Optineurin regulates osteoblastogenesis through STAT1

A sophisticated and delicate balance between bone resorption by osteoclasts and bone formation by osteoblasts regulates bone metabolism. Optineurin (OPTN) is a gene involved in primary open-angle glaucoma and amyotrophic lateral sclerosis. Although its function has been widely studied in ophthalmology and neurology, recent reports have shown its possible involvement in bone metabolism through negative regulation of osteoclast differentiation. However, little is known about the role of OPTN in osteoblast function. Here, we demonstrated that OPTN controls not only osteoclast but also osteoblast differentiation. Different parameters involved in osteoblastogenesis and osteoclastogenesis were assessed in Optn−/- mice. The results showed that osteoblasts from Optn−/- mice had impaired alkaline phosphatase activity, defective mineralized nodules, and inability to support osteoclast differentiation. Moreover, OPTN could bind to signal transducer and activator of transcription 1 (STAT1) and regulate runt-related transcription factor 2 (RUNX2) nuclear localization by modulating STAT1 levels in osteoblasts. These data suggest that OPTN is involved in bone metabolism not only by regulating osteoclast function but also by regulating osteoblast function by mediating RUNX2 nuclear translocation via STAT1

Hydrodynamic and Energy Transport Model-Based Hot-Carrier Effect in GaAs <i>pin</i> Solar Cell

The hot-carrier effect and hot-carrier dynamics in GaAs solar cell device performance were investigated. Hot-carrier solar cells based on the conventional operation principle were simulated based on the detailed balance thermodynamic model and the hydrodynamic energy transportation model. A quasi-equivalence between these two models was demonstrated for the first time. In the simulation, a specially designed GaAs solar cell was used, and an increase in the open-circuit voltage was observed by increasing the hot-carrier energy relaxation time. A detailed analysis was presented regarding the spatial distribution of hot-carrier temperature and its interplay with the electric field and three hot-carrier recombination processes: Auger, Shockley–Read–Hall, and radiative recombinations

Quantum-Inspired Classification Algorithm from DBSCAN&ndash;Deutsch&ndash;Jozsa Support Vectors and Ising Prediction Model

Quantum computing is suggested as a new tool to deal with large data set for machine learning applications. However, many quantum algorithms are too expensive to fit into the small-scale quantum hardware available today and the loading of big classical data into small quantum memory is still an unsolved obstacle. These difficulties lead to the study of quantum-inspired techniques using classical computation. In this work, we propose a new classification method based on support vectors from a DBSCAN&ndash;Deutsch&ndash;Jozsa ranking and an Ising prediction model. The proposed algorithm has an advantage over standard classical SVM in the scaling with respect to the number of training data at the training phase. The method can be executed in a pure classical computer and can be accelerated in a hybrid quantum&ndash;classical computing environment. We demonstrate the applicability of the proposed algorithm with simulations and theory

General Vapnik–Chervonenkis dimension bounds for quantum circuit learning

Quantifying the model complexity of quantum circuits provides a guide to avoid overfitting in quantum machine learning. Previously we established a Vapnik–Chervonenkis (VC) dimension upper bound for ‘encoding-first’ quantum circuits, where the input layer is the first layer of the circuit. In this work, we prove a general VC dimension upper bound for quantum circuit learning including ‘data re-uploading’ circuits, where the input gates can be single qubit rotations anywhere in the circuit. A linear lower bound is also constructed. The properties of the bounds and approximation-estimation trade-off considerations are discussed

Model-Free Deep Recurrent Q-Network Reinforcement Learning for Quantum Circuit Architectures Design

Artificial intelligence (AI) technology leads to new insights into the manipulation of quantum systems in the Noisy Intermediate-Scale Quantum (NISQ) era. Classical agent-based artificial intelligence algorithms provide a framework for the design or control of quantum systems. Traditional reinforcement learning methods are designed for the Markov Decision Process (MDP) and, hence, have difficulty in dealing with partially observable or quantum observable decision processes. Due to the difficulty of building or inferring a model of a specified quantum system, a model-free-based control approach is more practical and feasible than its counterpart of a model-based approach. In this work, we apply a model-free deep recurrent Q-network (DRQN) reinforcement learning method for qubit-based quantum circuit architecture design problems. This paper is the first attempt to solve the quantum circuit design problem from the recurrent reinforcement learning algorithm, while using discrete policy. Simulation results suggest that our long short-term memory (LSTM)-based DRQN method is able to learn quantum circuits for entangled Bell–Greenberger–Horne–Zeilinger (Bell–GHZ) states. However, since we also observe unstable learning curves in experiments, suggesting that the DRQN could be a promising method for AI-based quantum circuit design application, more investigation on the stability issue would be required