Boosting computational power through spatial multiplexing in quantum reservoir computing

Quantum reservoir computing provides a framework for exploiting the natural dynamics of quantum systems as a computational resource. It can implement real-time signal processing and solve temporal machine learning problems in general, which requires memory and nonlinear mapping of the recent input stream using the quantum dynamics in computational supremacy region, where the classical simulation of the system is intractable. A nuclear magnetic resonance spin-ensemble system is one of the realistic candidates for such physical implementations, which is currently available in laboratories. In this paper, considering these realistic experimental constraints for implementing the framework, we introduce a scheme, which we call a spatial multiplexing technique, to effectively boost the computational power of the platform. This technique exploits disjoint dynamics, which originate from multiple different quantum systems driven by common input streams in parallel. Accordingly, unlike designing a single large quantum system to increase the number of qubits for computational nodes, it is possible to prepare a huge number of qubits from multiple but small quantum systems, which are operationally easy to handle in laboratory experiments. We numerically demonstrate the effectiveness of the technique using several benchmark tasks and quantitatively investigate its specifications, range of validity, and limitations in detail.Comment: 15 page

Analyzing the impact of labor market integration

We develop a competitive search model involving multiple regions, geographically mobile workers, and moving costs. Equilibrium mobility patterns are analyzed and characterized, indicating that shocks to a particular region, such as a productivity shock, can propagate to other regions through workers' mobility. Moreover, equilibrium mobility patterns are not efficient due to the existence of moving costs, implying that they affect social welfare not only because they are costs but also because they distort equilibrium allocation. By calibrating our framework to Japanese regional data, we demonstrate that the impacts of eliminating migration costs are comparable to those of a 30% productivity increase

Efficient Optical Modulation of Terahertz Transmission in Organic and Inorganic Semiconductor Hybrid System for Printed Terahertz Electronics and Photonics

Highly efficient optical modulation of terahertz (THz) transmission through Si substrate coated with thin layer of organic π-conjugated materials was investigated under various laser light irradiation conditions using THz time-domain spectroscopy. As in the pioneering work by Yoo et al. [Yoo et al., Applied Physics Letters. 2013;103:151116-1–151116-3.], we also used copper phthalocyanine (CuPc). It was perceived that the charge carrier transfer from Si to CuPc is crucial for the photo-induced metallization and efficient optical modulation of THz transmission. We found that the thickness of CuPc layer is a critical parameter to realize high charge carrier density for efficient THz transmission modulation. We also fabricated a split-ring resonator (SRR) array metamaterial on CuPc-coated Si utilizing superfine inkjet printer and succeeded in obtaining efficient modulation of resonant responses of SRR array metamaterials by laser light irradiation. We have further investigated THz transmission modulation through Si substrates coated with another four solution-processable π-conjugated materials. Two of them are π-conjugated low molecules such as the [6,6]-phenyl-C61-butyric acid methyl ester (PCBM) and 6,13-bis(triisopropylsilylethynyl) pentacene (TIPS-pentacene), and another two are the π-conjugated polymer materials such as poly[5-(2-ethylhexyloxy)-2-methoxycyanoterephthalyliden] (MEH-CN-PPV) and poly(benzimidazobenzophenanthroline) (BBL). Among these four π-conjugated materials, PCBM- and TIPS-pentacene showed better modulation efficiencies even higher than CuPc. Our findings may open the way to fabricating various types of THz active devices utilizing printing technologies

Treatment effects on neurometabolite levels in schizophrenia: A meta-analysis dataset of proton magnetic resonance spectroscopy

This article describes a dataset for a meta-analysis that aimed to investigate the effects of treatment on the neurometabolite status in patients with schizophrenia (DOI of original article: https://doi.org/10.1016/j.schres.2020.03.069 [1]). The data search was performed with MEDLINE, Embase, and PsycINFO. The neurometabolites investigated include glutamate, glutamine, glutamate + glutamine, gamma-aminobutyric acid, N-acetylaspartate, and myo-inositol, and the regions of interest (ROIs) include the frontal cortex, temporal cortex, parieto-occipital cortex, thalamus, basal ganglia, and hippocampus. The meta-analysis was conducted with a random-effects model, and the use of the standardized mean difference method between pre- and post-treatment of subjects for neurometabolites in each ROI of three patient groups or more. The dataset covers raw data of 39 patient groups (773 patients with schizophrenia at follow-up) with neurometabolite levels measured by magnetic resonance spectroscopy both before and after treatment. Furthermore, it contains details of clinical characteristics and treatment types for each group. Therefore, the data would be useful for a reinvestigation of treatment effects on the neurometabolite status from diverse points of view, as well as for the development of future treatment strategies for psychiatric diseases