Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze Problems

Quantum computing holds great potential for advancing the limitations of machine learning algorithms to handle higher dimensions of data and reduce overall training parameters in deep learning (DL) models. This study uses a trainable variational quantum circuit (VQC) on a gate-based quantum computing model to investigate the potential for quantum benefit in a model-free reinforcement learning problem. Through a comprehensive investigation and evaluation of the current model and capabilities of quantum computers, we designed and trained a novel hybrid quantum neural network based on the latest Qiskit and PyTorch framework. We compared its performance with a full-classical CNN with and without an incorporated VQC. Our research provides insights into the potential of deep quantum learning to solve a maze problem and, potentially, other reinforcement learning problems. We conclude that reinforcement learning problems can be practical with reasonable training epochs. Moreover, a comparative study of full-classical and hybrid quantum neural networks is discussed to understand these two approaches' performance, advantages, and disadvantages to deep-Q learning problems, especially on larger-scale maze problems larger than 4x4

Quantum Embedding with Transformer for High-dimensional Data

Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a challenging high-dimensional dataset. The study showcases and analyzes empirical evidence that our transformer-based architecture is a highly versatile and practical approach to modern quantum machine learning problems

Structural study in Highly Compressed BiFeO3 Epitaxial Thin Films on YAlO3

We report a study on the thermodynamic stability and structure analysis of the epitaxial BiFeO3 (BFO) thin films grown on YAlO3 (YAO) substrate. First we observe a phase transition of MC-MA-T occurs in thin sample (<60 nm) with an utter tetragonal-like phase (denoted as MII here) with a large c/a ratio (~1.23). Specifically, MII phase transition process refers to the structural evolution from a monoclinic MC structure at room temperature to a monoclinic MA at higher temperature (150oC) and eventually to a presence of nearly tetragonal structure above 275oC. This phase transition is further confirmed by the piezoforce microscopy measurement, which shows the rotation of polarization axis during the phase transition. A systematic study on structural evolution with thickness to elucidate the impact of strain state is performed. We note that the YAO substrate can serve as a felicitous base for growing T-like BFO because this phase stably exists in very thick film. Thick BFO films grown on YAO substrate exhibit a typical "morphotropic-phase-boundary"-like feature with coexisting multiple phases (MII, MI, and R) and a periodic stripe-like topography. A discrepancy of arrayed stripe morphology in different direction on YAO substrate due to the anisotropic strain suggests a possibility to tune the MPB-like region. Our study provides more insights to understand the strain mediated phase co-existence in multiferroic BFO system.Comment: 18 pages, 6 figures, submitted to Journal of Applied Physic

ELEMENTARY SCHOOL BOYS’ SOCCER KICK SKILL ANALYSIS

The purpose of this study is aimed to analyze elementary school boys’ kicking skills on the perspective of motor skills. The data is collected by Vicon Motion Analysis System (250Hz). The parameters include the compare of the instant joint angles and the time proportion during the process of the kicking toward the different kick performance groups. The participants are 36 elementary boy soccer players (age: 11.7±0.3 yrs; height: 1.42±0.13 m; weight: 37.5±13.0 kg). The subjects were divided to two groups according to the instance kicking ball speed. The result indicated that the high ball speed group players have greater extremity joint angles than the low ball speed group. No difference was found on the time proportion during the process of the kicking. We suggest that the learning of kicking skill can start with the lower speed in the beginner stage

Successful Endoscopic Management of Double Iatrogenic Perforations Induced by Endoscopic Retrograde Cholangiopancreatography and Computed Tomography-Guided Colon Drainage

