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Physical Review A 2025年第1期111卷 L011701-L011701页

作者： Takeru Utsugi Rui Asaoka Yuuki Tokunaga Takao Aoki Department of Applied Physics Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo 169-8555 Japan Computer & Data Science Laboratories NTT Corporation Musashino Tokyo 180-8585 Japan

We investigate atom-photon entangling gates based on cavity quantum electrodynamics (QED) for a finite photon-pulse duration, where not only the photon loss but also the temporal mode-mismatch of the photon pulse becomes a severe source of error. We analytically derive relations between cavity parameters, including transmittance, length, and effective cross-sectional area of the cavity, that minimize both the photon loss probability and the error rate due to temporal mode-mismatch by taking it into account as state-dependent pulse delay. We also investigate the effects of pulse distortion using numerical simulations for the case of short pulse duration. We believe that these analyses are the first to suggest that a cavity has an optimal length for the atom-photon gate, providing a fundamental guideline for implementing quantum information processing.

关键词： Atoms

Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models

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arXiv 2025年

作者： Chijiwa, Daiki Hasegawa, Taku Nishida, Kyosuke Saito, Kuniko Takeuchi, Susumu NTT Computer and Data Science Laboratories NTT Corporation Japan NTT Human Informatics Laboratories NTT Corporation Japan

While foundation models have been exploited for various expert tasks through fine-tuning, any foundation model will become outdated due to its old knowledge or limited capability. Thus the underlying foundation model should be eventually replaced by new ones, which leads to repeated cost of fine-tuning these new models. Existing work addresses this problem by inference-time tuning, i.e., modifying the output probabilities from the new foundation model with the outputs from the old foundation model and its fine-tuned model, which involves an additional overhead in inference by the latter two models. In this paper, we propose a new fine-tuning principle, Portable Reward Tuning (PRT), that reduces the inference overhead by its nature, based on the reformulation of fine-tuning as the reward maximization. Specifically, instead of fine-tuning parameters of the foundation models, PRT trains the reward model explicitly through the same loss function as in fine-tuning. During inference, the reward model can be used with any foundation model (with the same set of vocabularies or labels) through the formulation of reward maximization. Experimental results, covering both vision and language models, demonstrate that the PRT-trained model can achieve comparable accuracy to the existing work of inference-time tuning, with less inference cost. Copyright © 2025, The Authors. All rights reserved.

关键词： Reusability

Fault-tolerant logical state construction based on cavity-QED network

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arXiv 2025年

作者： Asaoka, Rui Suzuki, Yasunari Tokunaga, Yuuki NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan

Exploring an efficient and scalable architecture of fault-tolerant quantum computing (FTQC) is vital for demonstrating useful quantum computing. Here, we propose and evaluate a scalable and practical architecture with a cavity-quantum-electrodynamics (CQED) network. Our architecture takes advantage of the stability of neutral atoms and the flexibility of a CQED network. We show a concrete framework for implementing surface codes and numerically analyze the logical error rate and threshold values beyond the simplified circuit-level noise model on several network structures. Although the requirement of CQED parameters is demanding given the current performance of experimental systems, we show that an error-decoding algorithm tailored to our proposed architecture, where the loss information of ancillary photons is utilized, greatly improves the error threshold. For example, the internal cooperativity, a good figure of merit of the cavity performance for quantum computing, required for FTQC is relaxed to 1/5 compared to the normal error-decoding for the surface code. Since our proposal and results can be extended to other LDPC codes straightforwardly, our approach will lead to achieve more reliable FTQC using CQED. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

data-Efficient Error Mitigation for Physical and Algorithmic Errors in a Hamiltonian Simulation

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arXiv 2025年

作者： Hakkaku, Shigeo Suzuki, Yasunari Tokunaga, Yuuki Endo, Suguru NTT Computer and Data Science Laboratories NTT Corporation 3-9-11 Midori-cho Musashino Tokyo180-8585 Japan NTT Research Center for Theoretical Quantum Physics NTT Corporation 3-1 Morinosato-Wakamiya Atsugi243-0198 Japan JST PRESTO 4-1-8 Honcho Saitama Kawaguchi332-0012 Japan

