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Yutaka Shikano

Publications and source records attributed to Yutaka Shikano.

At least 19 recordsLinked to original sources

Atmospheric Newtonian noise in torsion-balance measurements of the gravitational constant $G$

Measurements of Newton's gravitational constant remain limited by environmental and apparatus-dependent systematics whose treatment is often less explicit than that of instrumental noise. Among these, atmospheric density fluctuations generate unshieldable gravity gradients that couple directly to torsion-balance observables as atmospheric Newtonian noise. Here, we develop a GUM-consistent framework for propagating this contribution through the torque estimator into the uncertainty budget of torsion-balance measurements. We derive closed-form spatial transfer functions for two benchmark geometries: a two-mass dumbbell, which provides an upper-coupling reference, and a perfectly symmetric cross, which serves as an idealized rejection limit and exposes the trade-off between suppressing low-order environmental coupling and preserving signal response. We also separate stationary correlated inputs from non-stationary baseline drift, using the Ornstein--Uhlenbeck process only as a benchmark for the former. Atmospheric pressure benchmarks indicate that the resulting background contribution is below present reference uncertainty levels, but can become relevant as systematic floors approach the part-per-million regime. This framework provides a practical route for incorporating site-specific environmental gravity gradients into future torsion-balance uncertainty budgets.

gr-qc

Torsion balances as operational probes of semiclassical gravity: Matched-filter bounds, torque-diffusion constraints, and quantum-noise benchmarks

Calibrated torsion-balance spectra constrain deterministic and stochastic deviations from standard Newtonian gravity. Using one-sided spectra, we derive a calibrated angle-equivalent noise budget and finite-time matched-filter/Cramér-Rao bounds for known torque templates. For stochastic models, subtracting the calibrated standard noise budget (thermal, Newtonian, environmental, imprecision, backaction) from the observed angle spectrum yields a residual spectrum, convertible to an equivalent residual torque spectrum via calibrated torsional susceptibility. This bounds additional stationary stochastic torque noise. This frequency-resolved bound compresses to a single torque-diffusion coefficient, $D_τ$, only in the Markovian white-noise limit; non-Markovian or colored models require the full residual spectrum. Page--Geilker branch discrimination and Fedida-Kent mixture-equivalence tests address distinct physical questions, but upon projection onto torque templates, both reduce to the same statistical matched-filter discrimination problem. For a room-temperature Cavendish benchmark, resonant thermal angle ASD is $1.36\times10^{-4}\,\mathrm{rad}/\sqrt{\mathrm{Hz}}$, while measurement-added SQL is $3.25\times10^{-12}\,\mathrm{rad}/\sqrt{\mathrm{Hz}}$. For the Yan \emph{et al.} search, the reported $0.3\,μ\mathrm{rad}/\sqrt{\mathrm{Hz}}$ sensitivity at $2.5\,\mathrm{mHz}$ yields a conservative bound $D_τ\lesssim 2.4\times10^{-23}\,\mathrm{N^2\,m^2\,s}$, assuming white torque noise. These formulations provide an interface linking calibrated torsion-balance data, deterministic tests, and stochastic semiclassical-gravity searches, without asserting a direct test of the full relativistic semiclassical Einstein equation.

gr-qc

Engineering Nanodiamonds for Quantum Sensing: Material Constraints at the Nanoscale

Optically addressable solid-state spin defects have emerged as powerful multimodal quantum sensors, with nitrogen-vacancy (NV) centers in bulk diamond providing benchmark quantum control and sensitivity under ambient conditions. Embedding such defects in nanodiamonds (NDs) extends these capabilities to mobile probes capable of accessing complex biological and nanoscale environments. Reduced dimensions, however, introduce constraints beyond volumetric spin impurities, notably enhanced lattice strain and surface-induced noise sources, which shorten NV spin relaxation times (T1 and T2) and destabilize the NV charge state, as well as resulting in pronounced particle-to-particle variability in NDs typically produced by top-down approaches. These effects complicate both sensing performance and the quantitative interpretation of multimodal signals in realistic environments. This article provides a structured perspective on the physical mechanisms by which material properties constrain NV behavior in NDs, together with mitigation strategies that shape the robust use of these mobile quantum sensors for biosensing and nanoscale science.

