arXiv subjects

Shunsuke Daimon

Publications and source records attributed to Shunsuke Daimon.

At least 19 recordsLinked to original sources

arXiv2026

Quantum electrometry in a silicon carbide power device

For high-bias operation devices such as silicon carbide (SiC) power devices, early detection of failure mechanisms is essential to ensure reliability. This requires a method to map high electric fields with high spatial resolution, which has not been realized until now. Here we report that the silicon vacancy (Vsi) in SiC has outstanding characteristics for detecting electric fields applied in various directions within a high-biased SiC device. Vsi exhibits an equivalent response to electric field components parallel (Epara) and perpendicular (Eperp) to the c-axis, a feature unique among quantum sensors, and the responsiveness to Epara and Eperp enables detection of arbitrary electric fields encountered in cutting-edge SiC power devices. We confirmed high electric field detection of ~2.3 MV/cm, which is ~90% of the breakdown electric field of a 4H-SiC with typical carrier concentration. Selectively formed Vsi enables high-resolution mapping of electric field distribution. Vsi-based quantum sensors bring data-driven research and development methodologies as well as device degradation diagnosis.

quant-ph↗

arXiv2026

Approximate Amplitude Encoding with the Adaptive Interpolating Quantum Transform

Amplitude encoding of real-world data on quantum computers is often the workflow bottleneck: direct amplitude encoding scales poorly with input size and can offset any speedups in subsequent processing. Fourier-based sparse amplitude encoding lowers cost by retaining only a small subset of dominant coefficients, but its fixed, non-adaptive basis leads to significant information loss. In this work, we replace the Fourier transform with the adaptive interpolating quantum transform (AIQT) in the sparse amplitude encoding workflow. The AIQT learns a data-adapted basis that concentrates information into a small number of coefficients. Consequently, at matched sparsity, the AIQT retains more information and achieves lower reconstruction error compared to the Fourier baseline. On financial time-series data, the AIQT reduces reconstruction error by 40% relative to the Fourier baseline, and on image datasets the reduction is up to 50% at the same sparsity level, with nearly identical encoding gate cost. Crucially, the approach preserves the efficiency of Fourier-based methods: the AIQT is built on the structure of the quantum Fourier transform circuit. Its gate count scales quadratically with the number of qubits, while classical evaluation can be carried out in quasilinear time. In addition, the AIQT is trained without labels and does not require sampling from quantum hardware or a simulator, removing a major bottleneck in data-driven amplitude-encoding methods.

quant-ph↗

arXiv2025

Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning

Machine learning on quantum computers has attracted attention for its potential to deliver computational speedups in different tasks. However, deep variational quantum circuits require a large number of trainable parameters that grows with both qubit count and circuit depth, often rendering training infeasible. In this study, we introduce the Adaptive Interpolating Quantum Transform (AIQT), a quantum-native framework for flexible and efficient learning. AIQT defines a trainable unitary that interpolates between quantum transforms, such as the Hadamard and quantum Fourier transforms. This approach enables expressive quantum state manipulation while controlling parameter overhead. It also allows AIQT to inherit any quantum advantages present in its constituent transforms. Our results show that AIQT achieves high performance with minimal parameter count, offering a scalable and interpretable alternative to deep variational circuits.

quant-ph↗

arXiv2025

Diffusion model for analyzing quantum fingerprints in conductance fluctuation

A conditional diffusion model has been developed to analyze intricate conductance fluctuations called universal conductance fluctuations or quantum fingerprints appearing in quantum transport phenomena. The model reconstructs impurity arrangements and quantum interference patterns in nanometals by using magnetoconductance data, providing a novel approach to analyze complex data based on machine learning. In addition, we visualize the attention weights in the model, which efficiently extract information on the non-local correlation of the electron wave functions, and the score functions, which represent the force fields in the wave-function space.

cond-mat.mes-hall↗

arXiv2025

General Transform: A Unified Framework for Adaptive Transform to Enhance Representations

