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

Control and learning are key to technological advancement, both in the classical and
quantum domains, yet their interrelationship is insufficiently clear in the literature, especially …

Understanding the power and limitations of quantum access to data in machine learning
tasks is primordial to assess the potential of quantum computing in artificial intelligence …

Parametrised quantum circuits offer expressive and data-efficient representations for
machine learning. Due to quantum states residing in a high-dimensional complex Hilbert …

This study proposes a QRA approach for designing optimal Quantum Sensor Circuits
(QSCs) to address complex quantum physics problems. The QRA generates QSCs by …

Quantum Computing aims to streamline machine learning, making it more effective with
fewer trainable parameters. This reduction of parameters can speed up the learning process …

This research delves into the role of the quantum Fisher Information Matrix (FIM) in
enhancing the performance of Parameterized Quantum Circuit (PQC)-based reinforcement …

This research explores the trainability of Parameterized Quantum circuit-based policies in
Reinforcement Learning, an area that has recently seen a surge in empirical exploration …

This research addresses the trainability of Parameterized Quantum Circuit-based Softmax
policies in Reinforcement Learning. We assess the trainability of these policies by …

Quantum machine learning (QML) is a promising area of application for near-term quantum
computing devices, with hybrid quantum-classical models based on parameterized quantum …