quant-ph

Analytical study of birefringent cavities for axion-like dark matter search

Light polarization plays a crucial role in optical-cavity experiments; however, mirror birefringence presents a significant challenge that must be addressed carefully. In this study, a rigorous, nonperturbative framework is developed to quantify birefringence effects by incorporating variations in reflectance and polarization misalignment. We analyze the impact of this framework on the sensitivity of axion-like particle (ALP) dark-matter searches. The results show that both birefringence and misalignment contribute to sensitivity degradation in the low-mass regime; however, the adverse effects of misalignment can be mitigated by selecting a postselection angle greater than the misalignment angle. Furthermore, birefringence produces an additional resonance peak in the high-mass region, which remains largely unaffected by misalignment and postselection variations. This rigorous framework underscores the importance of considering birefringence in high-precision optical-cavity experiments for ALP detection.

hep-ph

Glitch noise classification in KAGRA O3GK observing data using unsupervised machine learning

Gravitational wave interferometers are disrupted by various types of nonstationary noise, referred to as glitch noise, that affect data analysis and interferometer sensitivity. The accurate identification and classification of glitch noise are essential for improving the reliability of gravitational wave observations. In this study, we demonstrated the effectiveness of unsupervised machine learning for classifying images with nonstationary noise in the KAGRA O3GK data. Using a variational autoencoder (VAE) combined with spectral clustering, we identified eight distinct glitch noise categories. The latent variables obtained from VAE were dimensionally compressed, visualized in three-dimensional space, and classified using spectral clustering to better understand the glitch noise characteristics of KAGRA during the O3GK period. Our results highlight the potential of unsupervised learning for efficient glitch noise classification, which may in turn potentially facilitate interferometer upgrades and the development of future third-generation gravitational wave observatories.

gr-qc

Quantum-grade nanodiamonds for ultrabright spin detection in live cells

Optically accessible spin-active nanomaterials are promising as quantum nanosensors for probing biological samples. However, achieving bioimaging-level brightness and high-quality spin properties for these materials is challenging and hinders their application in quantum biosensing. Here, we demonstrate ultrabright fluorescent nanodiamonds (NDs) containing 0.6-1.3-ppm nitrogen-vacancy (NV) centers by spin-environment engineering via enriching spin-less 12C-carbon isotopes and reducing substitutional nitrogen spin impurities. The NDs, readily introduced into cultured cells, exhibited substantially narrow optically detected magnetic resonance (ODMR) spectra, requiring 16-times less microwave excitation power to give an ODMR depth comparable to that of conventional type-Ib NDs. They show average spin-relaxation times of T1 = 0.68 ms and T_2 = 1.6 us (1.6 ms and 2.7 us maximum) that were 5- and 11-fold longer than those of type-Ib, respectively. The bulk-like NV spin properties and bright fluorescence demonstrated in this study significantly improve the sensitivity of ND-based quantum sensors for biological applications.

physics.app-ph

Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time-frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised-learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised-learning architecture of the previous study is examined and reported.

gr-qc

Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors

In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results in problems such as detector instability and the hiding and/or imitation of gravitational-wave signals. This transient noise has various characteristics in the time--frequency representation, which is considered to be associated with environmental and instrumental origins. Classification of transient noise can offer clues for exploring its origin and improving the performance of the detector. One approach for accomplishing this is supervised learning. However, in general, supervised learning requires annotation of the training data, and there are issues with ensuring objectivity in the classification and its corresponding new classes. By contrast, unsupervised learning can reduce the annotation work for the training data and ensure objectivity in the classification and its corresponding new classes. In this study, we propose an unsupervised learning architecture for the classification of transient noise that combines a variational autoencoder and invariant information clustering. To evaluate the effectiveness of the proposed architecture, we used the dataset (time--frequency two-dimensional spectrogram images and labels) of the Laser Interferometer Gravitational-wave Observatory (LIGO) first observation run prepared by the Gravity Spy project. The classes provided by our proposed unsupervised learning architecture were consistent with the labels annotated by the Gravity Spy project, which manifests the potential for the existence of unrevealed classes.

