Search

This article surveys the status of the appointment of private sector personnel to national public service and the specific contributions of private sector personnel in the public service workplace, the challenges they face, and the direction of measures to address them. With regard to the status of appointment, the increasing trend of private sector personnel appointment in recent years has been remarkable, as evidenced by the fact that the number of those accepted into national public service without a limitation of period has doubled over the past five years. In this context, based on the results of an interview-based survey of private sector personnel, specific examples of contributions by private sector personnel in the public service workplace were identified under the categories of (1) “knowledge and experience utilization,” (2) “work improvement,” and (3)“network utilization.” The survey also identified significant issues, such as dealing with tasks unique to the public service and responding to the Diet and revisions of the law, and the provision of sufficient information on tasks unique to public service.

We develop an approach to chiral kinetic theories for electrons close to equilibrium and neutrinos away from equilibrium based on a systematic power counting scheme for different timescales of electromagnetic and weak interactions. Under this framework, we derive electric and energy currents along magnetic fields induced by neutrino radiation in general nonequilibrium states. This may be regarded as an effective chiral magnetic effect (CME), which is present without a chiral chemical potential, unlike the conventional CME. We also consider the so-called gain region of core-collapse supernovae as an example and find that the effective CME enhanced by persistent neutrino emission in time is sufficiently large to lead to the inverse cascade of magnetic and fluid kinetic energies and observed magnitudes of pulsar kicks. Our framework may also be applicable to other dense-matter systems involving nonequilibrium neutrinos., Comment: 6 pages, 1 figure, journal version

The quantum channel decomposition techniques, which contain the so-called probabilistic error cancellation and gate/wire cutting, are powerful approach for simulating a hard-to-implement (or an ideal) unitary operation by concurrently executing relatively easy-to-implement (or noisy) quantum channels. However, such virtual simulation necessitates an exponentially large number of decompositions, thereby significantly limiting their practical applicability. This paper proposes a channel decomposition method for target unitaries that have their input and output conditioned on specific quantum states, namely unitaries with pre- and post-selection. Specifically, we explicitly determine the requisite number of decomposing channels, which could be significantly smaller than the selection-free scenario. Furthermore, we elucidate the structure of the resulting decomposed unitary. We demonstrate an application of this approach to the quantum linear solver algorithm, highlighting the efficacy of the proposed method., 11pages, 5figures

Quantum state estimation (or state tomography) is an indispensable task in quantum information processing. Because full state tomography that determines all elements of the density matrix is computationally demanding, one usually takes the strategy of assuming a certain model of quantum states and identifying the model parameters. However, it is difficult to make a valid assumption given little prior knowledge on a quantum state of interest, and thus we need a reasonable model selection method for quantum state estimation. Actually, in the classical statistical estimation theory, several types of information criteria have been established and widely used in practice for appropriately choosing a classical statistical model. In this study, we propose quantum information criteria for evaluating the quality of the estimated quantum state in terms of the quantum relative entropy, which is a natural quantum analogue of the classical information criterion defined in terms of Kullback-Leibler divergence. In particular, we derive two quantum information criteria depending on the type of estimator for the quantum relative entropy; one uses the log-likelihood and the other uses the classical shadow. The general role of information criteria is to predict the performance of an estimated model for unseen data, although it is a function of only sampled data; this generalization capability of the proposed quantum information criteria is evaluated in numerical simulations.

Quantum computing can be used to speed up the simulation time (more precisely, the number of queries of the algorithm) for physical systems; one such promising approach is the Hamiltonian simulation (HS) algorithm. Recently, it was proven that the quantum singular value transformation (QSVT) achieves the minimum simulation time for HS. An important subroutine of the QSVT-based HS algorithm is the amplitude amplification operation, which can be realized via the oblivious amplitude amplification or the fixed-point amplitude amplification in the QSVT framework. In this work, we execute a detailed analysis of the error and number of queries of the QSVT-based HS and show that the oblivious method is better than the fixed-point one in the sense of simulation time for a given error tolerance. Based on this finding, we apply the QSVT-based HS to the one-dimensional linearized Vlasov-Poisson equation and demonstrate that the linear Landau damping can be successfully simulated., 18 pages, 14 figures

