The variational quantum eigensolver (or VQE), first developed by Peruzzo et al.(2014), has
received significant attention from the research community in recent years. It uses the …

Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …

With the increased focus on quantum circuit learning for near-term applications on quantum
devices, in conjunction with unique challenges presented by cost function landscapes of …

We introduce a general method for building neural networks on quantum computers. The
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …

We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …

A large amount of effort has recently been put into understanding the barren plateau
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …

Quantum Neural Networks (QNNs) are a promising variational learning paradigm with
applications to near-term quantum processors, however they still face some significant …