gr-qc

Axion search with quantum nondemolition detection of magnons

The axion provides a solution for the strong CP problem and is one of the leading candidates for dark matter. This paper proposes an axion detection scheme based on quantum nondemolition detection of magnon, i.e., quanta of collective spin excitations in solid, which is expected to be excited by the axion-electron interaction predicted by the Dine-Fischer-Srednicki-Zhitnitsky (DFSZ) model. The prototype detector is composed of a ferrimagnetic sphere as an electronic spin target and a superconducting qubit. Both of these are embedded inside a microwave cavity, which leads to a coherent effective interaction between the uniform magnetostatic mode in the ferrimagnetic crystal and the qubit. An upper limit for the coupling constant between an axion and an electron is obtained as $g_{aee}<2.6\times10^{-6}$ at the 95% confidence level for the axion mass of $33.117$$μ$eV $<m_{a}<33.130$$μ$eV.

hep-ex

Quantum walks driven by quantum coins with two multiple eigenvalues

We consider a spectral analysis on the quantum walks on graph $G=(V,E)$ with the local coin operators $\{C_u\}_{u\in V}$ and the flip flop shift. The quantum coin operators have commonly two distinct eigenvalues $κ,κ'$ and $p=\dim(\ker(κ-C_u))$ for any $u\in V$ with $1\leq p\leq δ(G)$, where $δ(G)$ is the minimum degrees of $G$. We show that this quantum walk can be decomposed into a cellular automaton on $\ell^2(V;\mathbb{C}^p)$ whose time evolution is described by a self adjoint operator $T$ and its remainder. We obtain how the eigenvalues and its eigenspace of $T$ are lifted up to as those of the original quantum walk. As an application, we express the eigenpolynomial of the Grover walk on $\mathbb{Z}^d$ with the moving shift in the Fourier space.

math-ph

Diamond quantum thermometry: From foundations to applications

Diamond quantum thermometry exploits the optical and electrical spin properties of colour defect centres in diamonds and, acts as a quantum sensing method exhibiting ultrahigh precision and robustness. Compared to the existing luminescent nanothermometry techniques, a diamond quantum thermometer can be operated over a wide temperature range and a sensor spatial scale ranging from nanometres to micrometres. Further, diamond quantum thermometry is employed in several application, including electronics and biology, to explore these fields with nanoscale temperature measurements. This review covers the operational principles of diamond quantum thermometry for spin-based and all-optical methods, material development of diamonds with a focus on thermometry, and examples of applications in electrical and biological systems with demand-based technological requirements.

cond-mat.mes-hall

Post-Hartree-Fock method in Quantum Chemistry for Quantum Computer

Quantum computational chemistry is a potential application of quantum computers that is expected to effectively solve several quantum-chemistry problems, particularly the electronic structure problem. Quantum computational chemistry can be compared to the conventional computational devices. This review comprehensively investigates the applications and overview of quantum computational chemistry, including a review of the Hartree-Fock method for quantum information scientists. Quantum algorithms, quantum phase estimation, and variational quantum eigensolver, have been applied to the post-Hartree-Fock method.

quant-ph

Wide-field fluorescent nanodiamond spin measurements toward real-time large-area intracellular quantum thermometry

In this study, we analyze the operational process of nanodiamond (ND) quantum thermometry based on wide-field detection of optically detected magnetic resonance (ODMR) of nitrogen vacancy centers, and compare its performance with that of confocal ODMR detection. We found that (1) the thermometry results are significantly affected by the shape and size of the camera region of interest (ROI) surrounding the target NDs and that (2) by properly managing the ROI and acquisition parameters of the camera, a temperature precision comparable to confocal detection in living cells can be obtained by wide-field ODMR. Our results are significant to the development of camera-based real-time large-area quantum thermometry of living cells.