The circuit cutting techniques are widely used to execute quantum algorithms that require more qubits than physically available; the idea is to decompose the original circuit to smaller sub-circuits that are combined to simulate the original one by sampling. However, they suffer from an inevitable issue that the sampling cost exponentially grows with the number of cuts, and thus it is practically important to develop decomposition methods working with smaller sampling cost. This paper focuses on the parallel wire-cutting problem, meaning that the target to cut is the identity channel acting on multiple qubits in parallel. There are two previous studies. The first one is a teleportation-based method that achieves this task with the use of ancilla qubits, whose sampling cost was proven to be optimal. The second one uses the technique of classical shadow to solve the problem without any ancilla qubits, which however does not achieve the optimal sampling cost. This paper gives a definitive solution to this problem. That is, we develop the optimal parallel wire cutting without ancilla qubits, in the same setup as that taken in the above previous studies. Moreover, the developed ancilla-free method can be applied to the non-parallel case, which also improves the sampling cost obtained in the previous study.

We demonstrate the general existence of a local dipole conservation law in bosonic field theory. The scalar charge density arises from the symplectic form of the system, whereas the tensor current descends from its stress tensor. The algebra of spatial translations becomes centrally extended in presence of field configurations with a finite nonzero charge. Furthermore, when the symplectic form is closed but not exact, the system may, surprisingly, lack a well-defined momentum density. This leads to a theorem for the presence of additional light modes in the system whenever the short-distance physics is governed by a translationally invariant local field theory. We also illustrate this mechanism for axion electrodynamics as an example of a system with Nambu-Goldstone modes of higher-form symmetries., 30 pages, 2 figures

Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical problem classifying different objects based on temporal sensor data. In both cases the proposed reservoir computer shows a higher performance than a linear regression or classification model. The results indicate that a noisy quantum device potentially functions as a reservoir computer, and notably, the quantum noise, which is undesirable in the conventional quantum computation, can be used as a rich computation resource.

A 56-year-old woman who was diagnosed with schizophrenia at 30 years old presented with acute exacerbation of psychiatric symptoms. She was treated with increasing doses of antipsychotics owing to suspicion of worsening schizophrenia. Thereafter, she rapidly developed pyrexia, marked sweating, tremors, myotonia/postural abnormalities, stupor with akinesia, and decreased blood pressure, suggesting neuroleptic malignant syndrome (NMS) caused by the increased dose of antipsychotics. As a differential diagnosis, we considered malignant catatonia caused by schizophrenia. In addition, because the patient showed atypical symptoms and an atypical course of schizophrenia, we considered malignant catatonia caused by an organic disease and performed an antibody test of her cerebrospinal fluid. Modified electroconvulsive therapy was performed to treat the NMS, and the patient’s psychiatric and somatic symptoms improved. During treatment, she exhibited positivity for anti-N-methyl-Daspartate (NMDA) receptor antibodies. However, she did not meet the consensus criteria for anti-NMDA receptor encephalitis. Thus, as a final diagnosis, we considered NMS of atypical schizophrenia with anti-NMDA receptor antibody positivity. Anti-NMDA receptor encephalitis can be misdiagnosed as schizophrenia because of similar psychiatric symptoms. This case emphasizes the importance of testing for anti-NMDA receptor antibodies in patients with an atypical course of schizophrenia., Acta medica Nagasakiensia, 65(2), pp.63−66; 2022

Electron microscopy based on high-energy electrons allows nanoscopic structural imaging and nano-analysis taking advantage of secondarily generated particles, such as secondary electrons, photons, or quasi-particles. Especially for the photon emission upon electron beam excitation, so-called cathodoluminescence, the correlation between primary incident electrons and emitted photons includes information on the entire interaction process. In this work, we propose time-correlated electron and photon counting microscopy, where coincidence events of primary electrons and generated photons are counted after interaction. The electron-photon time correlation enables extracting an emitter lifetime and is applicable to both coherent and incoherent photon generation processes, demonstrating a universal lifetime measurement independent of the photon state. We also introduce a correlation factor and discuss the correlation between electrons and generated coherent photons. Through momentum selection, we observe correlation changes, indicating the effective contribution of pair correlation originating from the electron-photon entanglement based on momentum conservation. The present work is a milestone for next-generation electron microscopy utilizing quantum correlation.

