Quantum machine learning, which involves running machine learning algorithms on
quantum devices, has garnered significant attention in both academic and business circles …

A bstract Krylov complexity measures the spread of the wavefunction in the Krylov basis,
which is constructed using the Hamiltonian and an initial state. We investigate the evolution …

We report universal statistical properties displayed by ensembles of pure states that
naturally emerge in quantum many-body systems. Specifically, two classes of state …

In finite entropy systems, real-time partition functions do not decay to zero at late time.
Instead, assuming random matrix universality, suitable averages exhibit a growing" ramp" …

Parametrized quantum circuits can be used as quantum neural networks and have the
potential to outperform their classical counterparts when trained for addressing learning …

A bstract We study a notion of operator growth known as Krylov complexity in free and
interacting massive scalar quantum field theories in d-dimensions at finite temperature. We …

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) …

We propose a measure, which we call the dissipative spectral form factor (DSFF), to
characterize the spectral statistics of non-Hermitian (and nonunitary) matrices. We show that …

A bstract A number of recent works have argued that quantum complexity, a well-known
concept in computer science that has re-emerged recently in the context of the physics of …

A long period of linear growth in the spectral form factor provides a universal diagnostic of
quantum chaos at intermediate times. By contrast, the behavior of the spectral form factor in …