MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era

We introduce the first large-scale dataset, MNISQ, for both the Quantum and the Classical Machine Learning community during the Noisy Intermediate-Scale Quantum era. MNISQ consists of 4,950,000 data points organized in 9 subdatasets. Building our dataset from the quantum encoding of classical information (e.g., MNIST dataset), we deliver a dataset in a dual form: in quantum form, as circuits, and in classical form, as quantum circuit descriptions (quantum programming language, QASM). In fact, also the Machine Learning research related to quantum computers undertakes a dual challenge: enhancing machine learning exploiting the power of quantum computers, while also leveraging state-of-the-art classical machine learning methodologies to help the advancement of quantum computing. Therefore, we perform circuit classification on our dataset, tackling the task with both quantum and classical models. In the quantum endeavor, we test our circuit dataset with Quantum Kernel methods, and we show excellent results up to $97\%$ accuracy. In the classical world, the underlying quantum mechanical structures within the quantum circuit data are not trivial. Nevertheless, we test our dataset on three classical models: Structured State Space sequence model (S4), Transformer and LSTM. In particular, the S4 model applied on the tokenized QASM sequences reaches an impressive $77\%$ accuracy. These findings illustrate that quantum circuit-related datasets are likely to be quantum advantageous, but also that state-of-the-art machine learning methodologies can competently classify and recognize quantum circuits. We finally entrust the quantum and classical machine learning community the fundamental challenge to build more quantum-classical datasets like ours and to build future benchmarks from our experiments. The dataset is accessible on GitHub and its circuits are easily run in qulacs or qiskit.Comment: Preprint. Under revie

Usefulness of peripherally inserted central catheters

Introduction : Central venous catheter (CVC) use is essential for treating esophageal cancer. Peripherally inserted central catheters (PICC) are commonly used recently for improved patient comfort and safety. We compared centrally inserted central catheters (CICC) and PICC insertions and examined their safety. Methods : We retrospectively investigated complications at the catheter insertion and post-insertion for 199 patients’ esophageal cancer treatment (CICC : 45, PICC : 154) from 2013 to 2018. In addition, we summarized the results of catheter tip culture. Results : No serious complications occurred at the catheter insertion in either group. The rate of complications at catheter insertion was 5.8% for PICC and 6.7% for CICC patients. Post-insertion complications were observed in 6.5% and 11.1% of patients with PICC and CICC, respectively, and this difference was not significant. The incidence of catheter-related blood stream infection (CRBSI) was significantly lower in PICC than CICC patients (0.3 vs. 1.8 / 1,000 catheter-days ; p = 0.029). Catheter-related thrombosis was observed in PICC : 0.5 and CICC : 0.6, and occlusion due to blood flow reversal was observed in PICC : 0.5 and CICC : 0.6. Conclusion : PICCs are safer and more effective than CICCs for the treatment of esophageal cancer, and reduce the incidence of CRBSI. We hope to standardize the insertion procedures, conventionalize techniques, and establish training systems

Suicidal ideation and burnout among psychiatric trainees in Japan

AIM: Burnout is a psychological condition that may occur in all workers after being exposed to excessive work-related stresses. We investigated suicidal ideation and burnout among Japanese psychiatric trainees as a part of the Burnout Syndrome Study (BoSS) International.  METHODS: In the Japanese branch, 91 trainees fully completed suicide ideation and behaviour questionnaire (SIBQ) and Maslach Burnout Inventory-General Survey (MBI-GS).  RESULTS: Passive suicidal ideation was reported by 38.5% of Japanese trainees and 22.0% of them had experienced active suicidal ideation. The burnout rate among Japanese subjects was 40.0%. These results were worse compared to the all 1980 trainees who fully completed the main outcome measure in BoSS International, 25.9%, 20.4% and 36.7%, respectively.  CONCLUSIONS: Our results suggest a higher risk of suicide among Japanese residents. Japan has a higher suicide rate than other countries. Early detection of, and appropriate intervention for, suicidal ideation is important in preventing suicide in psychiatry residents

Development of Gas Multiplier Counters (GMCs) Onboard the 6U CubeSat X-Ray Observatory NinjaSat

