A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

Decoherence inevitably happens when a quantum state is exposed to its environment,
which can affect quantum critical points (QCPs) in a nontrivial way. As was pointed out in the …

The prospect of achieving quantum advantage with quantum neural networks (QNNs) is
exciting. Understanding how QNN properties (for example, the number of parameters M) …

Variational quantum algorithms are promising algorithms for achieving quantum advantage
on near-term devices. The quantum hardware is used to implement a variational wave …

Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …

Variational quantum algorithms are ubiquitous in applications of noisy intermediate-scale
quantum computers. Due to the structure of conventional parametrized quantum gates, the …

Abstract The Quantum Fisher Information matrix (QFIM) is a central metric in promising
algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary …

We propose a general-purpose, self-adaptive approach to construct a variational wave-
function ansatz for highly accurate quantum dynamics simulations based on McLachlan's …

To harness the potential of noisy intermediate-scale quantum devices, it is paramount to find
the best type of circuits to run hybrid quantum-classical algorithms. Key candidates are …