We propose two quantum algorithms for a problem in bioinformatics, position weight matrix (PWM) matching, which aims to find segments (sequence motifs) in a biological sequence such as DNA and protein that have high scores defined by the PWM and are thus of informational importance related to biological function. The two proposed algorithms, the naive iteration method and the Monte-Carlo-based method, output matched segments, given the oracular accesses to the entries in the biological sequence and the PWM. The former uses quantum amplitude amplification (QAA) for sequence motif search, resulting in the query complexity scaling on the sequence lengthn, the sequence motif lengthmand the number of the PWMsKas, which means speedup over existing classical algorithms with respect tonandK. The latter also uses QAA, and further, quantum Monte Carlo integration for segment score calculation, instead of iteratively operating quantum circuits for arithmetic in the naive iteration method; then it provides the additional speedup with respect tomin some situation. As a drawback, these algorithms use quantum random access memories and their initialization takesO(n) time. Nevertheless, our algorithms keep the advantage especially when we search matches in a sequence for many PWMs in parallel.

Variational Quantum Eigensolver (VQE) is a hybrid algorithm for finding the minimum eigenvalue/vector of a given Hamiltonian by optimizing a parametrized quantum circuit (PQC) using a classical computer. Sequential optimization methods, which are often used in quantum circuit tensor networks, are popular for optimizing the parametrized gates of PQCs. This paper focuses on the case where the components to be optimized are single-qubit gates, in which the analytic optimization of a single-qubit gate is sequentially performed. The analytical solution is given by diagonalization of a matrix whose elements are computed from the expectation values of observables specified by a set of predetermined parameters which we call the parameter configurations. In this study, we first show that the optimization accuracy significantly depends on the choice of parameter configurations due to the statistical errors in the expectation values. We then identify a metric that quantifies the optimization accuracy of a parameter configuration for all possible statistical errors, named configuration overhead/cost or C-cost. We theoretically provide the lower bound of C-cost and show that, for the minimum size of parameter configurations, the lower bound is achieved if and only if the parameter configuration satisfies the so-called equiangular line condition. Finally, we provide numerical experiments demonstrating that the optimal parameter configuration exhibits the best result in several VQE problems. We hope that this general statistical methodology will enhance the efficacy of sequential optimization of PQCs for solving practical problems with near-term quantum devices., Comment: 20 pages, 5 figures

We demonstrate a universal mechanism of a class of instabilities in infrared regions for massless Abelian $p$-form gauge theories with topological interactions, which we call generalized chiral instabilities. Such instabilities occur in the presence of initial electric fields for the $p$-form gauge fields. We show that the dynamically generated magnetic fields tend to decrease the initial electric fields and result in configurations with linking numbers, which can be characterized by non-invertible global symmetries. The so-called chiral plasma instability and instabilities of the axion electrodynamics and $(4+1)$-dimensional Maxwell-Chern-Simons theory in electric fields can be described by the generalized chiral instabilities in a unified manner. We also illustrate this mechanism in the $(2+1)$-dimensional Goldstone-Maxwell model in electric field., Comment: 38 pages, 9 figures; v2: references added, minor corrections, published version

