arXiv subjects

Ryoichi Ishihara

Publications and source records attributed to Ryoichi Ishihara.

12 recordsLinked to original sources

arXiv2026

Plasma Etch Process Optimization for Photonic-Grade Diamond-on-Insulator Substrates and Thickness Evaluation using Colorimetry

Diamond color-center qubits integrated with photonic circuits can be initialized, manipulated, entangled, and read individually with high fidelity, making them attractive for large-scale modular quantum computers, quantum networks, and distributed quantum sensing. However, the limited size of heteroepitaxially grown single-crystal diamond (SCD) and photonic-grade diamond-on-insulator (DOI) substrates remains a challenge for integration with existing manufacturing processes. Here, we develop a plasma etch recipe to thin direct-bonded (100) SCD membranes (<50 $μ$m) into large-area, thin-film DOI substrates, and demonstrate free-standing photonic chiplets fabricated from the resulting DOI. The ICP-RIE recipe preserves diamond bonding, provides sufficient micromasking and surface-quality control, and enables thin-film DOI manufacture. We thin a 10 $μ$m diamond plate bonded to SiO$_2$/Si and obtain a photonic-grade DOI substrate with diamond thickness $\leq$300 nm. The DOI film is around 300 nm thick over 0.5 $\times$ 0.5 mm$^2$, with surface roughness < 0.5 nm, while the bonding interface remains intact. Diamond photonic chiplets are fabricated on this DOI substrate using a standard two-step lithography process, without complex thin-film transfer, under-etching, or pedestal formation. We also present a colorimetric study of diamond visibility on SiO$_2$ and quantify color differences across thicknesses in common colorimetric spaces. This analysis enables automatic diamond-thickness extrapolation from standard optical microscope images with 5 nm resolution, in good agreement with white-light interferometry (WLI) measurements. The DOI substrate and colorimetric thickness-evaluation method provide an effective fabrication platform and reliable validation route for scalable manufacturing of diamond nanophotonic devices, opening a path toward large-scale integrated quantum systems.

physics.optics↗

arXiv2026

Integration of diamond nanobeams with SnVs on Al2O3 waveguides for scalable quantum photonic chip application

Tin vacancy (SnV) centers in diamond are promising solid state qubits for integrated quantum photonics. Here, we fabricate and characterize a diamond on Al2O3 dual taper waveguide structure containing SnV centers, demonstrating optical coupling between the diamond nanobeam and the underlying Al2O3 waveguide. The devices are realized using a bilayer fabrication approach compatible with wafer scale lithography. Clear guided SnV- emission is observed in all optically active devices, indicating effective optical coupling in the integrated structure. These results demonstrate a scalable fabrication approach toward integrating diamond color centers with photonic waveguides.

physics.optics↗

arXiv2026

Geometry-enabled magnetic resilience in superconducting nanowire single-photon detectors

While magnetic fields and superconductors are both central to classical and quantum technologies, their combined use is often challenging, as magnetic fields significantly affect superconducting device performance. In superconducting nanowire single-photon detectors (SNSPDs), magnetic fields drastically reduce detection efficiencies, hampering their application in magnetically-active classical and quantum photonics. Here, we systematically characterize the performance of NbTiN SNSPDs under magnetic fields and show the enhancement of their intrinsic detection efficiency (IDE) at lower bias currents and its suppression at higher currents. This leads to SNSPD performance degradation through reduced or disappearing saturation plateaus. We show that the magnitude of this degradation is highly dependent on nanowire width and demonstrate width-optimized SNSPDs with saturating IDE for a wide range of photon energies under application-relevant magnetic fields. Minimizing degradation in superconducting devices under magnetic fields enables applications like detector-integrated spin-optic and atomic quantum processors, high-sensitivity magnetometry, and quantum transduction.

cond-mat.supr-con↗

arXiv2026

PdNeuRAM: forming-free, multi-bit Pd/HfO2 ReRAM for energy-efficient neuromorphic computing

