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Tsuyoshi Yoshida

Publications and source records attributed to Tsuyoshi Yoshida.

15 recordsLinked to original sources
arXiv2025

Perfect impedance matching unlocks sensitive radio-frequency reflectometry in 2D material quantum dots

Two-dimensional (2D) materials are attractive platforms for realizing high-performance quantum bits (qubits). However, radio-frequency (RF) charge detection, which is a key technique for qubits readout, remains challenging in such systems. We demonstrate RF reflectometry with impedance matching for high-resistance quantum dot devices based on bilayer graphene and molybdenum disulfide. By integrating a tunable strontium titanate (SrTiO3) varactor into a resonant circuit, we achieve nearly perfect impedance matching, enabling sensitive charge detection. The demodulated RF signal clearly shows Coulomb oscillations, and the SrTiO3 varactor exhibits robustness against both magnetic fields and voltage noise on the varactor. Our results establish SrTiO3 varactors as effective tunable matching components for RF reflectometry in high-resistance 2D material quantum devices, providing a foundation for high-speed qubits readout.

cond-mat.mes-hall↗
arXiv2025

Hyperbolic Floquet code with graph-edge syndromes

Quantum error correction would be a primitive for demonstrating quantum advantage in a realistic noisy environment. Floquet codes are a class of dynamically generated, stabilizer-based codes in which low-weight parity measurements are applied in a time-periodic schedule. Furthermore, for several Floquet codes, the encoding rate becomes finite even for an infinitely large qubit number. However, despite these advantageous properties, existing Floquet codes require handling more intricate, often hypergraph-structured syndromes from the decoding perspective, which makes decoding comparatively demanding. We give a concrete method for solving this issue by proposing hyperbolic color Floquet (HCF) code. To this end, we simultaneously take advantage of hyperbolic Floquet and Floquet color codes. Parity measurements in our code consist of the repetitions of six-step measurements on (semi-)hyperbolic three-colorable tilings. Since each step just measures $X \otimes X$ or $Z \otimes Z$, our code on the regular $\{8,3\}$ lattice has the following three advantages: (i) parity measurements are weight-2, (ii) for the numbers $k$ and $n$ of logical and physical qubits, respectively, the encoding rate is finite, i.e., $\lim_{n \to \infty} k/n = 1/8$, and (iii) the code distance is proportional to $\log n$. From the above property, each single-fault event generally affects at most two detectors, which implies ``graph-edge'' syndromes, and hence decoding with a minimum-weight perfect matching (MWPM) decoder is efficient and virtually scales near-linearly in the number of physical qubits $n$. This is a stark contrast to several known Floquet codes because their parity measurements repeat the measurements of $X\!\otimes\!X$, $Y\!\otimes\!Y$, and $Z\!\otimes\!Z$, and thus the syndromes are represented as a hypergraph, which basically requires decoders with longer decoding time.

quant-ph↗
arXiv2025

RFSoC-based radio-frequency reflectometry in gate-defined bilayer graphene quantum devices

Quantum computers require both scalability and high performance for practical applications. While semiconductor quantum dots are promising candidates for quantum bits, the complexity of measurement setups poses an important challenge for scaling up these devices. Here, radio-frequency system-on-chip (RFSoC) technology is exepcted for a promising approach that combines scalability with flexibility. In this paper, we demonstrate RF reflectometry in gate-defined bilayer graphene quantum devices using RFSoC-based measurement architecture. By controlling the confinement strength through gate voltages, we achieve both Fabry-Pérot interferometer and quantum dot operations in a single device. Although impedance matching conditions currently limit the measurement sensitivity, we identify pathways for optimization through tunnel barrier engineering and resonator design. These results represent a step toward integrating high-bandwidth measurements with scalable quantum devices.

cond-mat.mes-hall↗
arXiv2024

Efficient Bit Labeling in Factorization Machines with Annealing for Traveling Salesman Problem

To efficiently find an optimum parameter combination in a large-scale problem, it is a key to convert the parameters into available variables in actual machines. Specifically, quadratic unconstrained binary optimization problems are solved with the help of machine learning, e.g., factorization machines with annealing, which convert a raw parameter to binary variables. This work investigates the dependence of the convergence speed and the accuracy on binary labeling method, which can influence the cost function shape and thus the probability of being captured at a local minimum solution. By exemplifying traveling salesman problem, we propose and evaluate Gray labeling, which correlates the Hamming distance in binary labels with the traveling distance. Through numerical simulation of traveling salesman problem up to 15 cities at a limited number of iterations, the Gray labeling shows less local minima percentages and shorter traveling distances compared with natural labeling.

cs.LG↗
arXiv2024

Simplification of tensor updates toward performance-complexity balanced quantum computer simulation

