With the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era and the fast
development of machine learning, variational quantum algorithms (VQA) including …

Quantum computing is regarded as a promising paradigm that may overcome the current
computational power bottlenecks in the post-Moore era. The increasing maturity of quantum …

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 …

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 …

This paper presents a quantum architecture search (QAS) framework using quantum
reinforcement learning (QRL) to generate quantum gate sequences for multi-qubit GHZ …

The variational quantum algorithms are crucial for the application of NISQ computers. Such
algorithms require short quantum circuits, which are more amenable to implementation on …

The rapid advancement of quantum computing (QC) and machine learning (ML) has given
rise to the burgeoning field of quantum machine learning (QML), aiming to capitalize on the …

Designing performant and noise-robust circuits for Quantum Machine Learning (QML) is
challenging---the design space scales exponentially with circuit size, and there are few well …

Quantum architecture search (QAS) is desired to construct a powerful and general QAS
platform that can significantly accelerate quantum advantages in error-prone and depth …