arXiv subjects

Keisuke Fujii

Publications and source records attributed to Keisuke Fujii.

At least 235 records · Page 13Linked to original sources

arXiv2020

Study of the $hγZ$ coupling at the ILC

We study the $h γZ$ coupling, which is a loop induced coupling in the Standard Model (SM), to probe new physics. In a global fit based on the SM Effective Field Theory, measurement of the SM $h γZ$ coupling can provide a very useful constraint, in particular for the precise determination of $hZZ$ and $hWW$ couplings. At the International Linear Collider (ILC), there are two direct ways to study the $h γZ$ coupling: one is to measure the branching ratio of the $h \to γZ$ decay and the other to measure the cross section for the $e^+e^- \to h γ$ process. We have performed a full simulation study of the $e^+e^- \to h γ$ process at the 250 GeV ILC, assuming 2 ab$^{-1}$ data collected by the International Large Detector (ILD). The expected 1$σ$ bound on the effective $hγZ$ coupling ($ζ_{AZ}$) combining measurements of the cross section for $e^+e^- \to h γ$ followed by $h \to b \bar{b}$ and the $h \to γZ$ branching ratio is $-0.0015<ζ_{AZ}<0.0015$. The expected significance for the signal cross section in the fully hadronic $h \to WW^*$ channel is 0.09 $σ$ for beam polarizations of $P(e^-,e^+)=(-80\%,+30\%)$.

hep-ex↗

arXiv2019

The ILC as a natural SUSY discovery machine and precision microscope: from light higgsinos to tests of unification

The requirement of electroweak naturalness in simple supersymmetric models implies the existence of a cluster of four light higgsinos with mass $\sim 100-300$\,GeV, the lighter the better. While such light compressed spectra may be challenging to observe at LHC, the International Linear $e^+e^-$ Collider (ILC) with $\sqrt{s}>2m_{\rm higgsino}$ would serve as both a SUSY discovery machine and a precision microscope. We study higgsino pair production signatures at the ILC based on full, \texttt{Geant4-}based simulation of the ILD detector concept. We examine several benchmark scenarios that may be challenging for discovery at HL-LHC due to mass differences between the higgsino states between $20$ and $4$\,GeV. Assuming $\sqrt{s}= 500$\,GeV and 1000\,fb$^{-1}$ of integrated luminosity, the individual higgsino masses can be measured to $1-2\%$ precision in case of the larger mass differences, and at the level of $5\%$ for the smallest mass difference case. The higgsino mass splittings are sensitive to the electroweak gaugino masses and allow extraction of gaugino masses to $\sim 3-20\%$ (depending on the model). Extrapolation of gaugino masses via renormalization group running can test the hypothesis of gaugino mass unification. We also examine a case with natural generalized mirage mediation where the unification of gaugino masses at an intermediate scale apparently gives rise to a natural SUSY spectrum somewhat beyond the reach of HL-LHC.

hep-ex↗

arXiv2019

Experimental quantum kernel machine learning with nuclear spins in a solid

We employ so-called quantum kernel estimation to exploit complex quantum dynamics of solid-state nuclear magnetic resonance for machine learning. We propose to map an input to a feature space by input-dependent Hamiltonian evolution, and the kernel is estimated by the interference of the evolution. Simple machine learning tasks, namely one-dimensional regression tasks and two-dimensional classification tasks, are performed using proton spins which exhibit correlation over 10 spins. We also performed numerical simulations to evaluate the performance without the noise inevitable in the actual experiments. The performance of the trained model tends to increase with the longer evolution time, or equivalently, with a larger number of spins involved in the dynamics for certain tasks. This work presents a quantum machine learning experiment using one of the largest quantum systems to date.

quant-ph↗

arXiv2019

Model-Independent Determination of the Triple Higgs Coupling at $e^+e^-$ Colliders