Matrix Product States (MPS) provide a powerful framework for simulating quantum circuits. In practical simulations, tensor updates are typically performed in the canonical form (CF), which corresponds to the Schmidt decomposition and improves approximation accuracy. However, maintaining the canonical form introduces significant computational overhead. An alternative approach, known as the Simple Update (SU), does not enforce the Schmidt decomposition and is expected to reduce computational complexity. In this work, we systematically compare the performance and computational cost of SU and CF in quantum circuit simulations. We benchmark both methods on highly entangled circuits and on a QASM benchmark suite covering a wide range of circuit types. Our results show that SU achieves accuracy comparable to CF while reducing computational complexity, indicating that SU provides an efficient alternative for practical quantum circuit simulations.

quant-ph↗
arXiv2024

Preconditioning for a Variational Quantum Linear Solver

We apply preconditioning, which is widely used in classical solvers for linear systems $A\textbf{x}=\textbf{b}$, to the variational quantum linear solver. By utilizing incomplete LU factorization as a preconditioner for linear equations formed by $128\times128$ random sparse matrices, we numerically demonstrate a notable reduction in the required ansatz depth, demonstrating that preconditioning is useful for quantum algorithms. This reduction in circuit depth is crucial to improving the efficiency and accuracy of Noisy Intermediate-Scale Quantum (NISQ) algorithms. Our findings suggest that combining classical computing techniques, such as preconditioning, with quantum algorithms can significantly enhance the performance of NISQ algorithms.

quant-ph↗
arXiv2024

Application of Tensorized Neural Networks for Cloud Classification

Convolutional neural networks (CNNs) have gained widespread usage across various fields such as weather forecasting, computer vision, autonomous driving, and medical image analysis due to its exceptional ability to extract spatial information, share parameters, and learn local features. However, the practical implementation and commercialization of CNNs in these domains are hindered by challenges related to model sizes, overfitting, and computational time. To address these limitations, our study proposes a groundbreaking approach that involves tensorizing the dense layers in the CNN to reduce model size and computational time. Additionally, we incorporate attention layers into the CNN and train it using Contrastive self-supervised learning to effectively classify cloud information, which is crucial for accurate weather forecasting. We elucidate the key characteristics of tensorized neural network (TNN), including the data compression rate, accuracy, and computational speed. The results indicate how TNN change their properties under the batch size setting.

cs.CV↗
arXiv2021

Performance Prediction Recipes for Optical Links

Although forward error-correction (FEC) coding is an essential part of modern fiber-optic communication systems, it is impractical to implement and evaluate FEC in transmission experiments and simulations. Therefore, it is desirable to accurately predict the end-to-end link performance including FEC from transmission data recorded without FEC. In this tutorial, we provide ready-to-implement "recipes" for such prediction techniques, which apply to arbitrary channels and require no knowledge of information or coding theory. The appropriate choice of recipe depends on properties of the FEC encoder and decoder. The covered metrics include bit error rate, symbol error rate, achievable information rate, and asymptotic information, in all cases computed using a mismatched receiver. Supplementary software implementations are available.

cs.IT↗
arXiv2021

Compressed Shaping: Concept and FPGA Demonstration

Probabilistic shaping (PS) has been widely studied and applied to optical fiber communications. The encoder of PS expends the number of bit slots and controls the probability distribution of channel input symbols. Not only studies focused on PS but also most works on optical fiber communications have assumed source uniformity (i.e. equal probability of marks and spaces) so far. On the other hand, the source information is in general nonuniform, unless bit-scrambling or other source coding techniques to balance the bit probability is performed. Interestingly, one can exploit the source nonuniformity to reduce the entropy of the channel input symbols with the PS encoder, which leads to smaller required signal-to-noise ratio at a given input logic rate. This benefit is equivalent to a combination of data compression and PS, and thus we call this technique compressed shaping. In this work, we explain its theoretical background in detail, and verify the concept by both numerical simulation and a field programmable gate array (FPGA) implementation of such a system. In particular, we find that compressed shaping can reduce power consumption in forward error correction decoding by up to 90% in nonuniform source cases. The additional hardware resources required for compressed shaping are not significant compared with forward error correction coding, and an error insertion test is successfully demonstrated with the FPGA.

cs.IT↗
arXiv2020

Post-FEC BER Benchmarking for Bit-Interleaved Coded Modulation with Probabilistic Shaping

