Variational quantum computing offers a flexible computational paradigm with applications in
diverse areas. However, a key obstacle to realizing their potential is the Barren Plateau (BP) …

We analyze the barren plateau phenomenon in the variational optimization of quantum
circuits inspired by matrix product states (qMPS), tree tensor networks (qTTN), and the …

Once developed for quantum theory, tensor networks (TNs) have been established as a
successful machine learning (ML) paradigm. Now, they have been ported back to the …

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

Entropy measures quantify the amount of information and correlation present in a quantum
system. In practice, when the quantum state is unknown and only copies thereof are …

The barren plateau problem in quantum neural networks (QNNs) is a significant challenge
that hinders the practical success of QNNs. In this paper, we introduce residual quantum …

Variational quantum algorithms (VQAs) hold great potential for near-term applications and
are promising to achieve quantum advantage in practical tasks. However, VQAs suffer from …

Barren plateaus appear to be a major obstacle for using variational quantum algorithms to
simulate large-scale quantum systems or to replace traditional machine learning algorithms …

Parameterized quantum circuits used as variational ansatzes are emerging as promising
tools to tackle complex problems ranging from quantum chemistry to combinatorial …

One impediment to the useful application of variational quantum algorithms in quantum
chemistry is slow convergence with large numbers of classical optimization parameters. In …