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

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 …

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 …

While quantum state tomography is notoriously hard, most states hold little interest to
practically minded tomographers. Given that states and unitaries appearing in nature are of …

The spectral form factor (SFF), characterizing statistics of energy eigenvalues, is a key
diagnostic of many-body quantum chaos. In addition, partial spectral form factors (PSFFs) …

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 …

Characterization protocols have so far played a central role in the development of noisy
intermediate-scale quantum (NISQ) computers capable of impressive quantum feats. This …

We propose a tensor network approach known as the locally purified density operator
(LPDO) to investigate the classification and characterization of symmetry-protected …

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

In the era of noisy intermediate-scale quantum devices, the number of controllable hardware
qubits is insufficient to implement quantum error correction. As an alternative, quantum error …