The observation of Higgs pair production at high-energy colliders can give evidence for the presence of a triple Higgs coupling. However, the actual determination of the value of this coupling is more difficult. In the context of general models for new physics, double Higgs production processes can receive contributions from many possible beyond-Standard-Model effects. This dependence must be understood if one is to make a definite statement about the deviation of the Higgs field potential from the Standard Model. In this paper, we study the extraction of the triple Higgs coupling from the process $e^+e^-\to Zhh$. We show that, by combining the measurement of this process with other measurements available at a 500 GeV $e^+e^-$ collider, it is possible to quote model-independent limits on the Effective Field Theory parameter c6 that parametrizes modifications of the Higgs potential. We present precise error estimates based on the anticipated ILC physics program, studied with full simulation. Our analysis also gives new insight into the model-independent extraction of the Higgs boson coupling constants and total width from $e^+e^-$ data.

hep-ph↗

arXiv2019

Tests of the Standard Model at the International Linear Collider

We present an overview of the capabilities that the International Linear Collider (ILC) offers for precision measurements that probe the Standard Model. First, we discuss the improvements that the ILC will make in precision electroweak observables, both from W boson production and radiative return to the Z at 250 GeV in the center of mass and from a dedicated GigaZ stage of running at the Z pole. We then present new results on precision measurements of fermion pair production, including the production of b and t quarks. We update the ILC projections for the determination of Higgs boson couplings through a Standard Model Effective Field Theory fit taking into account the new information on precision electroweak constraints. Finally, we review the capabilities of the ILC to measure the Higgs boson self-coupling.

hep-ex↗

arXiv2019

Robust Regression for Automatic Fusion Plasma Analysis based on Generative Modeling

The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely. However, it has been difficult to construct algorithms that fit all the data without over- and under-fitting. In this paper, we show that this difficulty originates from the lack of knowledge of the probability distribution that the measurement data follow. We demonstrate the use of a machine learning technique to estimate the data distribution and to construct an optimal generative model. We show that the fitting algorithm based on the generative modeling outperforms classical heuristic methods in terms of the stability as well as the accuracy.

physics.plasm-ph↗

arXiv2019

Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables

Understanding nonlinear dynamical systems (NLDSs) is challenging in a variety of engineering and scientific fields. Dynamic mode decomposition (DMD), which is a numerical algorithm for the spectral analysis of Koopman operators, has been attracting attention as a way of obtaining global modal descriptions of NLDSs without requiring explicit prior knowledge. However, since existing DMD algorithms are in principle formulated based on the concatenation of scalar observables, it is not directly applicable to data with dependent structures among observables, which take, for example, the form of a sequence of graphs. In this paper, we formulate Koopman spectral analysis for NLDSs with structures among observables and propose an estimation algorithm for this problem. This method can extract and visualize the underlying low-dimensional global dynamics of NLDSs with structures among observables from data, which can be useful in understanding the underlying dynamics of such NLDSs. To this end, we first formulate the problem of estimating spectra of the Koopman operator defined in vector-valued reproducing kernel Hilbert spaces, and then develop an estimation procedure for this problem by reformulating tensor-based DMD. As a special case of our method, we propose the method named as Graph DMD, which is a numerical algorithm for Koopman spectral analysis of graph dynamical systems, using a sequence of adjacency matrices. We investigate the empirical performance of our method by using synthetic and real-world data.

stat.ML↗

arXiv2019

Quantum Circuit Learning

We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.

quant-ph↗

arXiv2019

The International Linear Collider: A Global Project

The International Linear Collider (ILC) is now under consideration as the next global project in particle physics. In this report, we review of all aspects of the ILC program: the physics motivation, the accelerator design, the run plan, the proposed detectors, the experimental measurements on the Higgs boson, the top quark, the couplings of the W and Z bosons, and searches for new particles. We review the important role that polarized beams play in the ILC program. The first stage of the ILC is planned to be a Higgs factory at 250 GeV in the centre of mass. Energy upgrades can naturally be implemented based on the concept of a linear collider. We discuss in detail the ILC program of Higgs boson measurements and the expected precision in the determination of Higgs couplings. We compare the ILC capabilities to those of the HL-LHC and to those of other proposed e+e- Higgs factories. We emphasize throughout that the readiness of the accelerator and the estimates of ILC performance are based on detailed simulations backed by extensive RandD and, for the accelerator technology, operational experience.

