Research Institute for Advanced Computer Science

This study investigates changes in the rhythmic characteristics of Japanese speech induced by delayed auditory feedback (DAF). Ten native speakers read sentences under four DAF conditions (0, 100, 200, and 300 ms), and interval durations (consonant, vowel, and mora) were measured. Speech rhythm indices were evaluated using Mean (average duration), Delta (absolute variability), and Varco (relative variability). DAF increased Mean and Delta in consonant and vowel durations, indicating greater temporal variability at the phoneme level. However, the relative isochrony of morae, measured by VarcoM, remained stable, suggesting mora timing tends to be preserved under feedback perturbations.

Severe performance decay is witnessed for fast-charging lithium-ion batteries due to tremendous kinetic barriers. Herein, high-entropy solvation configurations are achieved to diminish such kinetic limitations by inducing multiple salts into the electrolyte. Theoretical calculations combined with experimental characterizations confirm the enhanced entropy of the electrolyte, rendering the participation of several anionic groups and reducing the solvation strength between Li+ and solvent/anion species. Concurrently, inorganic-rich interphases are further identified via preferential oxidation/reduction of diverse multianions clusters, thus reducing the interphases impedance. The graphite/LiNi0.5Mn0.2Co0.3O2 full cell maintains 95.4% capacity retention over 650 cycles at 1 C and delivers a capacity of 82.3 mAh g–1 (45.2 mAh g–1 for routine batteries) at 5 C. Such kinetic performance ensures the safety without Li plating at fast charging, increasing the thermal failure onset temperature from 68.6 °C to 167.7 °C under deeply aging conditions. This electrolyte offers an opportunity to achieve safe and long-term fast-charging lithium-ion batteries.

Variational quantum eigensolvers are touted as a near-term algorithm capable of impacting many applications. However, the potential has not yet been realized, with few claims of quantum advantage and high resource estimates, especially due to the need for optimization in the presence of noise. Finding algorithms and methods to improve convergence is important to accelerate the capabilities of near-term hardware for variational quantum eigensolver or more broad applications of hybrid methods in which optimization is required. To this goal, we look to use modern approaches developed in circuit simulations and stochastic classical optimization, which can be combined to form a surrogate optimization approach to quantum circuits. Using an approximate (classical central processing unit/graphical processing unit) state vector simulator as a surrogate model, we efficiently calculate an approximate Hessian, which is passed as input for a quantum processing unit or exact circuit simulator. This method will lend itself well to parallelization across quantum processing units. We demonstrate the capabilities of such an approach with and without sampling noise and a proof-of-principle demonstration on a quantum processing unit utilizing 40 qubits.

Multiple-Input-Multiple-Output (MIMO) signal detection is central to nearly every state-of-the-art communication system, and enhancements in error performance and computational complexity of MIMO detection would significantly enhance data rate and latency experienced by the users. Theoretically, the optimal MIMO detector is the maximum-likelihood (ML) MIMO detector; however, due to its extremely high complexity, it is not feasible for large real-world communication systems. Over the past few years, algorithms based on physics-inspired Ising solvers, like Coherent Ising machines and Quantum Annealers, have shown significant performance improvements for the MIMO detection problem. However, the current state-of-the-art is limited to low-order modulations or systems with few users. In this paper, we propose an adaptive multi-stage Ising machine-based MIMO detector that extends the performance gains of physicsinspired computation to Large and Massive MIMO systems with a large number of users and very high modulation schemes (up to 256-QAM). We enhance our previously proposed delta Ising formulation and develop a heuristic that adaptively optimizes the performance and complexity of our proposed method. We perform extensive micro-benchmarking to optimize several free parameters of the system and evaluate our methods' BER and spectral efficiency for Large and Massive MIMO systems (up to 32 users and 256-QAM modulation).

Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum algorithms has developed in many areas, with optimization being one of the most pronounced domains. Across computer science and physics, there are a number of algorithmic approaches, often with little linkage. This is further complicated by the fragmented nature of the field of mathematical optimization, where major classes of optimization problems, such as combinatorial optimization, convex optimization, non-convex optimization, and stochastic extensions, have devoted communities. With these aspects in mind, this work draws on multiple approaches to study quantum optimization. Provably exact versus heuristic settings are first explained using computational complexity theory — highlighting where quantum advantage is possible in each context. Then, the core building blocks for quantum optimization algorithms are outlined to subsequently define prominent problem classes and identify key open questions that, if answered, will advance the field. The effects of scaling relevant problems on noisy quantum devices are also outlined in detail, alongside meaningful benchmarking problems. We underscore the importance of benchmarking by proposing clear metrics to conduct appropriate comparisons with classical optimization techniques. Lastly, we highlight two domains – finance and sustainability – as rich sources of optimization problems that could be used to benchmark, and eventually validate, the potential real-world impact of quantum optimization.

Diagrammatic representations of quantum algorithms and circuits offer novel approaches to their design and analysis.In this work, we describe extensions of the ZX-calculus especially suitable for parameterized quantum circuits, in particular for computing observable expectation values as functions of or for fixed parameters, which are important algorithmic quantities in a variety of applications ranging from combinatorial optimization to quantum chemistry.We provide several new ZX-diagram rewrite rules and generalizations for this setting.In particular, we give formal rules for dealing with linear combinations of ZX-diagrams, where the relative complex-valued scale factors of each diagram must be kept track of, in contrast to most previously studied single-diagram realizations where these coefficients can be effectively ignored.This allows us to directly import a number useful relations from the operator analysis to ZX-calculus setting, including causal cone and quantum gate commutation rules.We demonstrate that the diagrammatic approach offers useful insights into algorithm structure and performance by considering several anstze from the literature including realizations of hardware-efficient anstze and QAOA.We find that by employing a diagrammatic representation, calculations across different anstze can become more intuitive and potentially easier to approach systematically than by alternative means.Finally, we outline how diagrammatic approaches may aid in the design and study of new and more effective quantum circuit anstze.

In this work, we present an in-depth study of average-case quantum distances introduced in [1]. The average-case distances approximate, up to the relative error, the average Total-Variation (TV) distance between measurement outputs of two quantum processes, in which quantum objects of interest (states, measurements, or channels) are intertwined with random quantum circuits. Contrary to conventional distances, such as trace distance or diamond norm, they quantify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">average-case</i> statistical distinguishability via random quantum circuits. We prove that once a family of random circuits forms an δ-approximate 4-design, with δ = <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–8</sup> ), then the average-case distances can be approximated by simple explicit functions that can be expressed via simple degree two polynomials in objects of interest. For systems of moderate dimension, they can be easily explicitly computed – no optimization is needed as opposed to diamond norm distance between channels or operational distance between measurements. We prove that those functions, which we call quantum average-case distances, have a plethora of desirable properties, such as subadditivity w.r.t. tensor products, joint convexity, and (restricted) data-processing inequalities. Notably, all distances utilize the Hilbert-Schmidt (HS) norm, which provides this norm with a new operational interpretation. We also provide upper bounds on the maximal ratio between worst-case and average-case distances, and for each of them, we provide an example that saturates the bound. Specifically, we show that for each dimension <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> this ratio is at most <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2</sup> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3/2</sup> for states, measurements, and channels, respectively. To support the practical usefulness of our findings, we study multiple examples in which average-case quantum distances can be calculated analytically.