Endoscopic retrograde cholangiopancreatography (ERCP) is a high-risk procedure with a significantly high rate of complications, such as pancreatitis, bleeding, perforation, and infection. Pancreatitis is the most common post-ERCP complication with an incidence of approximately 3.5%. Although perforation is a rare complication with an incidence of 0.1–0.6%, it may be associated with a high rate of mortality of 1.0–1.5%. Here, we report a rare case of ERCP-induced double iatrogenic perforations in the duodenum and colon complicated by an intra-abdominal abscess. The post-ERCP perforation was successfully sealed using fibrin glue (Tisseel). The intra-abdominal abscess was treated with a computed tomography-guided pigtail drainage; however, the pigtail spontaneously migrated and perforated the ascending colon. The pigtail was removed, and closure of the colon perforation was successfully achieved with endoscopic clipping. Tisseel spray can be a treatment option for post-ERCP perforations. Careful consideration of procedural complications, early detection of perforations, and prompt treatment can be life-saving

Preparing random state for quantum financing with quantum walks

In recent years, there has been an emerging trend of combining two innovations in computer science and physics to achieve better computation capability. Exploring the potential of quantum computation to achieve highly efficient performance in various tasks is a vital development in engineering and a valuable question in sciences, as it has a significant potential to provide exponential speedups for technologically complex problems that are specifically advantageous to quantum computers. However, one key issue in unleashing this potential is constructing an efficient approach to load classical data into quantum states that can be executed by quantum computers or quantum simulators on classical hardware. Therefore, the split-step quantum walks (SSQW) algorithm was proposed to address this limitation. We facilitate SSQW to design parameterized quantum circuits (PQC) that can generate probability distributions and optimize the parameters to achieve the desired distribution using a variational solver. A practical example of implementing SSQW using Qiskit has been released as open-source software. Showing its potential as a promising method for generating desired probability amplitude distributions highlights the potential application of SSQW in option pricing through quantum simulation.Comment: 11 pages, 7 figure

Modulating Microglia/Macrophage Activation by CDNF Promotes Transplantation of Fetal Ventral Mesencephalic Graft Survival and Function in a Hemiparkinsonian Rat Model

Parkinson's disease (PD) is characterized by the loss of dopaminergic neurons in substantia nigra pars compacta, which leads to the motor control deficits. Recently, cell transplantation is a cutting-edge technique for the therapy of PD. Nevertheless, one key bottleneck to realizing such potential is allogenic immune reaction of tissue grafts by recipients. Cerebral dopamine neurotrophic factor (CDNF) was shown to possess immune-modulatory properties that benefit neurodegenerative diseases. We hypothesized that co-administration of CDNF with fetal ventral mesencephalic (VM) tissue can improve the success of VM replacement therapies by attenuating immune responses. Hemiparkinsonian rats were generated by injecting 6-hydroxydopamine (6-OHDA) into the right medial forebrain bundle of Sprague Dawley (SD) rats. The rats were then intrastriatally transplanted with VM tissue from rats, with/without CDNF administration. Recovery of dopaminergic function and survival of the grafts were evaluated using the apomorphine-induced rotation test and smallanimal positron emission tomography (PET) coupled with [F-18] DOPA or [F-18] FE-PE2I, respectively. In addition, transplantation-related inflammatory response was determined by uptake of [F-18] FEPPA in the grafted side of striatum. Immunohistochemistry (IHC) examination was used to determine the survival of the grated dopaminergic neurons in the striatum and to investigate immune-modulatory effects of CDNF. The modulation of inflammatory responses caused by CDNF might involve enhancing M2 subset polarization and increasing fractal dimensions of 6-OHDA-treated BV2 microglial cell line. Analysis of CDNF-induced changes to gene expressions of 6-OHDA-stimulated BV2 cells implies that these alternations of the biomarkers and microglial morphology are implicated in the upregulation of protein kinase B signaling as well as regulation of catalytic, transferase, and protein serine/threonine kinase activity. The effects of CDNF on 6-OHDA-induced alternation of the canonical pathway in BV2 microglial cells is highly associated with PI3K-mediated phagosome formation. Our results are the first to show that CDNF administration enhances the survival of the grafted dopaminergic neurons and improves functional recovery in PD animal model. Modulation of the polarization, morphological characteristics, and transcriptional profiles of 6-OHDA-stimualted microglia by CDNF may possess these properties in transplantation-based regenerative therapies.Peer reviewe