Discrete transforms, such as the discrete Fourier transform, are widely used in machine learning to improve model performance by extracting meaningful features. However, with numerous transforms available, selecting an appropriate one often depends on understanding the dataset's properties, making the approach less effective when such knowledge is unavailable. In this work, we propose General Transform (GT), an adaptive transform-based representation designed for machine learning applications. Unlike conventional transforms, GT learns data-driven mapping tailored to the dataset and task of interest. Here, we demonstrate that models incorporating GT outperform conventional transform-based approaches across computer vision and natural language processing tasks, highlighting its effectiveness in diverse learning scenarios.

cs.LG↗

arXiv2025

Towards Improved Quantum Machine Learning for Molecular Force Fields

This study explores the use of equivariant quantum neural networks (QNN) for generating molecular force fields, focusing on the rMD17 dataset. We consider a QNN architecture based on previous research and point out shortcomings in the parametrization of the atomic environments. These shortcomings limit its expressivity as an interatomic potential and precludes transferability between molecules. We propose a revised QNN architecture that addresses these shortcomings. While both QNNs show promise in force prediction, with the revised architecture showing improved accuracy, they struggle with energy prediction. Further, both QNNs architectures fail to demonstrate a meaningful scaling law of decreasing errors with increasing training data. These findings highlight the challenges of scaling QNNs for complex molecular systems and emphasize the need for improved encoding strategies, regularization techniques, and hybrid quantum-classical approaches.

physics.chem-ph↗

arXiv2024

Negative longitudinal resistance of monolayer graphene in the quantum Hall regime

In the quantum Hall regime the charge current is carried by ideal one-dimensional edge channels where the backscattering is prohibited by topology. This results in the constant potential along the edge of the Hall bar leading to zero 4-terminal longitudinal resistance r_xx. Finite scattering between the counter-propagating edge states, when the topological protection is broken, commonly results in r_xx > 0. However, a local disorder, if allowing intersection of the edge states, can result in a counter-intuitive scenario when r_xx<0. In this work we report the observation and a systematic study of such unconventional negative longitudinal resistance seen in an encapsulated monolayer graphene Hall bar device measured in the quantum Hall regime. We supplement our findings with the numerical calculations which allow us to outline the conditions necessary for the appearance of negative r_xx and to exclude the macroscopic disorder (contamination bubble) as the main origin of it.

cond-mat.mes-hall↗

arXiv2024

Qubit encoding for a mixture of localized functions

One of the crucial generic techniques for quantum computation is amplitude encoding. Although several approaches have been proposed, each of them often requires exponential classical-computational cost or an oracle whose explicit construction is not provided. Given the growing demands for practical quantum computation, we develop moderately specialized encoding techniques that generate an arbitrary linear combination of localized complex functions. We demonstrate that $n_{\mathrm{loc}}$ discrete Lorentzian functions as an expansion basis set lead to eficient probabilistic encoding, whose computational time is $\mathcal{O}( \max ( n_{\mathrm{loc}}^2 \log n_{\mathrm{loc}},n_{\mathrm{loc}}^2 \log n_q, n_q ))$ for $n_q$ data qubits equipped with $\log_2 n_{\mathrm{loc}}$ ancillae. Furthermore, amplitude amplification in combination with amplitude reduction renders it deterministic analytically with controllable errors and the computational time is reduced to $\mathcal{O}( \max ( n_{\mathrm{loc}}^{3/2} \log n_{\mathrm{loc}}, n_{\mathrm{loc}}^{3/2} \log n_q, n_q )).$ We estimate required resources for applying our scheme to quantum chemistry in real space. We also show the results on real superconducting quantum computers to confirm the validity of our techniques.

quant-ph↗

arXiv2024

Dynamics of measurement-induced state transitions in superconducting qubits

We have investigated temporal fluctuation of superconducting qubits via the time-resolved measurement for an IBM Quantum system. We found that the qubit error rate abruptly changes during specific time intervals. Each high error state persists for several tens of seconds, and exhibits an on-off behavior. The observed temporal instability can be attributed to qubit transitions induced by a measurement stimulus. Resonant transition between fluctuating dressed states of the qubits coupled with high-frequency resonators can be responsible for the error-rate change.