We report the development of Gas Multiplier Counters (GMCs) onboard the 6U CubeSat X-ray observatory NinjaSat, scheduled to be launched in October 2023. GMC is a 1U-size non-imaging gas X-ray detector sensitive to 2–50 keV X-rays, and two identical GMCs are mounted on NinjaSat. GMC consists of a gas cell filled with a xenon/argon/dimethyl ether (75%/24%/1%) gas mixture with a pressure of 1.2 atm at 0◦C, a high voltage supply and analog signal processing board, a digital signal processing board, an X-ray collimator of a 2.1◦ field of view, and an iron-55 calibration source. The most significant feature of the GMC is its large effective area of 32 cm2 at 6 keV, which is more than two orders of magnitude larger than the X-ray detectors onboard previously launched CubeSats. We have achieved this at a low cost and in a short development time by employing a gas detector that can easily increase its effective area and using a space-proven gas electron multiplier. GMC was characterized with X-rays from an X-ray generator in a laboratory and monochromatic X-rays on the BL-14A beamline at the KEK synchrotron radiation facility. In this paper, we present the design of GMC and the preliminary results of the detector calibration

NinjaSat: 6U CubeSat Observatory for Bright X-Ray Sources

NinjaSat is a 6U CubeSat observatory designed for long-term monitoring of bright X-ray sources, such as binary systems between normal stars and black holes or neutron stars. NinjaSat is the first Japanese CubeSat dedicated to astronomical observation, and it is also a mission to demonstrate that even a small satellite, which can be developed quickly and inexpensively, unlike large satellites, can perform excellent scientific observations. NinjaSat realizes the world’s highest X-ray sensitivity in CubeSat missions by using gas X-ray detectors filling the entire space allocated for science payloads. The fabrication of the flight model payloads began in 2021, and testing at the payload component level was completed in August 2022; as of April 2023, the payloads were integrated into the Nano Avionics 6U bus (M6P) in Lithuania. After four months of testing, the payload will be stored in the Exolaunch deployer in August and launched by the SpaceX Transporter-9 mission in October 2023. This paper will describe the scientific objectives, satellite structure, payloads, and operations of NinjaSat

Development of Radiation Belt Monitors for the 6U CubeSat X-Ray Observatory NinjaSat

NinjaSat is a 6U CubeSat-sized X-ray observatory to be launched into the low Earth orbit at an altitude of 550 km, and is scheduled for launch this October. NinjaSat is equipped with two 1U-sized gas X-ray detectors (GMC) and is expected to operate mainly for astronomical observations of bright X-ray objects in the sky, such as neutron stars and black holes. Since high voltages are applied to the gas cells of GMC, two radiation belt monitors (RBM) will also be installed to protect GMC from electrical discharges potentially caused by excessively high rate of charged particles. NinjaSat RBM will play a fail-safe function in the voltage suppression operation of GMC in the auroral zone and South Atlantic Anomaly, and also protect GMC from charged particles such as protons and electrons that arrive unexpectedly due to solar flares or other low-Earth orbit radiation events. RBM uses a 9 mm x 9 mm Si-PIN photodiode as a charged particle sensor. By taking advantage of the difference in sensor response to protons and electrons, the sensor is designed to simultaneously count charged particle rates at multiple energy thresholds so that GMC protection function will operate even if either the proton or electron rate increases. RBM can count up to about 10 kcps with almost no loss of counts, and proton beam tests have confirmed that the response performance is sufficient to protect GMC against excessively high charged particle rates above 10 Mcps without choking the circuitry. The flight models of the RBM have passed the thermal vacuum and vibration tests last year. The developed RBM occupies only about 6% of the 1U CubeSat size in volume and weighs only 70g. In addition, since the RBM uses inexpensive, commercially available sensors, it could be installed on small satellites other than NinjaSat with relatively small development resources

Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio

There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR

Four distinct network patterns of supramolecular/polymer composite hydrogels controlled by formation kinetics and interfiber interactions

Synthetic hydrogels composed of covalent polymers and supramolecular fibers have been investigated for controlled delivery of biopharmaceuticals, but characterisation of the structures and properties can be challenging. Here, the authors report an imaging study for the composite network, categorizing into four distinct patterns controlled by network formation kinetics and interactions betwee