Generalized eigenvalue problems (GEPs) play an important role in the variety of fields including engineering and machine learning. Especially, many problems in these fields can be reduced to finding the minimum or maximum eigenvalue of GEPs. One of the key problems to handle GEPs is that the memory usage and computational complexity explode as the system of interest grows. This paper aims at extending sequential quantum optimizers for GEPs. Sequential quantum optimizers are a family of algorithms that iteratively solve the analytical optimization of single-qubit gates in a coordinate descent manner. The contribution of this paper is as follows. First, we formulate the GEP as the minimization/maximization problem of the fractional form of the expectations of two Hermitians. We then showed that the fractional objective function can be analytically minimized or maximized with respect to a single-qubit gate by solving a GEP of a 4 x 4 matrix. Second, we show that a system of linear equations (SLE) characterized by a positive-definite Hermitian can be formulated as a GEP and thus be attacked using the proposed method. Finally, we demonstrate two applications to important engineering problems formulated with the finite element method. Through the demonstration, we have the following bonus finding; a problem having a real-valued solution can be solved more effectively using quantum gates generating a complex-valued state vector, which demonstrates the effectiveness of the proposed method., Comment: 14 pages, 8 figures

Quantum computers are expected to solve the problems for quantum chemistry and materials science with higher accuracy than classical computers. Quantum computing quantum Monte Carlo (QC-QMC) is a method that can be combined with quantum algorithms such as variational quantum eigensolver (VQE) to obtain the ground state with fewer quantum resources and higher accuracy than either VQE or QMC alone. In this study, we propose an algorithm combining QC-QMC with hybrid tensor network (HTN) to extend the applicability of QC-QMC for the system beyond the size of a single quantum device, and we named the algorithm HTN+QMC. For HTN with the structure of a two-layer quantum-quantum tree tensor, the proposed algorithm for an $O(n^2)$-qubit reference wave function (trial wave function) in QMC can be performed by using only a $n$-qubit device excluding ancilla qubits. Full configuration interaction QMC is adopted as an example of QMC, and the proposed algorithm is applied to the Heisenberg chain model, the graphite-based Hubbard model, the hydrogen plane model, and MonoArylBiImidazole (MABI). The results show that the algorithm can achieve energy accuracy several orders of magnitude higher than either VQE or QMC alone. In addition, the energy accuracy of HTN+QMC is as same as QC-QMC when the system is appropriately decomposed. These results pave the way to electronic structure calculation for large systems with high accuracy on current quantum devices., Comment: 27pages, 19 figures, 5 tables

Quantum computing has a potential to accelerate the data processing efficiency, especially in machine learning, by exploiting special features such as the quantum interference. The major challenge in this application is that, in general, the task of loading a classical data vector into a quantum state requires an exponential number of quantum gates. The approximate amplitude encoding (AAE) method, which uses a variational means to approximately load a given real-valued data vector into the amplitude of a quantum state, was recently proposed as a general approach to this problem mainly for near-term devices. However, AAE cannot load a complex-valued data vector, which narrows its application range. In this work, we extend AAE so that it can handle a complex-valued data vector. The key idea is to employ the fidelity distance as a cost function for optimizing a parameterized quantum circuit, where the classical shadow technique is used to efficiently estimate the fidelity and its gradient. We apply this algorithm to realize the complex-valued-kernel binary classifier called the compact Hadamard classifier, and then give a numerical experiment showing that it enables classification of Iris dataset and credit card fraud detection., 12 pages, 7 figures

Quantum machine learning (QML) is the spearhead of quantum computer applications. In particular, quantum neural networks (QNN) are actively studied as the method that works both in near-term quantum computers and fault-tolerant quantum computers. Recent studies have shown that supervised machine learning with QNN can be interpreted as the quantum kernel method (QKM), suggesting that enhancing the practicality of the QKM is the key to building near-term applications of QML. However, the QKM is also known to have two severe issues. One is that the QKM with the (inner-product based) quantum kernel defined in the original large Hilbert space does not generalize; namely, the model fails to find patterns of unseen data. The other one is that the classical computational cost of the QKM increases at least quadratically with the number of data, and therefore, QKM is not scalable with data size. This paper aims to provide algorithms free from both of these issues. That is, for a class of quantum kernels with generalization capability, we show that the QKM with those quantum kernels can be made scalable by using our proposed deterministic and random features. Our numerical experiment, using datasets including $O(1,000) \sim O(10,000)$ training data, supports the validity of our method., 19 pages, 2 figures

