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 …

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 …

Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

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

Recent successes in producing intermediate-scale quantum devices have focused interest
on establishing whether near-term devices could outperform classical computers for …

Quantum variational algorithms are one of the most promising applications of near-term
quantum computers; however, recent studies have demonstrated that unless the variational …

Variational quantum algorithms rely on gradient based optimization to iteratively minimize a
cost function evaluated by measuring output (s) of a quantum processor. A barren plateau is …

Current gate-based quantum computers have the potential to provide a computational
advantage if algorithms use quantum hardware efficiently. To make combinatorial …

Quantum machine learning models have shown successful generalization performance
even when trained with few data. In this work, through systematic randomization …

Waveguide QED simulators are analog quantum simulators made by quantum emitters
interacting with one-dimensional photonic band gap materials. One of their remarkable …