Ground state preparation is classically intractable for general Hamiltonians. On quantum
devices, shallow parametrized circuits can be effectively trained to obtain short-range …

Learning probability distribution is an essential framework in classical learning theory. As a
counterpart, quantum state learning has spurred the exploration of quantum machine …

The Hamiltonian of a quantum system is represented in terms of operators corresponding to
the kinetic and potential energies of the system. The expectation value of a Hamiltonian and …

The tradeoff between trainability and expressibility is a central challenge faced by today's
variational quantum computing. Recent studies indicate that resolving this dilemma …

Variational quantum algorithms is one of the most representative algorithms in quantum
computing, which has a wide range of applications in quantum machine learning, quantum …