Background: Aortic valve neocuspidization (AVNeo) has emerged as a promising aortic valve procedure, and is expected to have a larger effective orifice area (EOA) than commercially available bioprostheses. It is, however, unclear which indices could facilitate left ventricular (LV) reverse remodeling after AVNeo. The aim of this study is to verify the impact of global left ventricular afterload on the LV reverse remodeling following AVNeo.Methods: Data-available consecutive 38 patients (median age, 77; interquartile range, 72.8–82.0) undergoing AVNeo for severe aortic stenosis were enrolled in this study. Preoperative and the last follow-up echocardiographic data were retrospectively analyzed including the valvuloarterial impedance (Zva), a marker of global LV afterload. Reduction in LV geometry index (LVGI) and relative wall thickness (RWT) were used as an indicator for LV reverse remodeling.Results: The Zva reduced in 24 patients (63.2%) during the follow-up period (median, 12 months). Reduction in Zva significantly correlated to improvement of LV geometry (LVGI (r = 0.400, p = 0.013) and RWT (r = 0.627, p < 0.001)),whereas increase in EOA index did not significantly correlate to LVGI (r = 0.009, p = 0.957), or RWT (r = 0.105, p = 0.529)). The reduction in Zva was the multivariate predictor of LV reverse remodeling.Conclusions: Low global LV afterload led to significant LV reverse remodeling even after AVNeo, which could achieve better valve performance than the conventional bioprostheses., 本文/Department of Thoracic and Cardiovascular Surgery, Mie University Hospital, 2‑174 Edobashi, Tsu City, Mie 514‑8507, Japan, 9p

Quantum computing has been moving from a theoretical phase to practical one, presenting daunting challenges in implementing physical qubits, which are subjected to noises from the surrounding environment. These quantum noises are ubiquitous in quantum devices and generate adverse effects in the quantum computational model, leading to extensive research on their correction and mitigation techniques. But do these quantum noises always provide disadvantages? We tackle this issue by proposing a framework called quantum noise-induced reservoir computing and show that some abstract quantum noise models can induce useful information processing capabilities for temporal input data. We demonstrate this ability in several typical benchmarks and investigate the information processing capacity to clarify the framework's processing mechanism and memory profile. We verified our perspective by implementing the framework in a number of IBM quantum processors and obtained similar characteristic memory profiles with model analyses. As a surprising result, information processing capacity increased with quantum devices' higher noise levels and error rates. Our study opens up a novel path for diverting useful information from quantum computer noises into a more sophisticated information processor.

We study the novel asteroseismology of the chiral magnetic wave (CMW) of the quark number density in relativistic quark matter inside neutron stars and core-collapse supernovae and the chiral vortical wave (CVW) of the neutrino number density in relativistic neutrino matter at the core of supernovae. We call the oscillation modes for these chiral waves the chiral magnetic mode (CM-mode) and chiral vortical mode (CV-mode), respectively. We derive the dispersion relations of these new modes in the presence of the chirality flipping due to the finite quark mass and diffusion. We then estimate the possible frequencies of these modes and amplitudes of the resulting gravitational waves. In particular, since the CM-mode can exist only in quark matter with nearly gapless quarks (such as the two-flavor color superconductivity) for a sufficiently strong magnetic field, corresponding gravitational waves provide a new possible probe for such quark matter and the magnetic field in neutron stars., 25 pages, 2 figures; v2: abstract modified, main results changed, discussions on the CM-mode of quark matter added

