Object detection applications, especially in autonomous driving, have drawn attention due to the advancements in deep learning. Additionally, with continuous improvements in classical convolutional neural networks (CNNs), there has been a notable enhancement in both the efficiency and speed of these applications, making autonomous driving more reliable and effective. However, due to the exponentially rapid growth in the complexity and scale of visual signals used in object detection, there are limitations regarding computation speeds while conducting object detection solely with classical computing. Motivated by this, this paper proposes the quantum object detection engine (QODE), which implements a quantum version of CNN, named QCNN, in object detection. Furthermore, this paper proposes a novel fast quantum convolution algorithm that processes the multi-channel of visual signals based on a small number of qubits and constructs the output channel data, thereby achieving relieved computational complexity. Our QODE, equipped with fast quantum convolution, demonstrates feasibility in object detection with multi-channel data, addressing a limitation of current QCNNs due to the scarcity of qubits in the current era of quantum computing. Moreover, this paper introduces a heterogeneous knowledge distillation training algorithm that enhances the performance of our QODE.

Recent advances in deep learning have successfully replaced classical algorithms with machine learning models based on neural networks (NNs). This is particularly prevalent in proactive caching. As NNs grows more capable as their size in terms of storage and computation increases, NN-based proactive caching achieves performance improvement. Nonetheless, there remain challenges in implementing NN-based proactive caching in realistic environments with dynamic user movement. These are due to the fixed structure of NNs that should expand the input size to match the dimensions of the input with the dimensions of the NN’s input units. To address these challenges, this paper proposes a scalable proactive caching framework, named slimmable federated reinforcement learning (SlimFRL). By adopting slimmable neural networks (SNNs) in FRL, our SlimFRL easily adjusts the widths of the SNNs during training according to the number of users. Moreover, due to the scalability of SNNs, our SlimFRL can set the appropriate input dimension while not using imputation, leading to performance improvement. This paper also validates the performance and advantages of SlimFRL in terms of reward and additional cost functions. Additionally, this paper proposes several training algorithms for SlimFRL and corroborates their superiority with convergence analysis and various experiments.

According to the challenges related to the limited availability of quantum bits (qubits) in the era of noisy intermediate-scale quantum (NISQ), the immediate replacement of all components in existing network architectures with quantum computing devices may not be practical. As a result, implementing a hybrid quantum-classical system is regarded as one of effective strategies. In hybrid quantum-classical systems, quantum computing devices can be used for computation-intensive applications, such as massive scheduling in dynamic environments. Furthermore, one of most popular network applications is advanced social media services such as metaverse. Accordingly, this paper proposes an advanced multi-metaverse dynamic streaming algorithm in hybrid quantum-classical systems. For this purpose, the proposed algorithm consists of three stages. For the first stage, three-dimensional (3D) point cloud data gathering should be conducted using spatially scheduled observing devices from physical-spaces for constructing virtual multiple meta-spaces in metaverse server. This is for massive scheduling over dynamic situations, i.e., quantum multi-agent reinforcement learning-based scheduling is utilized for scheduling dimension reduction into a logarithmic-scale. For the second stage, a temporal low-delay metaverse server’s processor scheduler is designed for region-popularity-aware multiple virtual meta-spaces rendering contents allocation via modified bin-packing with hard real-time constraints. Lastly, a novel dynamic dynamic streaming algorithm is proposed for high-quality, differentiated, and stabilized meta-spaces rendering contents delivery to individual users via Lyapunov optimization theory. Our performance evaluation results verify that the proposed spatio-temporal algorithm outperforms benchmarks in various aspects over hybrid quantum-classical systems.

Quantum convolutional neural network (QCNN) has just become as an emerging research topic as we experience the noisy intermediate-scale quantum (NISQ) era and beyond. As convolutional filters in QCNN extract intrinsic feature using quantum-based ansatz, it should use only finite number of qubits to prevent barren plateaus, and it introduces the lack of the feature information. In this paper, we propose a novel QCNN training algorithm to optimize feature extraction while using only a finite number of qubits, which is called fidelity-variation training (FV-Training).

In recent years, quantum neural network (QNN) based on quantum computing has attracted attention due to its potential for computation-acceleration and parallelism. However, the intrinsic limitations of QNN, where the output (i.e., observables) can only be obtained through a measurement process, pose scalability challenges. Motivated by this, this paper aims to address the scalability challenges by incorporating Pauli-Z measurement and Basis measurement. In conventional frameworks, QNN typically relies on classical fully connected networks (FCNs) or increases the number of qubits to achieve large output dimensions. However, by leveraging our proposed framework, this paper successfully expands the output dimensions to an exponential scale, surpassing the limitations imposed by the limited number of qubits without relying on FCNs. Through extensive experiments, this paper demonstrates that the proposed framework outperforms existing QNN frameworks in multi-class classification tasks that require numerous output dimensions.

Recently, research on quantum neural network (QNN) architectures has been attracted in various fields. Among them, the distributed computation of QNN has been actively discussed for privacy-preserving information management due to data and model distribution over multiple computing devices. Based on this concept, this paper proposes quantum split learning (QSL) which splits a single QNN architecture across multiple distributed computing devices to avoid entire QNN architecture exposure. In order to realize QSL design, this paper also proposes cross-channel pooling, which utilizes quantum state tomography. Our evaluation results verifies that QSL preserves privacy in classification tasks and also improves accuracy at most by 6.83% compared to existing methods.

Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices’ distributed computing capabilities. However, applying FL in practice is challenging due to the local devices’ heterogeneous energy, wireless channel conditions, and non-independently and identically distributed (non-IID) data distributions. To cope with these issues, this paper proposes a novel learning framework by integrating FL and width-adjustable slimmable neural networks (SNN). Integrating FL with SNNs is challenging due to time-varying channel conditions and data distributions. In addition, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, which makes SNN ill-suited for FL. Motivated by this, we propose a communication and energy-efficient SNN-based FL (namedSlimFL) that jointly utilizessuperposition coding (SC)for global model aggregation andsuperposition training (ST)for updating local models. By applying SC, SlimFL exchanges the superposition of multiple-width configurations decoded as many times as possible for a given communication throughput. Leveraging ST, SlimFL aligns the forward propagation of different width configurations while avoiding inter-width interference during backpropagation. We formally prove the convergence of SlimFL. The result reveals that SlimFL is not only communication-efficient but also deals with non-IID data distributions and poor channel conditions, which is also corroborated by data-intensive simulations.

This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) architectures. FL preserves data privacy by exchanging the locally trained models of mobile devices. By adopting SNNs as local models, FL can flexibly cope with the time-varying energy capacities of mobile devices. Combining FL and SNNs is however non-trivial, particularly under wireless connections with time-varying channel conditions. Furthermore, existing multi-width SNN training algorithms are sensitive to the data distributions across devices, so are ill-suited to FL. Motivated by this, we propose a communication and energy efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models. By applying SC, SlimFL exchanges the superposition of multiple width configurations that are decoded as many as possible for a given communication throughput. Leveraging ST, SlimFL aligns the forward propagation of different width configurations, while avoiding the inter-width interference during back propagation. We formally prove the convergence of SlimFL. The result reveals that SlimFL is not only communication-efficient but also can counteract non-IID data distributions and poor channel conditions, which is also corroborated by simulations.