Memristor technology shows great promise for energy-efficient computing, yet it grapples with challenges like resistance drift and inherent variability. For filamentary Resistive RAM (ReRAM), one of the most investigated types of memristive devices, the expensive electroforming step required to create conductive pathways results in increased power and area overheads and reduced endurance. In this study, we present novel HfO2-based forming-free ReRAM devices, PdNeuRAM, that operate at low voltages, support multi-bit functionality, and display reduced variability. Through a deep understanding and comprehensive material characterization, we discover the key process that allows this unique behavior: a Pd-O-Hf configuration that capitalizes on Pd innate affinity for integrating into HfO2. This structure actively facilitates charge redistribution at room temperature, effectively eliminating the need for electroforming. Moreover, the fabricated ReRAM device provides tunable resistance states for dense memory and reduces programming and reading energy by 43% and 73%, respectively, using spiking neural networks (SNN). This study reveals novel mechanistic insights and delineates a strategic roadmap for the realization of power-efficient and cost-effective ReRAM devices.

cond-mat.mtrl-sci↗

arXiv2026

From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data

High-fidelity circuit execution on noisy intermediate-scale quantum devices is bottlenecked by compilation pipelines that disregard complex, correlated noise. To address this, this methodology article proposes a quantum machine learning control (QMLC) framework for generative quantum circuit synthesis from gate-set tomography (GST) data that bypasses the traditional two-step pipeline of characterizing native quantum gates via GST followed by unitary decomposition algorithms. Instead, a generative concept space is directly learnt from GST data, enabling conditional synthesis of quantum circuits on a desired output distribution. Our approach tokenizes GST germ circuits and embeds them into a structured latent space using a curriculum-learning-motivated strategy, starting with short circuits and progressively incorporating longer ones with diverse output statistics. The embedded sequences are processed by a set-vision transformer with permutation-invariant pooling, producing k-seed vectors that represent the learned concept space of the quantum device. Aggregating data across multiple circuits makes this latent representation inherently context-aware, capturing the shared physical noise environment (e.g., crosstalk, drift) that isolated gate metrics miss. We propose an unconditional diffusion model to sample from the concept space. During inference, a user provides a target measurement distribution, and the model generates a corresponding circuit. To ensure fidelity and robustness, the output is denoised using a diffusion model that operates on the target conditional covariance matrix. This end-to-end framework is a step towards context-aware, hardware-native circuit synthesis directly from raw GST data, which offers a new paradigm for integrating quantum control and compilation. The QMLC framework is particularly suited for near-term quantum devices with complex calibration procedures.

quant-ph↗

arXiv2026

Cross Waveguide Design for Color-Centers in Diamond for Photonic Quantum Computing

Color centers in diamond are a promising platform for quantum computing applications because of their optical and spin properties. However, diamond presents some technological challenges that limit its use in complex or large photonic circuits. To mitigate these limitations, it is technically effective to separate the smallest possible diamond photonic structures or chiplet containing the color center(s) from the rest of the circuit, which is fabricated on another material platform, and then heterogeneously integrate them. Considering efficient excitation and photon collection from waveguide-coupled color centers, we design a cross waveguide as the primary component of our chiplet to access the color centers, channeling excitation and emitted photons into different waveguides, and connecting the structure to the other components of the photonic circuit. The chiplet containing the cross waveguide and supporting structures requires careful optimization of each subcomponent. The receptor's design is also critical for optimal signal transmission. In this paper, we develop a simple but efficient methodology to optimize the main components constituting both the chiplet and the receptor for their synergistic operation. The designed structure has an excitation-to-emission conversion of more than 5.4%, crosstalk of less than -40 dB, a working bandwidth of 160 nm, fabrication feasibility and tolerance within the limits of modern nanofabrication, and a mechanical solid structure with a footprint of less than 2000 $μ$m