physics.app-ph

Real-time estimation of the optically detected magnetic resonance shift in diamond quantum thermometry

We investigate the real-time estimation protocols for the frequency shift of optically detected magnetic resonance (ODMR) of nitrogen-vacancy (NV) centers in nanodiamonds (NDs). Efficiently integrating multipoint ODMR measurements and ND particle tracking into fluorescence microscopy has recently demonstrated stable monitoring of the temperature inside living animals. We analyze the multipoint ODMR measurement techniques (3-, 4-, and 6-point methods) in detail and quantify the amount of measurement artifact owing to several systematic errors derived from instrumental errors of experimental hardware and ODMR spectral shape. We propose a practical approach to minimize the effect of these factors, which allows for measuring accurate temperatures of single NDs during dynamic thermal events. We also discuss integration of noise filters, data estimation protocols, and possible artifacts for further developments in real-time temperature estimation. The present study provides technical details of quantum diamond thermometry and discusses factors that may affect the temperature estimation in biological applications.

physics.app-ph

Detecting Temporal Correlation via Quantum Random Number Generation

All computing devices, including quantum computers, must exhibit that for a given input, an output is produced in accordance with the program. The outputs generated by quantum computers that fulfill these requirements are not temporally correlated, however. In a quantum-computing device comprising solid-state qubits such as superconducting qubits, any operation to rest the qubits to their initial state faces a practical problem. We applied a statistical analysis to a collection of random numbers output from a 20-qubit superconducting-qubit cloud quantum computer using the simplest random number generation scheme. The analysis indicates temporal correlation in the output of some sequences obtained from the 20 qubits. This temporal correlation is not related to the relaxation time of each qubit. We conclude that the correlation could be a result of a systematic error.

quant-ph

Proceedings 9th International Conference on Quantum Simulation and Quantum Walks

This volume contains a selection of papers presented at the 9th in a series of international conferences on Quantum Simulation and Quantum Walks (QSQW). During this event, we worked on the development of theories based upon quantum walks and quantum simulation models, in order to solve interrelated problems concerning the simulation of standard quantum field theory, quantum gravity and cosmological models, dissipative quantum computing, searching on complex quantum networks, and the topological classification of multi-particle quantum walks.

quant-ph

Real-time nanodiamond thermometry probing in-vivo thermogenic responses

Real-time temperature monitoring inside living organisms provides a direct measure of their biological activities, such as homeostatic thermoregulation and energy metabolism. However, it is challenging to reduce the size of bio-compatible thermometers down to submicrometers despite their potential applications for the thermal imaging of subtissue structures with single-cell resolution. Light-emitting nanothermometers that remotely sense temperature via optical signals exhibit considerable potential in such \textit{in-vivo} high-spatial-resolution thermometry. Here, using quantum nanothermometers based on optically accessible electron spins in nanodiamonds (NDs), we demonstrate \textit{in-vivo} real-time temperature monitoring inside \textit{Caenorhabditis elegans} (\textit{C. elegans}) worms. We developed a thermometry system that can measure the temperatures of movable NDs inside live adult worms with a precision of $\pm 0.22^{\circ}{\rm C}$. Using this system, we determined the increase in temperature based on the thermogenic responses of the worms during the chemical stimuli of mitochondrial uncouplers. Our technique demonstrates sub-micrometer localization of real-time temperature information in living animals and direct identification of their pharmacological thermogenesis. The results obtained facilitate the development of a method to probe subcellular temperature variation inside living organisms and may allow for quantification of their biological activities based on their energy expenditures.

q-bio.QM

Concatenated Composite Pulses Applied to Liquid-State Nuclear Magnetic Resonance Spectroscopy

The error-robust and short composite operations named ConCatenated Composite Pulses (CCCPs), developed as high-precision unitary operations in quantum information processing (QIP), are derived from composite pulses widely employed in nuclear magnetic resonance (NMR). CCCPs simultaneously compensate for two types of systematic errors, which was not possible with the known composite pulses in NMR. Our experiments demonstrate that CCCPs are powerful and versatile tools not only in QIP but also in NMR.

physics.chem-ph