Objectives: Although large hepatectomy (i.e., resection of 2–3 segments) is an increasingly common treatment for hepatocellular carcinoma and cholangiocarcinoma, it can lead to liver failure. However, a resected liver may contain large quantities of both normal hepatocytes (NHs) and carcinoma cells. We investigated separating these cell types so that NHs could be used as transplantable cells. Materials and methods: Cancer cells were developed by immortalizing rat hepatocytes, using an artificial chromosome vector. Cancer cells and primary hepatocytes (PHs) were mixed in a 1:1 ratio, then separated into two groups using fluorescence activated cell sorting (FACS). Normal hepatocytes after FACS (NHaF) and cancer cells after FACS (CAaF) were transplanted into two spots on opposite sides of the backs of nude mice; and also into the spleens of three groups (NHaF, CAaF and controls) of non-albumin rats (NARs), from which we measured blood albumin levels, using ELISA. Result: The PH and cancer cells were successfully separated using FACS. After separation, cancer cells transplanted subcutaneously in nude mice formed tumors, whereas transplanted PH cells in NARs only produced higher albumin levels. Conclusion: Transplanted NHaF cells did not produce tumors. However, this cells function was not enough in power for transplant source by this method. Nevertheless, we believe this technique can be improved and used to treat patients successfully.

Natural decomposition of organic matter is essential in food systems, and compost is used worldwide as an organic fermented fertilizer. However, as a feature of the ecosystem, its effects on the animals are poorly understood. Here we show that oral administration of compost and/or its derived thermophilic Bacillaceae, i.e., Caldibacillus hisashii and Weizmannia coagulans, can modulate the prophylactic activities of various industrial animals. The fecal omics analyses in the modulatory process showed an improving trend dependent upon animal species, environmental conditions, and administration. However, structural equation modeling (SEM) estimated the grouping candidates of bacteria and metabolites as standard key components beyond the animal species. In particular, the SEM model implied a strong relationship among partly digesting fecal amino acids, increasing genus Lactobacillus as inhabitant beneficial bacteria and 2-aminoisobutyric acid involved in lantibiotics. These results highlight the potential role of compost for sustainable protective control in agriculture, fishery, and livestock industries.

Quantum-enhanced (i.e., less query complexity compared to any classical method) mean value estimation of observables is a fundamental task in various quantum technologies; in particular, it is an essential subroutine in quantum computing algorithms. Notably, the quantum estimation theory identifies the ultimate precision of such estimator, which is referred to as the quantum Cram\'{e}r-Rao (QCR) lower bound or equivalently the inverse of the quantum Fisher information. Because the estimation precision directly determines the performance of those quantum technological systems, it is highly demanded to develop a generic and practically implementable estimation method that achieves the QCR bound. Under imperfect conditions, however, such ultimate estimator has not been developed. This paper proposes a quantum-enhanced mean value estimation method in a depolarizing noisy environment that asymptotically achieves the QCR bound exponentially fast with respect to the number of qubits. The method employs a maximum likelihood estimator consisting of the amplitude amplification and an implementable measurement, which are adaptively optimized to achieve the QCR bound. We provide a rigorous analysis for the statistical properties of the proposed adaptive estimator such as consistency and asymptotic normality. Furthermore, several numerical simulations are provided to demonstrate the effectiveness of the method, particularly showing that the estimator needs only a modest number of measurements to almost saturate the QCR bound. The proposed method will be useful in various applications beyond the subroutine in quantum computing algorithms, thereby paving the way for an interdisciplinary research in quantum computing and quantum sensing.

Generative modeling is an unsupervised machine learning framework, that exhibits strong performance in various machine learning tasks. Recently we find several quantum version of generative model, some of which are even proven to have quantum advantage. However, those methods are not directly applicable to construct a generative model for learning a set of quantum states, i.e., ensemble. In this paper, we propose a quantum generative model that can learn quantum ensemble, in an unsupervised machine learning framework. The key idea is to introduce a new loss function calculated based on optimal transport loss, which have been widely used in classical machine learning due to its several good properties; e.g., no need to ensure the common support of two ensembles. We then give in-depth analysis on this measure, such as the scaling property of the approximation error. We also demonstrate the generative modeling with the application to quantum anomaly detection problem, that cannot be handled via existing methods. The proposed model paves the way for a wide application such as the health check of quantum devices and efficient initialization of quantum computation.

Additional file 1: Supplemental Table 1.

