arXiv subjects

Ryo Nagai

Publications and source records attributed to Ryo Nagai.

At least 19 recordsLinked to original sources

arXiv2026

Smooth velocity shuttling for suppressing valley excitations in disordered Si/SiGe quantum dots

Coherent electron shuttling is a key requirement for realizing scalable silicon quantum computing architectures. However, in silicon qubits, the existence of nearly degenerate conduction-band valleys poses a significant challenge because non-adiabatic transitions to excited valley states cause spin dephasing via spin-valley mixing. In this paper, we propose a smooth velocity shuttling protocol to suppress these valley excitations. By mapping the time-domain design of the shuttling velocity profile onto the design problem of window functions in signal processing, we establish an analytical and intuitive design guideline that does not require computationally expensive numerical optimization. We demonstrate that the high-frequency sidelobes of the shuttling velocity spectrum can be effectively suppressed by applying a frequency-modulated gate voltage based on the Tukey window. Through statistical numerical simulations incorporating realistic spatial randomness of the valley landscape, we show that the proposed smooth velocity control significantly reduces the average spin infidelity in the moderate-to-low disorder regime ($|\Delta_0|/\sigma_\Delta \simeq \mathcal{O}(1)$). Our results underscore that this simple, control-level velocity shaping provides a robust pathway toward high-fidelity spin transport in large-scale silicon quantum processors.

quant-ph↗

arXiv2026

Surface-Code Thresholds and Qubit Footprints in Shuttling-Based Spin-Qubit Railways

We present a fault-tolerant mapping of rotated surface codes onto a $2\times N$ silicon spin-qubit railway architecture, utilizing electron shuttling to resolve the wiring fan-out bottleneck. Employing circuit-level noise modeling, we evaluate threshold performances across various noise biases. We demonstrate that shuttling check qubits instead of data qubits fundamentally improves system thresholds. Crucially, under a noise model biased towards dephasing for spin-qubit shuttling, the non-CSS XZZX surface code outperforms standard CSS variants. By tailoring the topological code to this specific inherent bias, we show that the Megaquop footprint is achievable with a distance 7 code requiring a $p = 10^{-3}$ physical error rate, highlighting a pathway for substantial hardware reductions in early fault-tolerant quantum processors.

quant-ph↗

arXiv2026

Quantum Riemannian Hamiltonian Descent

We propose Quantum Riemannian Hamiltonian Descent (QRHD), a quantum algorithm for continuous optimization on Riemannian manifolds that extends Quantum Hamiltonian Descent (QHD) by incorporating geometric structure of the parameter space via a position-dependent metric in the kinetic term. We formulate QRHD at both operator and path integral formalisms and derive the corresponding quantum equations of motion, showing that quantum corrections appear in the action integral but they are suppressed at late times by the time-dependent dissipation factor. This implies that convergence near optimal points is controlled by the classical potential while quantum effects influence early-time dynamics. By analyzing the semiclassical equation, we estimate a lower bound on the convergence time and numerically demonstrate whether QRHD work as a quantum optimization algorithm in some examples. A quantum circuit implementation based on time-dependent Hamiltonian simulation is also discussed and the query complexity is estimated.

quant-ph↗

arXiv2025

Digital-Controlled Method of Conveyor-Belt Spin Shuttling in Silicon for Large-Scale Quantum Computation

We propose a digital-controlled conveyor-belt shuttling method for silicon-based quantum processors, addressing the scalability challenges of conventional analog sinusoidal implementations. By placing a switch matrix and low-pass filters in a cryogenic environment, our approach synthesizes near-sinusoidal waveforms from a limited number of DC voltage levels. Simulation results demonstrate that the proposed method achieves fidelity comparable to analog methods while significantly reducing wiring overhead and power dissipation. Moreover, the design offers robustness against device-level variations, enabling large-scale integration of high-fidelity spin shuttling for quantum error correction.

quant-ph↗

arXiv2023

Quantum channel decomposition with pre- and post-selection

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.

quant-ph↗

arXiv2023

Anapole Moment of Majorana Fermions and Implications for Direct Detection of Neutralino Dark Matter

For Majorana fermions the anapole moment is the only allowed electromagnetic multipole moment. In this work we calculate the anapole moment induced at one-loop by the Yukawa and gauge interactions of a Majorana fermion, using the pinch technique to ensure the finiteness and gauge-invariance of the result. As archetypical example of a Majorana fermion, we calculate the anapole moment for the lightest neutralino in the Minimal Supersymmetric Standard Model, and specifically in the bino, wino and higgsino limits. Finally, we briefly discuss the implications of the anapole moment for the direct detection of dark matter in the form of Majorana fermions.

hep-ph↗

arXiv2022

Development of exchange-correlation functionals assisted by machine learning

With the recent rapid progress in the machine-learning (ML), there have emerged a new approach using the ML methods to the exchange-correlation functional of density functional theory. In this chapter, we review how the ML tools are used for this and the performances achieved recently. It is revealed that the ML, not being opposed to the analytical methods, complements the human intuition and advance the development toward the first-principles calculation with desired accuracy.