hep-ex↗

arXiv2019

Sequential minimal optimization for quantum-classical hybrid algorithms

We propose a sequential minimal optimization method for quantum-classical hybrid algorithms, which converges faster, is robust against statistical error, and is hyperparameter-free. Specifically, the optimization problem of the parameterized quantum circuits is divided into solvable subproblems by considering only a subset of the parameters. In fact, if we choose a single parameter, the cost function becomes a simple sine curve with period $2π$, and hence we can exactly minimize with respect to the chosen parameter. Furthermore, even in general cases, the cost function is given by a simple sum of trigonometric functions with certain periods and hence can be minimized by using a classical computer. By repeatedly performing this procedure, we can optimize the parameterized quantum circuits so that the cost function becomes as small as possible. We perform numerical simulations and compare the proposed method with existing gradient-free and gradient-based optimization algorithms. We find that the proposed method substantially outperforms the existing optimization algorithms and converges to a solution almost independent of the initial choice of the parameters. This accelerates almost all quantum-classical hybrid algorithms readily and would be a key tool for harnessing near-term quantum devices.

quant-ph↗

arXiv2019

General framework for constructing fast and near-optimal machine-learning-based decoder of the topological stabilizer codes

Quantum error correction is an essential technique for constructing a scalable quantum computer. In order to implement quantum error correction with near-term quantum devices, a fast and near-optimal decoding method is demanded. A decoder based on machine learning is considered as one of the most viable solutions for this purpose, since its prediction is fast once training has been done, and it is applicable to any quantum error correcting code and any noise model. So far, various formulations of the decoding problem as the task of machine learning have been proposed. Here, we discuss general constructions of machine-learning-based decoders. We found several conditions to achieve near-optimal performance, and proposed a criterion which should be optimized when a size of training data set is limited. We also discuss preferable constructions of neural networks, and proposed a decoder using spatial structures of topological codes using a convolutional neural network. We numerically show that our method can improve the performance of machine-learning-based decoders in various topological codes and noise models.

quant-ph↗

arXiv2019

Study of the $hγZ$ coupling using $e^+e^-\xrightarrow{}h γ$ at the ILC

We study the $e^+e^-\to h γ$ process at the International Linear Collider (ILC) to probe new physics in the $hγZ$ coupling. The study is performed at a center of mass energy of 250 GeV and is based on the full simulation of the International Large Detector (ILD). The expected signal significance is found to be 0.53 $σ$ for an integrated luminosity of 2000 fb$^{-1}$ in the case of the standard model. The corresponding 95 $\%$ confidence level upper limit for the signal cross section is 1.08 fb for left-handed beam polarization.

hep-ex↗

arXiv2019

Study of fermion pair events at the 250 GeV ILC

Precise measurements of electroweak processes at the International Linear Collider (ILC) will provide unique opportunities to explore new physics beyond the Standard Model. Fermion pair production events are sensitive to new interactions involving a new heavy gauge boson or an electroweak interacting massive particle (EWIMP).We studied the mass reach of new particles at the ILC with $\sqrt{s}=250$ GeV by using $e^+ e^-\to e^+ e^-$and $e^+ e^-\to μ^+ μ^-$ events. We show that a mass reach for BSM particles can be determined with 90% confidence level using a toy Monte Carlo technique.

hep-ex↗

arXiv2019

The International Linear Collider. A Global Project

A large, world-wide community of physicists is working to realise an exceptional physics program of energy-frontier, electron-positron collisions with the International Linear Collider (ILC). This program will begin with a central focus on high-precision and model-independent measurements of the Higgs boson couplings. This method of searching for new physics beyond the Standard Model is orthogonal to and complements the LHC physics program. The ILC at 250 GeV will also search for direct new physics in exotic Higgs decays and in pair-production of weakly interacting particles. Polarised electron and positron beams add unique opportunities to the physics reach. The ILC can be upgraded to higher energy, enabling precision studies of the top quark and measurement of the top Yukawa coupling and the Higgs self-coupling. The key accelerator technology, superconducting radio-frequency cavities, has matured. Optimised collider and detector designs, and associated physics analyses, were presented in the ILC Technical Design Report, signed by 2400 scientists. There is a strong interest in Japan to host this international effort. A detailed review of the many aspects of the project is nearing a conclusion in Japan. Now the Japanese government is preparing for a decision on the next phase of international negotiations, that could lead to a project start within a few years. The potential timeline of the ILC project includes an initial phase of about 4 years to obtain international agreements, complete engineering design and prepare construction, and form the requisite international collaboration, followed by a construction phase of 9 years.

