66 research outputs found
Quantum deep Q learning with distributed prioritized experience replay
This paper introduces the QDQN-DPER framework to enhance the efficiency of
quantum reinforcement learning (QRL) in solving sequential decision tasks. The
framework incorporates prioritized experience replay and asynchronous training
into the training algorithm to reduce the high sampling complexities. Numerical
simulations demonstrate that QDQN-DPER outperforms the baseline distributed
quantum Q learning with the same model architecture. The proposed framework
holds potential for more complex tasks while maintaining training efficiency
Efficient quantum recurrent reinforcement learning via quantum reservoir computing
Quantum reinforcement learning (QRL) has emerged as a framework to solve
sequential decision-making tasks, showcasing empirical quantum advantages. A
notable development is through quantum recurrent neural networks (QRNNs) for
memory-intensive tasks such as partially observable environments. However, QRL
models incorporating QRNN encounter challenges such as inefficient training of
QRL with QRNN, given that the computation of gradients in QRNN is both
computationally expensive and time-consuming. This work presents a novel
approach to address this challenge by constructing QRL agents utilizing
QRNN-based reservoirs, specifically employing quantum long short-term memory
(QLSTM). QLSTM parameters are randomly initialized and fixed without training.
The model is trained using the asynchronous advantage actor-aritic (A3C)
algorithm. Through numerical simulations, we validate the efficacy of our
QLSTM-Reservoir RL framework. Its performance is assessed on standard
benchmarks, demonstrating comparable results to a fully trained QLSTM RL model
with identical architecture and training settings
Foundations of Quantum Federated Learning Over Classical and Quantum Networks
Quantum federated learning (QFL) is a novel framework that integrates the
advantages of classical federated learning (FL) with the computational power of
quantum technologies. This includes quantum computing and quantum machine
learning (QML), enabling QFL to handle high-dimensional complex data. QFL can
be deployed over both classical and quantum communication networks in order to
benefit from information-theoretic security levels surpassing traditional FL
frameworks. In this paper, we provide the first comprehensive investigation of
the challenges and opportunities of QFL. We particularly examine the key
components of QFL and identify the unique challenges that arise when deploying
it over both classical and quantum networks. We then develop novel solutions
and articulate promising research directions that can help address the
identified challenges. We also provide actionable recommendations to advance
the practical realization of QFL.Comment: 7 pages, 2 figures, 2 table
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