Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized
quantum circuit V (θ) to minimize a cost function C. While VQAs may enable practical …

Quantum neural networks (QNNs) have generated excitement around the possibility of
efficiently analyzing quantum data. But this excitement has been tempered by the existence …

Several architectures have been proposed for quantum neural networks (QNNs), with the
goal of efficiently performing machine learning tasks on quantum data. Rigorous scaling …

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

Variational quantum algorithms (VQAs) are expected to establish valuable applications on
near-term quantum computers. However, recent works have pointed out that the …

We define laziness to describe a large suppression of variational parameter updates for
neural networks, classical or quantum. In the quantum case, the suppression is exponential …

Variational quantum machine learning algorithms have become the focus of recent research
on how to utilize near-term quantum devices for machine learning tasks. They are …

The eigenstate thermalization hypothesis (ETH) represents a cornerstone in the theoretical
understanding of the emergence of thermal behavior in closed quantum systems. The ETH …

Several techniques of generating random quantum channels, which act on the set of d-
dimensional quantum states, are investigated. We present three approaches to the problem …

Many fundamental properties of a quantum system are captured by its Hamiltonian and
ground state. Despite the significance of ground states preparation (GSP), this task is …