cond-mat.mtrl-sci↗

arXiv2022

Lepton flavor violations in SUSY models for muon $g-2$ with right-handed neutrinos

We consider supersymmetric (SUSY) models for the muon $g-2$ anomaly without flavor violating masses at the tree-level. The models can avoid LHC constraints and the vacuum stability constraint in the stau-Higgs potential. Although large flavor violating processes are not induced within the framework of minimal SUSY standard model, once we adopt a seesaw model, sizable lepton flavor violating (LFV) processes such as $μ\to e γ$ and $μ\to e$ conversion are induced. These LFV processes will be observed at future experiments such as MEG-II, COMET and Mu2e if right-handed neutrinos are heavier than $10^9$ GeV motivated by the successful leptogenesis. This conclusion is somewhat model independent since Higgs doublets are required to have large soft SUSY breaking masses, leading to flavor violations in a slepton sector via neutrino Yukawa interactions.

hep-ph↗

arXiv2022

Electroweak phase transition in the nearly aligned Higgs effective field theory

We investigate the strongly first-order electroweak phase transition using an effective field theoretical approach. The standard effective field theory with finite number truncation of higher dimensional operators fails in the typical parameter space where the strongly first-order phase transition is realized because it cannot describe the non-decoupling quantum effect of new physics beyond the standard model. To parameterize the non-decoupling quantum effect, we employ the nearly aligned Higgs effective theory in which the Higgs potential is parameterized by a Coleman-Weinberg like form. Extending this framework with finite temperature corrections, we study the parameter space for realizing the strongly first-order phase transition, and estimate the gravitational wave produced at the phase transition.

hep-ph↗

arXiv2021

Machine-Learning-Based Exchange-Correlation Functional with Physical Asymptotic Constraints

Density functional theory is the standard theory for computing the electronic structure of materials, which is based on a functional that maps the electron density to the energy. However, a rigorous form of the functional is not known and has been heuristically constructed by interpolating asymptotic constraints known for extreme situations, such as isolated atoms and uniform electron gas. Recent studies have demonstrated that the functional can be effectively approximated using machine learning (ML) approaches. However, most ML models do not satisfy asymptotic constraints. In this study, by applying a novel ML model architecture, we demonstrate a neural network-based exchange-correlation functional satisfying physical asymptotic constraints. Calculations reveal that the trained functional is applicable to various materials with an accuracy higher than that of existing functionals, even for materials whose electronic properties are different from the properties of materials in the training dataset. Our proposed approach thus improves the accuracy and generalization performance of the ML-based functional by combining the advantages of ML and analytical modeling.

cond-mat.mtrl-sci↗

arXiv2021

A new Higgs effective field theory and the new no-lose theorem

Non-decoupling effects of heavy new particles cannot be described by the standard effective field theory with finite truncation of higher dimensional operators. We propose a new effective field theory in which non-decoupling quantum effects of new physics are correctly described. We discuss vacuum stability and perturbative unitarity in our effective field theory, and we find that the scale of new physics can be estimated if we will observe the Higgs coupling deviation via non-decoupling effects in future collider experiments.

hep-ph↗

arXiv2021

Performance of the D-Egg Optical Sensor for the IceCube Upgrade

New optical sensors called the "D-Egg" have been developed for cost-effective instrumentation for the IceCube Upgrade. With two 8-inch high quantum efficient photomultiplier tubes (PMTs), they offer increased effective photocathode area while retaining as much of the successful IceCube Digital Optical Module design as possible. Mass production of D-Eggs has started in 2020. By the end of 2021, there will be 310 D-Eggs produced with 288 deployed in the IceCube Upgrade. The D-Egg readout system uses advanced technologies in electronics and computing power. Each of the two PMT signals is digitised using ultra-low-power 14-bit ADCs with a sampling frequency of 240 megaSPS, enabling seamless and lossless event recording from single-photon signals to signals exceeding 200 PE within 10 nanosecond, as well as flexible event triggering. In this paper, we report the single photon detection performance as well as the multiple photon recording capability of D-Eggs from the mass production line which have been evaluated with the built-in data acquisition system.

physics.ins-det↗

arXiv2021

CP-Violation in a Composite 2-Higgs Doublet Model

We study CP-Violation (CPV) in a Composite 2-Higgs Doublet Model (C2HDM) based on the global symmetry breaking $SO(6)/[SO(4)\times SO(2)]$, wherein the strong sector is modeled by a two-site moose structure. Non-trivial complex phases in the interactions involving fermions in both the elementary and strong sectors can induce CPV in the Higgs potential as well as the Yukawa coupling parameters. We compute both of the latter and analyse their dependence upon the aforementioned complex phases. Finally, we discuss physics observables which are distinctive of this model. Even in the simplest case with only one complex phase in the strong sector we can get significant CPV effects.

hep-ph↗

arXiv2021

Scalar and fermion on-shell amplitudes in generalized Higgs effective field theory