hep-ex↗

arXiv2019

Quantum Analog-Digital Conversion

Many quantum algorithms, such as Harrow-Hassidim-Lloyd (HHL) algorithm, depend on oracles that efficiently encode classical data into a quantum state. The encoding of the data can be categorized into two types; analog-encoding where the data are stored as amplitudes of a state, and digital-encoding where they are stored as qubit-strings. The former has been utilized to process classical data in an exponentially large space of a quantum system, where as the latter is required to perform arithmetics on a quantum computer. Quantum algorithms like HHL achieve quantum speedups with a sophisticated use of these two encodings. In this work, we present algorithms that converts these two encodings to one another. While quantum digital-to-analog conversions have implicitly been used in existing quantum algorithms, we reformulate it and give a generalized protocol that works probabilistically. On the other hand, we propose an deterministic algorithm that performs a quantum analog-to-digital conversion. These algorithms can be utilized to realize high-level quantum algorithms such as a nonlinear transformation of amplitude of a quantum state. As an example, we construct a "quantum amplitude perceptron", a quantum version of neural network, and hence has a possible application in the area of quantum machine learning.

quant-ph↗

arXiv2019

Subspace-search variational quantum eigensolver for excited states

The variational quantum eigensolver (VQE), a variational algorithm to obtain an approximated ground state of a given Hamiltonian, is an appealing application of near-term quantum computers. The original work [A. Peruzzo et al.; \textit{Nat. Commun.}; \textbf{5}, 4213 (2014)] focused only on finding a ground state, whereas the excited states can also induce interesting phenomena in molecules and materials. Calculating excited states is, in general, a more difficult task than finding ground states for classical computers. To extend the framework to excited states, we here propose an algorithm, the subspace-search variational quantum eigensolver (SSVQE). This algorithm searches a low energy subspace by supplying orthogonal input states to the variational ansatz and relies on the unitarity of transformations to ensure the orthogonality of output states. The $k$-th excited state is obtained as the highest energy state in the low energy subspace. The proposed algorithm consists only of two parameter optimization procedures and does not employ any ancilla qubits. The disuse of the ancilla qubits is a great improvement from the existing proposals for excited states, which have utilized the swap test, making our proposal a truly near-term quantum algorithm. We further generalize the SSVQE to obtain all excited states up to the $k$-th by only a single optimization procedure. From numerical simulations, we verify the proposed algorithms. This work greatly extends the applicable domain of the VQE to excited states and their related properties like a transition amplitude without sacrificing any feasibility of it.

quant-ph↗

arXiv2019

Methodology for replacing indirect measurements with direct measurements

In quantum computing, the indirect measurement of unitary operators such as the Hadamard test plays a significant role in many algorithms. However, in certain cases, the indirect measurement can be reduced to the direct measurement, where a quantum state is destructively measured. Here we investigate in what cases such a replacement is possible and develop a general methodology for trading an indirect measurement with sequential direct measurements. The results can be applied to construct quantum circuits to evaluate the analytical gradient, metric tensor, Hessian, and even higher order derivatives of a parametrized quantum state. Also, we propose a new method to measure the out-of-time-order correlator based on the presented protocol. Our protocols can reduce the depth of the quantum circuit significantly by making the controlled operation unnecessary and hence are suitable for quantum-classical hybrid algorithms on near-term quantum computers.

quant-ph↗

arXiv2018

Hydrodynamics with spacetime-dependent scattering length

Hydrodynamics provides a concise but powerful description of long-time and long-distance physics of correlated systems out of thermodynamic equilibrium. Here we construct hydrodynamic equations for nonrelativistic particles with a spacetime-dependent scattering length and show that it enters constitutive relations uniquely so as to represent the fluid expansion and contraction in both normal and superfluid phases. As a consequence, we find that a leading dissipative correction to the contact density due to the spacetime-dependent scattering length is proportional to the bulk viscosity ($ζ_2$ in the superfluid phase). Also, when the scattering length is slowly varied over time in a uniform system, the entropy density is found to be produced even without fluid flows in proportion to the bulk viscosity, which may be useful as a novel probe to measure the bulk viscosity in ultracold-atom experiments.

cond-mat.quant-gas↗