VQE-generated Quantum Circuit Dataset for Machine Learning

Quantum machine learning has the potential to computationally outperform classical machine learning, but it is not yet clear whether it will actually be valuable for practical problems. While some artificial scenarios have shown that certain quantum machine learning techniques may be advantageous compared to their classical counterpart, it is unlikely that quantum machine learning will outclass traditional methods on popular classical datasets such as MNIST. In contrast, dealing with quantum data, such as quantum states or circuits, may be the task where we can benefit from quantum methods. Therefore, it is important to develop practically meaningful quantum datasets for which we expect quantum methods to be superior. In this paper, we propose a machine learning task that is likely to soon arise in the real world: clustering and classification of quantum circuits. We provide a dataset of quantum circuits optimized by the variational quantum eigensolver. We utilized six common types of Hamiltonians in condensed matter physics, with a range of 4 to 16 qubits, and applied ten different ans\"{a}tze with varying depths (ranging from 3 to 32) to generate a quantum circuit dataset of six distinct classes, each containing 300 samples. We show that this dataset can be easily learned using quantum methods. In particular, we demonstrate a successful classification of our dataset using real 4-qubit devices available through IBMQ. By providing a setting and an elementary dataset where quantum machine learning is expected to be beneficial, we hope to encourage and ease the advancement of the field.Comment: 9 pages, 6figure

A comprehensive survey on quantum computer usage: How many qubits are employed for what purposes?

Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring and simulation of quantum many-body systems. In the last decade, research and development of QCs have rapidly advanced. Now hundreds of physical qubits are at our disposal, and one can find several remarkable experiments actually outperforming the classical computer in a specific computational task. On the other hand, it is unclear what the typical usages of the QCs are. Here we conduct an extensive survey on the papers that are posted in the quant-ph section in arXiv and claim to have used QCs in their abstracts. To understand the current situation of the research and development of the QCs, we evaluated the descriptive statistics about the papers, including the number of qubits employed, QPU vendors, application domains and so on. Our survey shows that the annual number of publications is increasing, and the typical number of qubits employed is about six to ten, growing along with the increase in the quantum volume (QV). Most of the preprints are devoted to applications such as quantum machine learning, condensed matter physics, and quantum chemistry, while quantum error correction and quantum noise mitigation use more qubits than the other topics. These imply that the increase in QV is fundamentally relevant, and more experiments for quantum error correction, and noise mitigation using shallow circuits with more qubits will take place.Comment: 14 pages, 5 figures, figures regenerate

CANGAROO-III observation of TeV gamma rays from the unidentified gamma-ray source HESS J1614-518

We report the detection, with the CANGAROO-III imaging atmospheric Cherenkov telescope array, of a very high energy gamma-ray signal from the unidentified gamma-ray source HESS J1614-518, which was discovered in the H.E.S.S. Galactic plane survey. Diffuse gamma-ray emission was detected above 760 GeV at the 8.9 sigma level during an effective exposure of 54 hr from 2008 May to August. The spectrum can be represented by a power-law: 8.2+-2.2_{stat}+-2.5_{sys}x10^{-12}x (E/1TeV)^{-Gamma} cm^{-2} s^{-1} TeV^{-1} with a photon index Gamma of 2.4+-0.3_{stat}+-0.2_{sys}, which is compatible with that of the H.E.S.S. observations. By combining our result with multi-wavelength data, we discuss the possible counterparts for HESS J1614-518 and consider radiation mechanisms based on hadronic and leptonic processes for a supernova remnant, stellar winds from massive stars, and a pulsar wind nebula. Although a leptonic origin from a pulsar wind nebula driven by an unknown pulsar remains possible, hadronic-origin emission from an unknown supernova remnant is preferred.Comment: 9 pages, 7 figures, accepted for publication in Ap

Alveolar Dynamics and Beyond – The Importance of Surfactant Protein C and Cholesterol in Lung Homeostasis and Fibrosis

Surfactant protein C (SP-C) is an important player in enhancing the interfacial adsorption of lung surfactant lipid films to the alveolar air-liquid interface. Doing so, surface tension drops down enough to stabilize alveoli and the lung, reducing the work of breathing. In addition, it has been shown that SP-C counteracts the deleterious effect of high amounts of cholesterol in the surfactant lipid films. On its side, cholesterol is a wellknown modulator of the biophysical properties of biological membranes and it has been proven that it activates the inflammasome pathways in the lung. Even though the molecular mechanism is not known, there are evidences suggesting that these two molecules may interplay with each other in order to keep the proper function of the lung. This review focuses in the role of SP-C and cholesterol in the development of lung fibrosis and the potential pathways in which impairment of both molecules leads to aberrant lung repair, and therefore impaired alveolar dynamics. From molecular to cellular mechanisms to evidences in animal models and human diseases. The evidences revised here highlight a potential SP-C/cholesterol axis as target for the treatment of lung fibrosis

Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

Background: The EMPA KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. Methods: EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. Findings: Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5–2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62–0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16–1·59), representing a 50% (42–58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1). Interpretation: In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. Funding: Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council

Excitation-transcription coupling in skeletal muscle: the molecular pathways of exercise

Muscle fibres have different properties with respect to force, contraction speed, endurance, oxidative/glycolytic capacity etc. Although adult muscle fibres are normally post-mitotic with little turnover of cells, the physiological properties of the pre-existing fibres can be changed in the adult animal upon changes in usage such as after exercise. The signal to change is mainly conveyed by alterations in the patterns of nerve-evoked electrical activity, and is to a large extent due to switches in the expression of genes. Thus, an excitation-transcription coupling must exist. It is suggested that changes in nerve-evoked muscle activity lead to a variety of activity correlates such as increases in free intracellular Ca2+ levels caused by influx across the cell membrane and/or release from the sarcoplasmatic reticulum, concentrations of metabolites such as lipids and ADP, hypoxia and mechanical stress. Such correlates are detected by sensors such as protein kinase C (PKC), calmodulin, AMP-activated kinase (AMPK), peroxisome proliferator-activated receptor δ (PPARδ), and oxygen dependent prolyl hydroxylases that trigger intracellular signaling cascades. These complex cascades involve several transcription factors such as nuclear factor of activated T-cells (NFAT), myocyte enhancer factor 2 (MEF2), myogenic differentiation factor (myoD), myogenin, PPARδ, and sine oculis homeobox 1/eyes absent 1 (Six1/Eya1). These factors might act indirectly by inducing gene products that act back on the cascade, or as ultimate transcription factors binding to and transactivating/repressing genes for the fast and slow isoforms of various contractile proteins and of metabolic enzymes. The determination of size and force is even more complex as this involves not only intracellular signaling within the muscle fibres, but also muscle stem cells called satellite cells. Intercellular signaling substances such as myostatin and insulin-like growth factor 1 (IGF-1) seem to act in a paracrine fashion. Induction of hypertrophy is accompanied by the satellite cells fusing to myofibres and thereby increasing the capacity for protein synthesis. These extra nuclei seem to remain part of the fibre even during subsequent atrophy as a form of muscle memory facilitating retraining. In addition to changes in myonuclear number during hypertrophy, changes in muscle fibre size seem to be caused by alterations in transcription, translation (per nucleus) and protein degradation