Variational quantum computing schemes train a loss function by sending an initial state
through a parametrized quantum circuit, and measuring the expectation value of some …

Variational quantum computing schemes have received considerable attention due to their
high versatility and potential to make practical use of near-term quantum devices. At their …

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) …

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 generative models, in providing inherently efficient sampling strategies, show
promise for achieving a near-term advantage on quantum hardware. Nonetheless, important …

Barren plateaus have emerged as a pivotal challenge for variational quantum computing.
Our understanding of this phenomenon underwent a transformative shift with the recent …

In recent proposals of quantum circuit models for generative tasks, the discussion about their
performance has been limited to their ability to reproduce a known target distribution. For …

The training of a parameterized model largely depends on the landscape of the underlying
loss function. In particular, vanishing gradients are a central bottleneck in the scalability of …

We propose an approach for learning probability distributions as differentiable quantum
circuits (DQC) that enable efficient quantum generative modeling (QGM) and synthetic data …

Variational Quantum Algorithms (VQAs) are promising candidates for finding practical
applications of near to mid-term quantum computers. There has been an increasing effort to …