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

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

Functional Effects of let-7g Expression in Colon Cancer Metastasis.

MicroRNA regulation is crucial for gene expression and cell functions. It has been linked to tumorigenesis, development and metastasis in colorectal cancer (CRC). Recently, the let-7 family has been identified as a tumor suppressor in different types of cancers. However, the function of the let-7 family in CRC metastasis has not been fully investigated. Here, we focused on analyzing the role of let-7g in CRC. The Cancer Genome Atlas (TCGA) genomic datasets of CRC and detailed data from a Taiwanese CRC cohort were applied to study the expression pattern of let-7g. In addition, in vitro as well as in vivo studies have been performed to uncover the effects of let-7g on CRC. We found that the expression of let-7g was significantly lower in CRC specimens. Our results further supported the inhibitory effects of let-7g on CRC cell migration, invasion and extracellular calcium influx through store-operated calcium channels. We report a critical role for let-7g in the pathogenesis of CRC and suggest let-7g as a potential therapeutic target for CRC treatment

A lack of association between genetic polymorphisms in beta-defensins and susceptibility of psoriasis in Taiwanese: A case–control study

AbstractBackgroundGenetic predisposition of the inflammatory-host response may affect the development of psoriasis. Previous studies have shown that copy number variations (CNVs) of β-defensin genes (DEFB) are associated with the susceptibility of psoriasis in Caucasian populations.ObjectivesThis study aimed to assess the role of the CNVs of the DEFB4 gene and functional variants in the DEFB1 gene in Taiwanese patients with psoriasis.MethodsIn total, 196 patients with psoriasis and 196 control individuals were analyzed for the presence of the DEFB4 CNVs using the paralogue ratio test, and also for the DEFB1 polymorphisms rs11362, rs1800972, and rs1799946, using a polymerase chain reaction.ResultsNone of the polymorphisms were found to be associated with psoriasis. The distribution of DEFB4 genomic CNVs did not significantly differ between the control group and psoriasis group. The frequencies of patients who carried a greater than the median (≥ 5) number of copies did not significantly differ in patients with psoriasis and controls. The multivariate analysis similarly revealed that the DEFB4 CNVs were not associated with psoriasis (odds ratio = 1.03, 95% confidence interval = 0.89–1.19, p = 0.720). No significant difference was detected in the genotype and allele distribution for any of the individual DEFB1 polymorphisms between the cases and the controls. Finally, the overall haplotype frequency profiles derived from the three polymorphisms did not significantly differ between the cases and the controls.ConclusionOur results do not suggest that these genetic variants of the β-defensin genes contribute to the genetic background of psoriasis in Taiwanese patients

A 64-week, multicenter, open-label study of aripiprazole effectiveness in the management of patients with schizophrenia or schizoaffective disorder in a general psychiatric outpatient setting

<p>Abstract</p> <p>Objective</p> <p>To evaluate the overall long-term effectiveness of aripiprazole in patients with schizophrenia in a general psychiatric practice setting in Taiwan.</p> <p>Methods</p> <p>This was a prospective, open-label, multicenter, post-market surveillance study in Taiwanese patients with a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnosis of schizophrenia or schizoaffective disorder requiring a switch in antipsychotic medication because current medication was not well tolerated and/or clinical symptoms were not well controlled. Eligible patients were titrated to aripiprazole (5-30 mg/day) over a 12-week switching phase, during which their previous medication was discontinued. Patients could then enter a 52-week, long-term treatment phase. Aripiprazole was flexibly dosed (5-30 mg/day) at the discretion of the treating physicians. Efficacy was assessed using the Clinical Global Impression scale Improvement (CGI-I) score, the Clinical Global Impression scale Severity (CGI-S) score, The Brief Psychiatry Rating Scale (BPRS), and the Quality of Life (QOL) scale, as well as Preference of Medicine (POM) ratings by patients and caregivers. Safety and tolerability were also assessed.</p> <p>Results</p> <p>A total of 245 patients were enrolled and switched from their prior antipsychotic medications, and 153 patients entered the 52-week extension phase. In all, 79 patients (32.2%) completed the study. At week 64, the mean CGI-I score was 3.10 and 64.6% of patients who showed response. Compared to baseline, scores of CGI-S, QOL, and BPRS after 64 weeks of treatment also showed significant improvements. At week 12, 65.4% of subjects and 58.9% of caregivers rated aripiprazole as better than the prestudy medication on the POM. The most frequently reported adverse events (AEs) were headache, auditory hallucinations and insomnia. A total of 13 patients (5.3%) discontinued treatment due to AEs. No statistically significant changes were noted with respect to fasting plasma glucose, lipid profile, body weight, and body mass index after long-term treatment with aripiprazole.</p> <p>Conclusions</p> <p>Although the discontinuation rate was high, aripiprazole was found to be effective, safe and well tolerated in the long-term treatment of Taiwanese patients with schizophrenia who continued to receive treatment for 64 weeks.</p

Numerical Study of Thermal and Flow Characteristics of Plate-Fin Heat Sink with Longitudinal Vortex Generator Installed on the Ground

This study applied the commercial software ANSYS CFD (FLUENT), for simulating the transient flow field and investigating the influence of each parameter of longitudinal vortex generators (LVGs) on the thermal flux of a plate-fin heat sink. Vortex generator was set in front of plate-fin heat sink and under the channel, which was in common-flow-down (CFD) and common-flow-up (CFU) conditions, which have the result of vortex generator of delta winglet pair (DWP). In this study the parameters were varied, such as the minimum transverse distance between winglet pair, the attack angle of the vortex generator, fins number, the fin height, and the distance between the vortex generator and plate-fin. The coolant fluid flew into the fin-to-fin channel and pushed the vortex from different geometry toward the bottom. This phenomenon took off the heat from the plate to enhance the heat transfer. The numerical results indicated that the LVGs located close to the plate-fin heat sink are zero with the attack angle being 30°, presenting optimal overall conditions