Wildfires are one of the major disasters among many and are responsible for more than 6 million acres burned in the United States alone every year. Accurate, insightful, and timely wildfire detection is needed to help authorities mitigate and prevent further destruction. Uncertainty quantification is always a crucial part of the detection of natural disasters, such as wildfires, and modeling products can be misinterpreted without proper uncertainty quantification. In this study, we propose a supervised deep generative machine-learning model that generates stochastic wildfire detection, allowing fast and comprehensive uncertainty quantification for individual and collective events. In the proposed approach, we also aim to address the patchy and discontinuous Moderate Resolution Imaging Spectroradiometer (MODIS) wildfire product by training the proposed model with MODIS raw and combined bands to detect fire. This approach allows us to generate diverse but plausible segmentations to represent the disagreements regarding the delineation of wildfire boundaries by subject matter experts. The proposed approach generates stochastic segmentation via two model streams in which one learns meaningful stochastic latent distributions, and the other learns the visual features. Two model branches join eventually to become a supervised stochastic image-to-image wildfire detection model. The model is compared to two baseline stochastic machine-learning models: (1) with permanent dropout in training and test phases and (2) with Stochastic ReLU activations. The visual and statistical metrics demonstrate better agreements between the ground truth and the proposed model segmentations. Furthermore, we used multiple scenarios to evaluate the model comprehension, and the proposed Probabilistic U-Net model demonstrates a better understanding of the underlying physical dynamics of wildfires compared to the baselines.

O objetivo deste estudo foi relatar a experiência da operacionalização de grupos focais on-line síncronos como técnica de produção de dados para pesquisas de pós-graduação. Relato de experiência sobre a operacionalização de grupos focais on-line junto a docentes e preceptores de graduação em enfermagem de universidades públicas de Mato Grosso entre os meses de outubro e dezembro de 2021. Foram realizados 11 encontros, com 19 participantes distribuídos em três grupos. A operacionalização foi dividida nas etapas: planejamento, envolvendo preparo do roteiro, teste piloto e escolha da plataforma de reunião virtual; seleção dos participantes; execução; e feedback. Os grupos focais on-line são exequíveis, desde que planejados e adaptados à implementação virtual, como a escolha da plataforma de comunicação, pessoas com experiência em tecnologia da informação, fidelidade ao número e seleção dos participantes, duração dos encontros, roteiros norteadores e feedback. Descritores: Grupos Focais, Metodologia, Pesquisa Qualitativa. Operationalization of synchronous online focus groups: experience report Abstract: The objective of this study was to report the experience of using synchronous online focus groups as a data production technique for graduate research. Experience report on the implementation of online focus groups with undergraduate nursing professors and preceptors at public universities in Mato Grosso between october and december 2021. 11 meetings were held, with 19 participants distributed into three groups. Operationalization was divided into stages: planning, involving preparing the script, pilot testing and choosing the virtual meeting platform; selection of participants; execution; and feedback. Online focus groups are feasible, as long as they are planned and adapted to virtual implementation, such as the choice of communication platform, people with experience in information technology, fidelity to the number and selection of participants, duration of meetings, guiding scripts and feedback. Descriptors: Focus Groups, Methodology, Qualitative Research. Operacionalización de grupos focales en línea sincronicos: reporte de experiencia Resumen: El objetivo de este estudio fue relatar la experiencia del uso de grupos focales en línea sincrónicos como técnica de producción de datos para la investigación de posgrado. Informe de experiencia sobre la implementación de grupos focales en línea con profesores y preceptores de graduación en enfermería en universidades públicas de Mato Grosso entre octubre y diciembre de 2021. Se realizaron 11 reuniones, con 19 participantes distribuidos en tres grupos. La operacionalización se dividió en etapas: planificación, que involucra la preparación del guión, la prueba piloto y la elección de la plataforma de reunión virtual; selección de participantes; ejecución; y retroalimentación. Los grupos focales en línea son factibles, siempre que sean planificados y adaptados a la implementación virtual, como la elección de la plataforma de comunicación, personas con experiencia en tecnologías de la información, fidelidad al número y selección de participantes, duración de las reuniones, guiones orientadores y retroalimentación. Descriptores: Grupos Focales, Metodología, Investigación Cualitativa.