FV-Train: Quantum Convolutional Neural Network Training with a Finite Number of Qubits by Extracting Diverse Features

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).Comment: 2 pages, 3 figure

Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare

The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information. Although NLP techniques have exhibited substantial potential in augmenting patient care and informing clinical decision-making, data privacy and adherence to regulations persist as critical concerns. Federated learning (FL) emerges as a viable solution, empowering multiple organizations to train machine learning models collaboratively without disseminating raw data. This paper proffers a pragmatic approach to medical NLP by amalgamating FL, NLP models, and the NVFlare framework, developed by NVIDIA. We introduce two exemplary NLP models, the Long-Short Term Memory (LSTM)-based model and Bidirectional Encoder Representations from Transformers (BERT), which have demonstrated exceptional performance in comprehending context and semantics within medical data. This paper encompasses the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and performance, incorporating BERT pretraining, and comprehensively substantiating the efficacy of the proposed approach

Scalable Quantum Convolutional Neural Networks

With the beginning of the noisy intermediate-scale quantum (NISQ) era, quantum neural network (QNN) has recently emerged as a solution for the problems that classical neural networks cannot solve. Moreover, QCNN is attracting attention as the next generation of QNN because it can process high-dimensional vector input. However, due to the nature of quantum computing, it is difficult for the classical QCNN to extract a sufficient number of features. Motivated by this, we propose a new version of QCNN, named scalable quantum convolutional neural network (sQCNN). In addition, using the fidelity of QC, we propose an sQCNN training algorithm named reverse fidelity training (RF-Train) that maximizes the performance of sQCNN

Quantum Split Neural Network Learning using Cross-Channel Pooling

In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing. Among these emerging areas, quantum federated learning (QFL) has gained particular attention due to the integration of quantum neural networks (QNNs) with traditional federated learning (FL) techniques. In this study, a novel approach entitled quantum split learning (QSL) is presented, which represents an advanced extension of classical split learning. Previous research in classical computing has demonstrated numerous advantages of split learning, such as accelerated convergence, reduced communication costs, and enhanced privacy protection. To maximize the potential of QSL, cross-channel pooling is introduced, a technique that capitalizes on the distinctive properties of quantum state tomography facilitated by QNNs. Through rigorous numerical analysis, evidence is provided that QSL not only achieves a 1.64\% higher top-1 accuracy compared to QFL but also demonstrates robust privacy preservation in the context of the MNIST classification task

Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications

This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications. For the purpose, a single neural network is utilized in centralized training for cooperation among multiple agents while maximizing the total quality of service (QoS) in mobile access applications.Comment: 2 pages, 4 figure

Visual Simulation Software Demonstration for Quantum Multi-Drone Reinforcement Learning

Quantum computing (QC) has received a lot of attention according to its light training parameter numbers and computational speeds by qubits. Moreover, various researchers have tried to enable quantum machine learning (QML) using QC, where there are also multifarious efforts to use QC to implement quantum multi-agent reinforcement learning (QMARL). Existing classical multi-agent reinforcement learning (MARL) using neural network features non-stationarity and uncertain properties due to its large number of parameters. Therefore, this paper presents a visual simulation software framework for a novel QMARL algorithm to control autonomous multi-drone systems to take advantage of QC. Our proposed QMARL framework accomplishes reasonable reward convergence and service quality performance with fewer trainable parameters than the classical MARL. Furthermore, QMARL shows more stable training results than existing MARL algorithms. Lastly, our proposed visual simulation software allows us to analyze the agents' training process and results.Comment: 5 pages, 4 figure

Arrhythmia surgery for atrial fibrillation associated with atrial septal defect: Right-sided maze versus biatrial maze

BackgroundAlthough it has been inferred that a biatrial maze procedure for atrial fibrillation in left-sided heart lesions may lead to better outcomes compared with a limited left atrial lesion set, it remains controversial whether the biatrial maze procedure is superior to the right atrial maze procedure in right-sided heart lesions.MethodsA retrospective review was performed for 56 adults who underwent surgical closure of atrial septal defect and various maze procedures for atrial fibrillation between June 1998 and February 2011. The median age at operation was 59 years (range, 34-79 years). Clinical manifestations of atrial fibrillation were paroxysmal in 8 patients, persistent in 15 patients, and long-standing persistent in 33 patients. A right atrial maze procedure was performed in 23 patients (group 1), and a biatrial maze procedure was performed in 33 patients (group 2). Treatment failure was defined as atrial fibrillation recurrence, development of atrial flutter or other types of atrial tachyarrhythmia, or implantation of a permanent pacemaker. The Cox proportional hazards model was used to identify risk factors for decreased time to treatment failure.ResultsDuring the median follow-up period of 49 months (range, 5-149 months), there was no early death and 1 late noncardiac death. On Cox survival model, group 1 showed a significantly decreased time to treatment failure in comparison with group 2 (hazard ratio, 5.11; 95% confidence interval, 1.59-16.44; P = .006). Maintenance of normal sinus rhythm without any episode of atrial fibrillation recurrence at 2 and 5 years postoperatively was 57% and 45% in group 1, respectively, and 82% and 69% in group 2, respectively.ConclusionsLeft-sided ablation in addition to a right atrial maze procedure leads to better electrophysiologic outcome in atrial fibrillation associated with atrial septal defect

Coprinus comatus Cap Inhibits Adipocyte Differentiation via Regulation of PPARγ and Akt Signaling Pathway

This study assessed the effects of Coprinus comatus cap (CCC) on adipogenesis in 3T3-L1 adipocytes and the effects of CCC on the development of diet-induced obesity in rats. Here, we showed that the CCC has an inhibitory effect on the adipocyte differentiation of 3T3-L1 cells, resulting in a significant decrease in lipid accumulation through the downregulation of several adipocyte specific-transcription factors, including CCAAT/enhancer binding protein β, C/EBPδ, and peroxisome proliferator-activated receptor gamma (PPARγ). Moreover, treatment with CCC during adipocyte differentiation induced a significant down-regulation of PPARγ and adipogenic target genes, including adipocyte protein 2, lipoprotein lipase, and adiponectin. Interestingly, the CCC treatment of the 3T3-L1 adipocytes suppressed the insulin-stimulated Akt and GSK3β phosphorylation, and these effects were stronger in the presence of an inhibitor of Akt phosphorylation, LY294002, suggesting that CCC inhibited adipocyte differentiation through the down-regulation of Akt signaling. In the animal study, CCC administration significantly reduced the body weight and adipose tissue weight of rats fed a high fat diet (HFD) and attenuated lipid accumulation in the adipose tissues of the HFD-induced obese rats. The size of the adipocyte in the epididymal fat of the CCC fed rats was significantly smaller than in the HFD rats. CCC treatment significantly reduced the total cholesterol and triglyceride levels in the serum of HFD rats. These results strongly indicated that the CCC-mediated decrease in body weight was due to a reduction in adipose tissue mass. The expression level of PPARγ and phospho-Akt was significantly lower in the CCC-treated HFD rats than that in the HFD obesity rats. These results suggested that CCC inhibited adipocyte differentiation by the down-regulation of major transcription factor involved in the adipogenesis pathway including PPARγ through the regulation of the Akt pathway in 3T3-L1 cells and HFD adipose tissue