Quantum kernel method is a machine learning model exploiting quantum computers to calculate the quantum kernels (QKs) that measure the similarity between data. Despite the potential quantum advantage of the method, the commonly used fidelity-based QK suffers from a detrimental issue, which we call the vanishing similarity issue; detecting the difference between data becomes hard with the increase of the number of qubits, due to the exponential decrease of the expectation and the variance of the QK. This implies the need to design QKs alternative to the fidelity-based one. In this work, we propose a new class of QKs called the quantum Fisher kernels (QFKs) that take into account the geometric structure of the data source. We analytically and numerically demonstrate that the QFK based on the anti-symmetric logarithmic derivatives (ALDQFK) can avoid the issue when the alternating layered ansatzs (ALAs) are used, while the fidelity-based QK cannot even with the ALAs. Moreover, the Fourier analysis numerically elucidates that the ALDQFK can have expressivity comparable to that of the fidelity-based QK. These results indicate that the QFK paves the way for practical applications of quantum machine learning with possible quantum advantages.

INTRODUCTION: Hyperbaric oxygen (HBO) exposure for 10−15 min has been shown to reduce peripheral blood flow due to vasoconstriction. However, the relationship between decreased peripheral blood flow and the therapeutic effects of HBO treatment on peripheral circulatory disorders remain unknown. Longer exposures have been reported to have vasodilatory effects and increase peripheral blood flow. This study investigated the effect of HBO treatment on blood flow and transcutaneous oxygen pressure (TcPO(2)). METHODS: Twenty healthy volunteers aged 20–65 years (nine males) participated in this study. All participants breathed oxygen for 60 min at 253.3 kPa. Peripheral blood flow using laser Doppler flowmetry and TcPO(2) on the ear, hand, and foot were continuously measured from pre-HBO exposure to 10 min post-exposure. RESULTS: Peripheral blood flow in each body part decreased by 7–23% at the beginning of the HBO exposure, followed by a slow increase. Post-exposure, peripheral blood flow increased 4–76% in each body part. TcPO(2) increased by 840–1,513% during the exposure period, and remained elevated for at least 10 min after the exposure. CONCLUSIONS: The findings of the current study suggest vasoconstriction during HBO treatment is transient, and even when present does not inhibit the development of increased tissue oxygen partial pressure. These findings are relevant to studies investigating changes in peripheral blood flow during HBO treatment in patients with circulatory disorders.

Recently, quantum neural networks or quantum-classical neural networks (qcNN) have been actively studied, as a possible alternative to the conventional classical neural network (cNN), but their practical and theoretically-guaranteed performance is still to be investigated. In contrast, cNNs and especially deep cNNs, have acquired several solid theoretical basis; one of those basis is the neural tangent kernel (NTK) theory, which can successfully explain the mechanism of various desirable properties of cNNs, particularly the global convergence in the training process. In this paper, we study a class of qcNN composed of a quantum data-encoder followed by a cNN. The quantum part is randomly initialized according to unitary 2-designs, which is an effective feature extraction process for quantum states, and the classical part is also randomly initialized according to Gaussian distributions; then, in the NTK regime where the number of nodes of the cNN becomes infinitely large, the output of the entire qcNN becomes a nonlinear function of the so-called projected quantum kernel. That is, the NTK theory is used to construct an effective quantum kernel, which is in general nontrivial to design. Moreover, NTK defined for the qcNN is identical to the covariance matrix of a Gaussian process, which allows us to analytically study the learning process and as a consequence to have a condition of the dataset such that qcNN may perform better than classical counterparts. These properties are investigated in thorough numerical experiments; particularly, we demonstrate that the qcNN shows a clear advantage over fully classical NNs and qNNs for the problem of learning the quantum data-generating process., 33 pages, 10 figures

We investigate polarized gravitational waves generated by chiral fermions in the early Universe. In particular, we focus on the contribution from left-handed neutrinos in thermal equilibrium with finite temperature and chemical potential in the radiation dominated era. We compute the correlation functions of gravitational fields pertinent to the Stokes parameter $V$ characterizing the circular polarization of gravitational waves in the Minkowski and expanding spacetime backgrounds. In the expanding universe, we find that the thermalized neutrinos induce a non-vanishing $V$ linear to the neutrino degeneracy parameter and wavenumber of gravitational waves in the long wavelength region. While the magnitude of the gravitational waves generated by thermal neutrinos is too small to be detectable by current and planned third generation gravitational wave detectors, their observations by future generation detectors for ultra-high frequency regimes could provide information on the neutrino degeneracy parameter in the early Universe., 37 pages, 1 figure; published version