Quantum dynamics simulation via Hamilton simulation algorithms is one of the most crucial applications in the quantum computing field. While this task has been relatively considered the target in the fault-tolerance era, the experiment for demonstrating utility by an IBM team simulates the dynamics of an Ising-type quantum system with the Trotter-based Hamiltonian simulation algorithm with the help of quantum error mitigation [1]. In this study, we propose the data-efficient 1D extrapolation method to mitigate not only physical errors but also algorithmic errors of Trotterized quantum circuits in both the near-term and early fault-tolerant eras. Our proposed extrapolation method uses expectation values obtained by Trotterized circuits, where the Trotter number is selected to minimize both physical and algorithmic errors according to the circuit’s physical error rate. We also propose a method that combines the data-efficient 1D extrapolation with purification QEM methods, which improves accuracy more at the expense of multiple copies of quantum states or the depth of the quantum circuit. Using the 1D transverse-field Ising model, we numerically demonstrate our proposed methods and confirm that our proposed extrapolation method suppresses both statistical and systematic errors more than the previous extrapolation method. © 2025, CC0.

关键词： Ising model

Logical entanglement distribution between distant 2D array qubits

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arXiv 2025年

作者： Maeda, Yuya Suzuki, Yasunari Kobayashi, Toshiki Yamamoto, Takashi Tokunaga, Yuuki Fujii, Keisuke Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Center for Quantum Information and Quantum Biology Institute for Open and Transdisciplinary Research Initiatives Osaka University Japan Center for Quantum Computing RIKEN Wako Saitama351-0198 Japan

Sharing logical entangled pairs between distant quantum nodes is a key process to achieve fault-tolerant quantum computation and communication. However, there is a gap between current experimental specifications and theoretical requirements for sharing logical entangled states while improving experimental techniques. Here, we propose an efficient logical entanglement distribution protocol based on surface codes for two distant 2D qubit array with nearest-neighbor interaction. A notable feature of our protocol is that it allows post-selection according to error estimations, which provides the tunability between the infidelity of logical entanglements and the success probability of the protocol. With this feature, the fidelity of encoded logical entangled states can be improved by sacrificing success rates. We numerically evaluated the performance of our protocol and the trade-off relationship, and found that our protocol enables us to prepare logical entangled states while improving fidelity in feasible experimental parameters. We also discuss a possible physical implementation using neutral atom arrays to show the feasibility of our protocol. © 2025, CC BY.

关键词： Qubits

High-purity single-photon generation based on cavity QED

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Physical Review Research 2025年第1期7卷 013251-013251页

作者： Seigo Kikura Rui Asaoka Masato Koashi Yuuki Tokunaga Department of Applied Physics Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Faculty of Science and Engineering Waseda University 3-4-1 Okubo Shinjuku-ku Tokyo 169-8585 Japan Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan Photon Science Center Graduate School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan

We propose a scheme for generating a high-purity single photon on the basis of cavity QED. This scheme employs an atom as a four-level system and the structure allows the suppression of the reexcitation process due to the atomic decay, which is known to significantly degrade the single-photon purity in state-of-the-art photon sources using a three-level system. Our analysis shows that the reexcitation probability arbitrarily approaches zero without sacrificing the photon generation probability when increasing the power of a driving laser between two excited states. This advantage is achievable by using current cavity-QED technologies. Our scheme can contribute to developing distributed quantum computation or quantum communication with high accuracy.

关键词： Quantum computers

Next-Speaker Prediction Based on Non-Verbal Information in Multi-Party Video Conversation 48

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Next-Speaker Prediction Based on Non-Verbal Information in M...

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Mizuno, Saki Hojo, Nobukatsu Kobashikawa, Satoshi Masumura, Ryo Ntt Computer & Data Science Laboratories

ISBN: (纸本)9781728163277

We propose a method for next-speaker prediction, a task to predict who speaks in the next turn among multiple current listeners, in multi-party video conversation. Previous studies used non-verbal features, such as head movements and gaze behavior, for next-speaker prediction in face-to-face conversation. However, in video conversation, these non-verbal features are vague and ineffective because they look at the screen displaying other participants. Since non-verbal features include participant characteristics, it is necessary to use training data with rich combinations of participants to robustly predict the next speaker. Previous studies used training data with a limited number of combinations of participants because the data consist only of recorded data. Therefore, the proposed method uses 1) novel non-verbal features for next-speaker prediction in video conversation, specifically facial expressions, hand movements and speech segments, and 2) data augmentation of participant combinations in the training data. We conducted experiments to evaluate the proposed method, and the results using video-conversation data indicate its effectiveness. © 2023 IEEE.