physics.optics↗

arXiv2026

Toward a CMOS-integrated quantum diamond biosensor based on NV centers

We report progress toward a CMOS-integrated quantum diamond biosensing platform that combines nitrogen-vacancy (NV) centers in diamond with a custom 40 nm CMOS Single-Photon Avalanche Diode (SPAD) array. The system integrates on-chip active quenching and digital readout with external FPGA-based photon counting, compact microwave delivery, and practical optical excitation and collection schemes to support widefield optically detected magnetic resonance (ODMR). System-level design considerations spanning fluorescence collection efficiency, detector count-rate capability, and microwave homogeneity are analyzed with biological compatibility and scalability in mind. Using superparamagnetic iron oxide nanoparticle (SPION)-labeled HEK293T cells as a representative use case, simple dipole-field estimates indicate that sub-$μ$T sensitivity is required to resolve ODMR shifts within typical ensemble linewidths. Based on the proposed architecture and efficiency analysis, a magnetic field sensitivity of approximately 90 nT/$\sqrt{\mathrm{Hz}}$ per pixel is estimated. These results outline a practical path from optics-heavy quantum diamond microscopes toward compact, CMOS-integrated NV-based biosensors for quantitative magnetic imaging in complex biological environments.

physics.app-ph↗

arXiv2025

Neural Network Architectures for Scalable Quantum State Tomography: Benchmarking and Memristor-Based Acceleration

Quantum State Tomography (QST) is essential for characterizing and validating quantum systems, but its practical use is severely limited by the exponential growth of the Hilbert space and the number of measurements required for informational completeness. Many prior claims of performance have relied on architectural assumptions rather than systematic validation. We benchmark several neural network architectures to determine which scale effectively with qubit number and which fail to maintain high fidelity as system size increases.To address this, we perform a comprehensive benchmarking of diverse neural architectures across two quantum measurement strategies to evaluate their effectiveness in reconstructing both pure and mixed quantum states. Our results reveal that CNN and CGAN scale more robustly and achieve the highest fidelities, while Spiking Variational Autoencoder (SVAE) demonstrates moderate fidelity performance, making it a strong candidate for embedded, low-power hardware implementations.Recognizing that practical quantum diagnostics will require embedded, energy-efficient computation, we also discuss how memristor-based Computation-in-Memory (CiM) platforms can accelerate these models in hardware, mitigating memory bottlenecks and reducing energy consumption to enable scalable in-situ QST. This work identifies which architectures scale favorably for future quantum systems and lays the groundwork for quantum-classical co-design that is both computationally and physically scalable.

quant-ph↗

arXiv2025

Strain Effects in a Directly Bonded Diamond-on-Insulator Substrate

The direct bonding process of a diamond-on-insulator (DOI) substrate enables monolithic integration of diamond photonic structures for quantum computing by improving photon collection efficiency and entanglement generation rate between emitters. It also addresses key fabrication challenges, such as robustness, bonding strength, and scalability. This study investigates strain effects in DOI substrates following direct bonding. Strain generation is expected near the diamond-SiO$_2$/Si interface due to thermal expansion coefficient mismatch between the bonded materials. Strain-induced lattice distortions are characterized using nitrogen-vacancy (NV) centers in diamond via optically detected magnetic resonance (ODMR) and photoluminescence (PL) mapping. PL mapping reveals interference fringes in unbonded regions, indicating bonding irregularities. Depth-resolved ODMR measurements show a volumetric strain component increase of $\approx$0.45 MHz and a shear component increase of $\approx$0.71 MHz between the top surface and the DOI interface. However, ODMR signal contrast and peak linewidth remain largely unaffected, suggesting no visible deterioration in the optical properties of the emitters. By combining ODMR and PL mapping, this work establishes a robust methodology for assessing bonding quality and strain impact on NV centers, an essential step toward advancing scalable quantum technologies and integrated photonic circuits.

physics.app-ph↗

arXiv2025

Deep Learning-Optimized, Fabrication Error-Tolerant Photonic Crystal Nanobeam Cavities for Scalable On-Chip Diamond Quantum Systems

