Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications
The past decade has witnessed significant advancements in quantum hardware,
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
encompassing improvements in speed, qubit quantity, and quantum volume—a metric …
Quantum bayesian optimization
Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent
method for optimizing complicated black-box reward functions. Various BO algorithms have …
method for optimizing complicated black-box reward functions. Various BO algorithms have …
Quantum policy gradient algorithm with optimized action decoding
Quantum machine learning implemented by variational quantum circuits (VQCs) is
considered a promising concept for the noisy intermediate-scale quantum computing era …
considered a promising concept for the noisy intermediate-scale quantum computing era …
Quantum computing through the lens of control: A tutorial introduction
J Berberich, D Fink - IEEE Control Systems, 2024 - ieeexplore.ieee.org
Quantum computing is a fascinating interdisciplinary research field that promises to
revolutionize computing by efficiently solving previously intractable problems. Recent years …
revolutionize computing by efficiently solving previously intractable problems. Recent years …
ResQNets: a residual approach for mitigating barren plateaus in quantum neural networks
M Kashif, S Al-Kuwari - EPJ Quantum Technology, 2024 - epjqt.epj.org
The barren plateau problem in quantum neural networks (QNNs) is a significant challenge
that hinders the practical success of QNNs. In this paper, we introduce residual quantum …
that hinders the practical success of QNNs. In this paper, we introduce residual quantum …
VQC-based reinforcement learning with data re-uploading: performance and trainability
Reinforcement learning (RL) consists of designing agents that make intelligent decisions
without human supervision. When used alongside function approximators such as Neural …
without human supervision. When used alongside function approximators such as Neural …
Asynchronous training of quantum reinforcement learning
SYC Chen - Procedia Computer Science, 2023 - Elsevier
The development of quantum machine learning (QML) has received a lot of interest recently
thanks to developments in both quantum computing (QC) and machine learning (ML). One …
thanks to developments in both quantum computing (QC) and machine learning (ML). One …
Quantum Machine Learning Architecture Search via Deep Reinforcement Learning
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 …
rise to the burgeoning field of quantum machine learning (QML), aiming to capitalize on the …
Improving robustness of quantum feedback control with reinforcement learning
Obtaining reliable state preparation protocols is a key step toward practical implementation
of many quantum technologies, and one of the main tasks in quantum control. In this work …
of many quantum technologies, and one of the main tasks in quantum control. In this work …
Quantum natural policy gradients: Towards sample-efficient reinforcement learning
Reinforcement learning is a growing field in AI with a lot of potential. Intelligent behavior is
learned automatically through trial and error in interaction with the environment. However …
learned automatically through trial and error in interaction with the environment. However …