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

Statistical mechanics provides a framework for describing the physics of large, complex
many-body systems using only a few macroscopic parameters to determine the state of the …

The efficient control of a large number of qubits is one of the most challenging aspects for
practical quantum computing. Current approaches in solid-state quantum technology are …

In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …

In recent years the dramatic progress in machine learning has begun to impact many areas
of science and technology significantly. In the present perspective article, we explore how …

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 …

Estimation of physical quantities is at the core of most scientific research, and the use of
quantum devices promises to enhance its performances. In real scenarios, it is fundamental …

Realizing the full potential of quantum technologies requires precise real-time control on
time scales much shorter than the coherence time. Model-free reinforcement learning …

Current automated systems have crucial limitations that need to be addressed before
artificial intelligence can reach human-like levels and bring new technological revolutions …

Recurrent neural networks (RNNs), originally developed for natural language processing,
hold great promise for accurately describing strongly correlated quantum many-body …