Beyond standard model (BSM) particles should be included in effective field theory in order to compute the scattering amplitudes involving these extra particles. We formulate an extension of Higgs effective field theory which contains arbitrary number of scalar and fermion fields with arbitrary electric and chromoelectric charges. The BSM Higgs sector is described by using the non-linear sigma model in a manner consistent with the spontaneous electroweak symmetry breaking. The chiral order counting rule is arranged consistently with the loop expansion. The leading order Lagrangian is organized in accord with the chiral order counting rule. We use a geometrical language to describe the particle interactions. The parametrization redundancy in the effective Lagrangian is resolved by describing the on-shell scattering amplitudes only with the covariant quantities in the scalar/fermion field space. We introduce a useful coordinate (normal coordinate), which simplifies the computations of the on-shell amplitudes significantly. We show the high energy behaviors of the scattering amplitudes determine the "curvature tensors" in the scalar/fermion field space. The massive spinor-wavefunction formalism is shown to be useful in the computations of on-shell helicity amplitudes.

hep-ph↗

arXiv2020

Direct detection of vector dark matter through electromagnetic multipoles

Dark matter particles, even if they are electrically neutral, could interact with the Standard Model particles via their electromagnetic multipole moments. In this paper, we focus on the electromagnetic properties of the complex vector dark matter candidate, which can be described by means of seven form factors. We calculate the differential scattering cross-section with nuclei due to the interactions of the dark matter and nuclear multipole moments, and we derive upper limits on the former from the non-observation of dark matter signals in direct detection experiments. We also present a model where the dark matter particle is a gauge boson of a dark $SU(2)$ symmetry, and which contains heavy new fermions, charged both under the dark $SU(2)$ symmetry and under the electromagnetic $U(1)$ symmetry. The new fermions induce at the one loop level electromagnetic multipole moments, which could lead to detectable signals in direct detection experiments.

hep-ph↗

arXiv2020

Machine Learning Exchange-Correlation Potential in Time Dependent Density Functional Theory

We propose a machine learning based approach to develop the exchange-correlation potential of time dependent density functional theory (TDDFT). The neural network projection from the time-varying electron densities to the corresponding correlation potentials in the time-dependent Kohn-Sham equation is trained using a few exact datasets for a model system of electron-hydrogen scattering. We demonstrate that this neural network potential can capture the complex structures in the time-dependent correlation potential during the scattering process and provide correct scattering probabilities, which are not obtained by the standard adiabatic functionals. We also show that it is possible to incorporate the nonadiabatic (or memory) effect in the potential with this machine learning technique, which significantly improves the accuracy of the dynamics. The method developed here offers a novel way to improve the exchange-correlation potential of TDDFT, which makes the theory a more powerful tool to study various excited state phenomena.

physics.comp-ph↗

arXiv2020

Oscillating Composite Asymmetric Dark Matter

The asymmetric dark matter (ADM) scenario can solve the coincidence problem between the baryon and the dark matter (DM) abundance when the DM mass is of ${\cal O}(1)\,$GeV. In the ADM scenarios, composite dark matter is particularly motivated, as it can naturally provide the DM mass in the ${\cal O}(1)\,$GeV range and a large annihilation cross section simultaneously. In this paper, we discuss the indirect detection constraints on the composite ADM model. The portal operators connecting the $B-L$ asymmetries in the dark and the Standard Model(SM) sectors are assumed to be generated in association with the seesaw mechanism. In this model, composite dark matter inevitably obtains a tiny Majorana mass which induces a pair-annihilation of ADM at late times. We show that the model can be efficiently tested by the searches for the $γ$-ray from the dwarf spheroidal galaxies and the interstellar electron/positron flux.

hep-ph↗

arXiv2019

Completing density functional theory by machine-learning hidden messages from molecules

Kohn-Sham density functional theory is the base of modern computational approaches to electronic structures. Their accuracy vitally relies on the exchange-correlation energy functional, which encapsulates electron-electron interaction beyond the classical one. The functional provides a way to obtain the density and energy without solving the many-body equation and can, in principle, be determined to reproduce the exact ones universally. However, the past approaches are dependent on the theoretical development, which limits the possibility of the functional to human's intuition. Here, we demonstrate a systematic way to machine-learn a functional from a database, without complicated assumptions. The density and energy are related with a flexible feed-forward neural network, which is trained to reproduce accurate dataset, and the KS-DFT is solved by taking the functional derivatives with the back-propagation technique. Surprisingly, a trial functional, trained for just a few molecules, has been shown to be applicable to hundreds of molecular systems with comparable accuracy to the standard functionals. Also, by adding the nodes connected to the hidden layers, a non-local term is straightforwardly included to improve accuracy, which has been hitherto impractically difficult. Utilizing the strategy of rapidly advancing machine learning techniques, this novel approach is expected to enrich the DFT framework by constructing a functional just from a database for materials conventionally difficult to calculate accurately.

physics.comp-ph↗