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 …

Variational quantum algorithm (VQA) derives advantages from its error resilience and high
flexibility in quantum resource requirements, rendering it broadly applicable in the noisy …

Hyperparameter optimization (HPO) and neural architecture search (NAS) of machine
learning (ML) models are in the core implementation steps of AI-enabled systems. With multi …

Automatic quantum architecture search (QAS) has been widely studied across disciplines
with different implications. In this paper, beyond a particular domain, we formulate the QAS …

The performance of the variational quantum algorithm (VQA) highly depends on the
structure of the quantum circuit. Quantum architecture search (QAS) algorithm aims to …

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

Quantum algorithms based on variational approaches are one of the most promising
methods to construct quantum solutions and have found a myriad of applications in the last …

Variational quantum algorithms, inspired by neural networks, have become a novel
approach in quantum computing. However, designing efficient parameterized quantum …

Quantum computing has made significant progress in recent years, attracting immense
interest not only in research laboratories but also in various industries. However, the …

The emergence of quantum reinforcement learning (QRL) is propelled by advancements in
quantum computing (QC) and machine learning (ML), particularly through quantum neural …