Cavity-enhanced diamond color center qubits can be initialized, manipulated, entangled, and read individually with high fidelity, which makes them ideal for large-scale, modular quantum computers, quantum networks, and distributed quantum sensing systems. However, diamond's unique material properties pose significant challenges in manufacturing nanophotonic devices, leading to fabrication-induced structural imperfections and inaccuracies in defect implantation, which hinder reproducibility, degrade optical properties and compromise the spatial coupling of color centers to small mode-volume cavities. A cavity design tolerant to fabrication imperfections, such as surface roughness, sidewall slant, and non-optimal emitter positioning, can improve coupling efficiency while simplifying fabrication. To address this challenge, a deep learning-based optimization methodology is developed to enhance the fabrication error tolerance of nanophotonic devices. Convolutional neural networks (CNNs) are applied to promising designs, such as L2 and fishbone nanobeam cavities, predicting Q-factors up to one million times faster than traditional finite-difference time-domain (FDTD) simulations, enabling efficient optimization of complex, high-dimensional parameter spaces. The CNNs achieve prediction errors below 3.99% and correlation coefficients up to 0.988. Optimized structures demonstrate a 52% reduction in Q-factor degradation, achieving quality factors of 5e4 under real-world conditions and a two-fold expansion in field distribution, enabling efficient coupling of non-optimally positioned emitters. This methodology enables scalable, high-yield manufacturing of robust nanophotonic devices, including the cavity-enhanced diamond quantum systems developed in this study.

physics.optics↗

arXiv2025

Transformer Models for Quantum Gate Set Tomography

Quantum computation represents a promising frontier in the domain of high-performance computing, blending quantum information theory with practical applications to overcome the limitations of classical computation. This study investigates the challenges of manufacturing high-fidelity and scalable quantum processors. Quantum gate set tomography (QGST) is a critical method for characterizing quantum processors and understanding their operational capabilities and limitations. This paper introduces Ml4Qgst as a novel approach to QGST by integrating machine learning techniques, specifically utilizing a transformer neural network model. Adapting the transformer model for QGST addresses the computational complexity of modeling quantum systems. Advanced training strategies, including data grouping and curriculum learning, are employed to enhance model performance, demonstrating significant congruence with ground-truth values. We benchmark this training pipeline on the constructed learning model, to successfully perform QGST for 2 and 3 gates on single-qubit and two-qubit systems, with over-rotation error and depolarizing noise estimation with comparable accuracy to pyGSTi. This research marks a pioneering step in applying deep neural networks to the complex problem of quantum gate set tomography, showcasing the potential of machine learning to tackle nonlinear tomography challenges in quantum computing.

quant-ph↗

arXiv2025

Hydrophilic direct bonding of (100) diamond and deposited SiO$_2$ substrates

Diamond has emerged as a leading material for solid-state spin quantum systems and extreme environment electronics. However, a major limitation is that most diamond devices and structures are fabricated using bulk diamond plates. The absence of a suitable diamond-on-insulator (DOI) substrate hinders the advanced nanofabrication of diamond quantum and electronic devices, posing a significant roadblock to large-scale, on-chip diamond quantum photonics and electronics systems. In this work, we demonstrate the direct bonding of (100) single-crystal (SC) diamond plates to PECVD-grown SiO$_2$/Si substrates at low temperatures and atmospheric conditions. The surfaces of the SiO$_2$ and diamond plates are then activated using oxygen plasma and piranha solution, respectively. Bonding occurs when the substrates are brought into contact with water in between and annealed at 200$^{\circ}$C under atmospheric conditions, resulting in a DOI substrate. We systematically studied the influence of piranha solution treatment time and diamond surface roughness on the shear strength of the bonded substrate, devising an optimal bonding process that achieves a high yield rate of 90$\%$ and a maximum shear strength of 9.6 MPa. X-ray photoelectron spectroscopy (XPS) was used for quantitative analysis of the surface chemicals at the bonding interface. It appears that the amount of -OH bindings increases with the initial roughness of the diamond, facilitating the strong bonding with the SiO$_2$. This direct bonding method will pave the way for scalable manufacturing of diamond nanophotonic devices and enable large-scale integration of diamond quantum and electronic systems.

cond-mat.mtrl-sci↗