关键词： multi-party conversation next-speaker prediction non-verbal information video conversation

Fast Iterative Hard Thresholding Methods with Pruning Gradient Computations 38

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Fast Iterative Hard Thresholding Methods with Pruning Gradie...

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ida, Yasutoshi Kanai, Sekitoshi Kumagai, Atsutoshi Iwata, Tomoharu Fujiwara, Yasuhiro NTT Computer and Data Science Laboratories Japan NTT Communication Science Laboratories Japan

We accelerate the iterative hard thresholding (IHT) method, which finds k important elements from a parameter vector in a linear regression model. Although the plain IHT repeatedly updates the parameter vector during the optimization, computing gradients is the main bottleneck. Our method safely prunes unnecessary gradient computations to reduce the processing time. The main idea is to efficiently construct a candidate set, which contains k important elements in the parameter vector, for each iteration. Specifically, before computing the gradients, we prune unnecessary elements in the parameter vector for the candidate set by utilizing upper bounds on absolute values of the parameters. Our method guarantees the same optimization results as the plain IHT because our pruning is safe. Experiments show that our method is up to 73 times faster than the plain IHT without degrading accuracy. © 2024 Neural information processing systems foundation. All rights reserved.

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Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation 12

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Recurrent Neural Networks for Learning Long-term Temporal De...

12th IEEE International Conference on Big Knowledge, ICBK 2021

作者： Ohno, Kentaro Kumagai, Atsutoshi NTT Computer Data Science Laboratories Japan

ISBN: (纸本)9781665438582

Recurrent neural networks with a gating mechanism such as an LSTM or GRU are powerful tools to model sequential data. In the mechanism, a forget gate, which was introduced to control information flow in a hidden state in the RNN, has recently been re-interpreted as a representative of the time scale of the state, i.e., a measure how long the RNN retains information on inputs. On the basis of this interpretation, several parameter initialization methods to exploit prior knowledge on temporal dependencies in data have been proposed to improve learn-ability. However, the interpretation relies on various unrealistic assumptions, such as that there are no inputs after a certain time point. In this work, we reconsider this interpretation of the forget gate in a more realistic setting. We first generalize the existing theory on gated RNNs so that we can consider the case where inputs are successively given. We then argue that the interpretation of a forget gate as a temporal representation is valid when the gradient of loss with respect to the state decreases exponentially as time goes back. We empirically demonstrate that existing RNNs satisfy this gradient condition at the initial training phase on several tasks, which is in good agreement with previous initialization methods. On the basis of this finding, we propose an approach to construct new RNNs that can represent a longer time scale than conventional models, which will improve the learnability for long-term sequential data. We verify the effectiveness of our method by experiments with real-world datasets. © 2021 IEEE.

关键词： Time series analysis

Text-to-Text Pre-Training with Paraphrasing for Improving Transformer-based Image Captioning 31

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Text-to-Text Pre-Training with Paraphrasing for Improving Tr...

31st European Signal Processing Conference, EUSIPCO 2023

作者： Masumura, Ryo Makishima, Naoki Ihori, Mana Takashima, Akihiko Tanaka, Tomohiro Orihashi, Shota NTT Computer Data Science Laboratories NTT Corporation Japan

ISBN: (纸本)9789464593600

In this paper, we propose a novel training method for the transformer encoder-decoder based image captioning, which directly generates a captioning text from an input image. In general, many image-to-text paired datasets need to be prepared for robust image captioning, but such datasets cannot be collected in practical cases. Our key idea for mitigating the data preparation cost is to utilize text-to-text paraphrasing modeling, i.e., a task to convert an input text into different expressions without changing the meaning. In fact, paraphrasing deals with a similar transformation task to image captioning even though paraphrasing tasks have to handle texts instead of images. In our proposed method, an encoder-decoder network trained via the paraphrasing task is directly leveraged for image captioning. Thus, an encoder-decoder network pre-trained by a text-to-text transformation task is transferred into an image-to-text transformation task even though a different modal must be handled in the encoder network. Our experiments using the MS COCO caption datasets demonstrate the effectiveness of the proposed method. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Image enhancement