir computing shows potential for handling complex tasks due to its access to the …

This chapter delves deeply into quantum process tomography (QPT) and regression: two
critical approaches in the field of processing quantum information. The process of …

We propose photonic integrated circuits augmented with a chi (3) nonlinearity--eg, a
semiconductor exciton-polariton nonlinearity--to accomplish two fundamental tasks in …