Accurate performance benchmarking after forward error correction (FEC) decoding is essential for system design in optical fiber communications. Generalized mutual information (GMI) has been shown to be successful at benchmarking the bit-error rate (BER) after FEC decoding (post-FEC BER) for systems with soft-decision (SD) FEC without probabilistic shaping (PS). However, GMI is not relevant to benchmark post-FEC BER for systems with SD-FEC and PS. For such systems, normalized GMI (NGMI), asymmetric information (ASI), and achievable FEC rate have been proposed instead. They are good at benchmarking post-FEC BER or to give an FEC limit in bit-interleaved coded modulation (BICM) with PS, but their relation has not been clearly explained so far. In this paper, we define generalized L-values under mismatched decoding, which are connected to the GMI and ASI. We then show that NGMI, ASI, and achievable FEC rate are theoretically equal under matched decoding but not under mismatched decoding. We also examine BER before FEC decoding (pre-FEC BER) and ASI over Gaussian and nonlinear fiber-optic channels with approximately matched decoding. ASI always shows better correlation with post-FEC BER than pre-FEC BER for BICM with PS. On the other hand, post-FEC BER can differ at a given ASI when we change the bit mapping, which describes how each bit in a codeword is assigned to a bit tributary.

eess.SP↗
arXiv2020

Performance Monitoring for Live Systems with Soft FEC and Multilevel Modulation

Performance monitoring is an essential function for margin measurements in live systems. Historically, system budgets have been described by the Q-factor converted from the bit error rate (BER) under binary modulation and direct detection. The introduction of hard-decision forward error correction (FEC) did not change this. In recent years technologies have changed significantly to comprise coherent detection, multilevel modulation and soft FEC. In such advanced systems, different metrics such as (nomalized) generalized mutual information (GMI/NGMI) and asymmetric information (ASI) are regarded as being more reliable. On the other hand, Q budgets are still useful because pre-FEC BER monitoring is established in industry for live system monitoring. The pre-FEC BER is easily estimated from available information of the number of flipped bits in the FEC decoding, which does not require knowledge of the transmitted bits that are unknown in live systems. Therefore, the use of metrics like GMI/NGMI/ASI for performance monitoring has not been possible in live systems. However, in this work we propose a blind soft-performance estimation method. Based on a histogram of log-likelihood-values without the knowledge of the transmitted bits, we show how the ASI can be estimated. We examined the proposed method experimentally for 16 and 64-ary quadrature amplitude modulation (QAM) and probabilistically shaped 16, 64, and 256-QAM in recirculating loop experiments. We see a relative error of 3.6%, which corresponds to around 0.5 dB signal-to-noise ratio difference for binary modulation, in the regime where the ASI is larger than the assumed FEC threshold. For this proposed method, the digital signal processing circuitry requires only a minimal additional function of storing the L-value histograms before the soft-decision FEC decoder.

eess.SP↗
arXiv2019

Preferred Design of Hierarchical Distribution Matching

Distribution matching and dematching (DM/invDM) are key functions in probabilistic shaping (PS). Recently techniques for low complexity implementation of DM/invDM have been well studied. Our previously proposed hierarchical DM (HiDM) is one of the good candidates, with capacity-approaching performance with reasonable hardware resources. Though we explained the recipe of HiDM construction with a small example having a short DM word length, there might still be difficulties to expand it to longer DM word lengths. To improve the reproducibility of our work, this paper explains the key parameters in an HiDM having a DM word length more than 100 symbols.

eess.SP↗
arXiv2018

Hierarchical Distribution Matching for Probabilistically Shaped Coded Modulation

The implementation difficulties of combining distribution matching (DM) and dematching (invDM) for probabilistic shaping (PS) with soft-decision forward error correction (FEC) coding can be relaxed by reverse concatenation, for which the FEC coding and decoding lies inside the shaping algorithms. PS can seemingly achieve performance close to the Shannon limit, although there are practical implementation challenges that need to be carefully addressed. We propose a hierarchical DM (HiDM) scheme, having fully parallelized input/output interfaces and a pipelined architecture that can efficiently perform the DM/invDM without the complex operations of previously proposed methods such as constant composition DM (CCDM). Furthermore, HiDM can operate at a significantly larger post-FEC bit error rate (BER) for the same post-invDM BER performance, which facilitates simulations. These benefits come at the cost of a slightly larger rate loss and required signal-to-noise ratio at a given post-FEC BER.

eess.SP↗
arXiv2017

Performance Metrics for Systems with Soft-Decision FEC and Probabilistic Shaping

High-throughput optical communication systems utilize binary soft-decision forward error correction (SD-FEC) with bit interleaving over the bit channels. The generalized mutual information (GMI) is an achievable information rate (AIR) in such systems and is known to be a good predictor of the bit error rate after SD-FEC decoding (post-FEC BER) for uniform signaling. However, for probabilistically shaped (nonuniform) signaling, we find that the normalized AIR, defined as the AIR divided by the signal entropy, is less correlated with the post-FEC BER. We show that the information quantity based on the distribution of the single bit signal, and its asymmetric loglikelihood ratio, are better predictors of the post-FEC BER. In simulations over the Gaussian channel, we find that the prediction accuracy, quantified as the peak-to-peak deviation of the post-FEC BER within a set of different modulation formats and distributions, can be improved more than 10 times compared with the normalized AIR.

cs.IT↗