quant-ph↗

arXiv2023

Detecting bulk carbon ferromagnetism in graphene multi-edge structure

The emergence of bulk carbon ferromagnetism is long-expected over years. At nanoscale, carbon ferromagnetism was detected by analyzing the magnetic edge states via scanning tunneling microscopy(STM), and its origin can be explained by local redistribution of electron wave function. In larger scale, carbon ferromagnetism can be created by deliberately producing defects in graphite, and detected by macroscopic technical magnetization. Meanwhile, it becomes crucial to determine that the detected magnetization is originated from carbon rather than from magnetic impurities. One solution is X-ray magnetic circular dichroism (XMCD). Nonetheless, a reproducible, full section of XMCD spectrum across C-1s absorption energy has not appeared yet, which should be decisive for assuring the indisputable existence of bulk carbon ferromagnetism. Besides, the lack of direct observation on the atomic structure of the ferromagnetic carbon leaves the structural origin of its ferromagnetism still in mist. In this work, for detecting bulk carbon ferromagnetism, we managed to grow all-carbon film consisting of vertically aligned graphene multi-edge (VGME), which wove into a three-dimensional hyperfine-porous network. Magnetization (M-H) curves and XMCD spectra co-confirmed bulk carbon ferromagnetism of VGME at room temperature, with the average unit magnetic momentum of ~0.0006 miuB/atom. The influence of magnetic impurities on magnetization was excluded by both absorption spectra and inductively coupled plasma mass spectrometry measurements. The spin transfer behavior also verified the long-range and robust feature of the bulk carbon ferromagnetism. Our work provides direct evidence of elementary resolved bulk carbon ferromagnetism at room temperature and clarifies its origin from pi-electrons at graphene edges.

cond-mat.mtrl-sci↗

arXiv2023

Channel Attention for Quantum Convolutional Neural Networks

Quantum convolutional neural networks (QCNNs) have gathered attention as one of the most promising algorithms for quantum machine learning. Reduction in the cost of training as well as improvement in performance is required for practical implementation of these models. In this study, we propose a channel attention mechanism for QCNNs and show the effectiveness of this approach for quantum phase classification problems. Our attention mechanism creates multiple channels of output state based on measurement of quantum bits. This simple approach improves the performance of QCNNs and outperforms a conventional approach using feedforward neural networks as the additional post-processing.

quant-ph↗

arXiv2023

Quantum Circuit Distillation and Compression

Quantum coherence in a qubit is vulnerable to environmental noise. When long quantum calculation is run on a quantum processor without error correction, the noise often causes fatal errors and messes up the calculation. Here, we propose quantum-circuit distillation to generate quantum circuits that are short but have enough functions to produce an output almost identical to that of the original circuits. The distilled circuits are less sensitive to the noise and can complete calculation before the quantum coherence is broken in the qubits. We created a quantum-circuit distillator by building a reinforcement learning model, and applied it to the inverse quantum Fourier transform (IQFT) and Shor's quantum prime factorization. The obtained distilled circuit allows correct calculation on IBM-Quantum processors. By working with the quantum-circuit distillator, we also found a general rule to generate quantum circuits approximating the general $n$-qubit IQFTs. The quantum-circuit distillator offers a new approach to improve performance of noisy quantum processors.

quant-ph↗

arXiv2023

Detection of temporal fluctuation in superconducting qubits for quantum error mitigation

We have investigated instability of a superconducting quantum computer by continuously monitoring the qubit output. We found that qubits exhibit a step-like change in the error rates. This change is repeatedly observed, and each step persists for several minutes. By analyzing the correlation between the increased errors and anomalous variance of the output, we demonstrate quantum error mitigation based on post-selection. Numerical analysis on the proposed method was also conducted.

quant-ph↗

arXiv2022

Deciphering quantum fingerprints in electric conductance

When the electric conductance of a nano-sized metal is measured at low temperatures, it often exhibits complex but reproducible patterns as a function of external magnetic fields, called quantum fingerprints in electric conductance. Such complex patterns are due to quantum-mechanical interference of conduction electrons; when thermal disturbance is feeble and coherence of the electrons extends all over the sample, the quantum interference pattern reflects microscopic structures, such as crystalline defects and the shape of the sample, giving rise to complicated interference. Although the interference pattern carries such microscopic information, it looks so random that it has not been analysed. Here we show that machine learning allows us to decipher quantum fingerprints; fingerprint patterns in magneto-conductance are shown to be transcribed into spatial images of electron wave function intensities (WIs) in a sample by using generative machine learning. The output WIs reveal quantum interference states of conduction electrons, as well as sample shapes. The present result augments the human ability to identify quantum states, and it should allow microscopy of quantum nanostructures in materials by making use of quantum fingerprints.