We consider the implementation of two-qubit gates when the physical systems used to realize the qubits possess additional quantum states in the accessible energy range. We use optimal control theory to determine the maximum achievable gate speed for two-qubit gates in the qubit subspace of the many-level Hilbert space, and we analyze the effect of the additional quantum states on the gate speed. We identify two competing mechanisms. On one hand, higher energy levels are generally more strongly coupled to each other. Under suitable conditions, this stronger coupling can be utilized to make two-qubit gates significantly faster than the reference value based on simple qubits. On the other hand, a weak anharmonicity constrains the speed at which the system can be adequately controlled: according to the intuitive picture, faster operations require stronger control fields, which are more likely to excite higher levels in a weakly anharmonic system, which in turn leads to faster decoherence and uncontrolled leakage outside the qubit space. In order to account for this constraint, we modify the pulse optimization algorithm to avoid pulses that lead to appreciable population of the higher levels. In this case we find that the presence of the higher levels can lead to a significant reduction in the maximum achievable gate speed. We also compare the optimal-control gate speeds with those obtained using the cross-resonance/selective-darkening gate protocol. We find that the latter, with some parameter optimization, can be used to achieve a relatively fast implementation of the CNOT gate. These results can help the search for optimized gate implementations in realistic quantum computing architectures, such as those based on superconducting circuits. They also provide guidelines for desirable conditions on anharmonicity that allow optimal utilization of the higher levels to achieve fast quantum gates., Comment: 14 pages (two-column), 6 figures

早大学位記番号:新8842

In this paper, we propose a quantum amplitude estimation method that uses a modified Grover operator and quadratically improves the estimation accuracy in the ideal case, as in the conventional one using the standard Grover operator. Under the depolarizing noise, the proposed method can outperform the conventional one in the sense that it can in principle achieve the ultimate estimation accuracy characterized by the quantum Fisher information in the limit of a large number of qubits, while the conventional one cannot achieve the same value of ultimate accuracy. In general this superiority requires a sophisticated adaptive measurement, but we numerically demonstrate that the proposed method can outperform the conventional one and approach to the ultimate accuracy, even with a simple non-adaptive measurement strategy., 20 pages, 4 figures

We present the mechanical squeezing of a mg-scale suspended mirror (i.e. a pendulum) near quantum regimes through continuous linear position measurement. The experiment involved the pendulum interacting with photon coherent fields in a detuned optical cavity. The position uncertainty in the measured data is reduced and squeezed to $470$ times the zero-point amplitude $x_{\rm zpf}$ with a purity of about 0.0004, by means of optimal state estimation through causal Wiener filtering. The purity of the squeezed state is clearly maximized by the Wiener filter, based on precisely identified optomechanical parameters. This is the first step for measurement-based quantum control of macroscopic pendulums, e.g. generation of an entanglement state between macroscopic pendulums. Such quantum control will provide a direct insight into the quantum to classical transition and will pave the way to test semiclassical gravity and gravity sourced by macroscopic quantum oscillators., 5 pages, 3 figures, and a supplemental material

We show that the presence of a magnetic monopole in position space gives rise to a violation of the fermion number conservation in chiral matter. Using the chiral kinetic theory, we derive a model-independent expression of such a violation in nonequilibrium many-body systems of chiral fermions. In local thermal equilibrium at finite temperature and chemical potential, in particular, this violation is proportional to the chemical potential with a topologically quantized coefficient. These consequences are due to the interplay between the Dirac monopole in position space and the Berry monopole in momentum space. Our mechanism can be applied to study the roles of magnetic monopoles in the nonequilibrium evolution of the early Universe., 5 pages