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 …

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

In experimentally realistic situations, quantum systems are never perfectly isolated and the
coupling to their environment needs to be taken into account. Often, the effect of the …

We present an efficient machine-learning (ML) algorithm for predicting any unknown
quantum process E over n qubits. For a wide range of distributions D on arbitrary n-qubit …

Abstract The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in
recent times as they may lead to real-world applications of near-term quantum devices …

Long short-term memory (LSTM) is a kind of recurrent neural networks (RNN) for sequence
and temporal dependency data modeling and its effectiveness has been extensively …

Model bias is an inherent limitation of the current dominant approach to optimal quantum
control, which relies on a system simulation for optimization of control policies. To overcome …

A key challenge in the effort to simulate today's quantum computing devices is the ability to
learn and encode the complex correlations that occur between qubits. Emerging …

Machine learning methods have proved to be useful for the recognition of patterns in
statistical data. The measurement outcomes are intrinsically random in quantum physics …

We perform quantum process tomography (QPT) for both discrete-and continuous-variable
quantum systems by learning a process representation using Kraus operators. The Kraus …