cond-mat.mes-hall↗

arXiv2020

Thickness dependence of spin Peltier effect visualized by thermal imaging technique

Magnon propagation length in a ferrimagnetic insulator yttrium iron garnet (YIG) has been investigated by measuring and analyzing the YIG-thickness t_YIG dependence of the spin Peltier effect (SPE) in a Pt/YIG junction system. By means of the lock-in thermography technique, we measured the spatial distribution of the SPE-induced temperature modulation in the Pt/YIG system with the t_YIG gradation, allowing us to obtain the accurate t_YIG dependence of SPE with high t_YIG resolution. Based on the t_YIG dependence of SPE, we verified the applicability of several phenomenological models to estimate the magnon diffusion length in YIG.

cond-mat.mtrl-sci↗

arXiv2019

Magnon polarons in the spin Peltier effect

We report the observation of anomalous peak structures induced by hybridized magnon-phonon excitation (magnon polarons) in the magnetic field dependence of the spin Peltier effect (SPE) in a Lu$_{2}$Bi$_{1}$Fe$_{4}$Ga$_{1}$O$_{12}$ (BiGa:LuIG) with Pt contact. The SPE peaks coincide with magnetic fields tuned to the threshold of magnon-polaron formation, consistent with the previous observation in the spin Seebeck effect. The enhancement of SPE is attributed to the lifetime increase in spin current caused by magnon-phonon hybridization in BiGa:LuIG.

cond-mat.mtrl-sci↗

arXiv2018

Efficient spin transport in a paramagnetic insulator

The discovery of new materials that efficiently transmit spin currents has been important for spintronics and material science. The electric insulator $\mathrm{Gd}_3\mathrm{Ga}_5\mathrm{O}_{12}$ (GGG) is a superior substrate for growing magnetic films, but has never been considered as a conduit for spin currents. Here we report spin current propagation in paramagnetic GGG over several microns. Surprisingly, the spin transport persists up to temperatures of 100 K $\gg$ $T_{\mathrm{g}} = 180$ mK, GGG's magnetic glass-like transition temperature. At 5 K we find a spin diffusion length ${λ_{\mathrm{GGG}}} = 1.8 \pm 0.2 μ$m and a spin conductivity $σ_{\mathrm{GGG}} = (7.3 \pm 0.3) \times10^4$ $\mathrm{Sm}^{-1}$ that is larger than that of the record quality magnet $\mathrm{Y}_3\mathrm{Fe}_5\mathrm{O}_{12}$ (YIG). We conclude that exchange coupling is not required for efficient spin transport, which challenges conventional models and provides new material-design strategies for spintronic devices.

cond-mat.mtrl-sci↗

arXiv2017

Magnetic-field-induced suppression of spin Peltier effect in Pt/${\rm Y_{3}Fe_{5}O_{12}}$ system at room temperature

We report the observation of magnetic-field-induced suppression of the spin Peltier effect (SPE) in a junction of a paramagnetic metal Pt and a ferrimagnetic insulator ${\rm Y_{3}Fe_{5}O_{12}}$ (YIG) at room temperature. For driving the SPE, spin currents are generated via the spin Hall effect from applied charge currents in the Pt layer, and injected into the adjacent thick YIG film. The resultant temperature modulation is detected by a commonly-used thermocouple attached to the Pt/YIG junction. The output of the thermocouple shows sign reversal when the magnetization is reversed and linearly increases with the applied current, demonstrating the detection of the SPE signal. We found that the SPE signal decreases with the magnetic field. The observed suppression rate was found to be comparable to that of the spin Seebeck effect (SSE), suggesting the dominant and similar contribution of the low-energy magnons in the SPE as in the SSE.

cond-mat.mtrl-sci↗