Since quantum states are higher dimensional objects that can only be seen via observables, our visualization method, which inherits the similarity of quantum data, would be useful in understanding the behavior of quantum circuits and algorithms.
In this paper, we introduce the quantum adaptive distribution search (QuADS), a quantum continuous optimization algorithm that integrates Grover adaptive search (GAS) with the covariance matrix adaptation - evolution strategy (CMA-ES), a classical technique for continuous optimization.
The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario.
We evaluated the system on a running practice video dataset and showed that the proposed method identified runners with higher accuracy than one of the state-of-the-art models in unsupervised re-identification.
The original training set of matches and features, which was provided for the competition, was augmented with additional matches that were played between 4 April and 13 April 2023, representing the period after which the training set ended, but prior to the first matches that were to be predicted (upon which the performance was evaluated).
To address this issue, we proposed a novel framework to analyze such scenarios based on game theory, where we estimate the expected payoff with machine learning (ML) models, and additional features for ML models were extracted with a theory-based shot block model.
In fact, also the Machine Learning research related to quantum computers undertakes a dual challenge: enhancing machine learning exploiting the power of quantum computers, while also leveraging state-of-the-art classical machine learning methodologies to help the advancement of quantum computing.
In this paper, we propose a method of valuing possible actions for on- and off-ball soccer players in a single holistic framework based on multi-agent deep reinforcement learning.
In the experiments, using chase-and-escape and football tasks with the different dynamics between the unknown source and target environments, we show that our approach achieved a balance between the reproducibility and the generalization ability compared with the baselines.
In this work, we present the first annotated drone dataset from top and back views in badminton doubles and propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance.
We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field.
Ranked #1 on Lane Detection on nuScenes
However, most sports sequential events modeling methods and performance metrics approaches could be incomprehensive in dealing with such large-scale spatiotemporal data (in particular, temporal process), thereby necessitating a more comprehensive spatiotemporal model and a holistic performance metric.
By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications.
Using the open-source location data of all players in broadcast video frames in football games of men's Euro 2020 and women's Euro 2022, we investigated the effect of the number of players on the prediction and validated our approach by analyzing the games.
We also revealed that the machine learning model detects faults according to the rules of race walking.
Finally, we evaluate the tracking accuracy among a GNSS, fish-eye camera and drone camera data.
However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates.
The weighted on base average (wOBA) is well known as a measure of an batter's hitting contribution.
Evaluation of intervention in a multi-agent system, e. g., when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields.
In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data.
Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data.
Recent advances in reinforcement learning (RL) have made it possible to develop sophisticated agents that excel in a wide range of applications.
In this work, we explore a quantum machine learning model with a deep parameterized quantum circuit and aim to go beyond the conventional quantum kernel method.
1 code implementation • • Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara
In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.
Results show that the proposed classifiers predicted the true events (mean F1 score $>$ 0. 483) better than the existing classifiers which were based on rare events or goals (mean F1 score $<$ 0. 201).
Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data.
This survey focuses on data-driven analysis for quantitative understanding of invasion team sports behaviors such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways.
1 code implementation • 16 Dec 2020 • M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, Patrick J. Coles
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost.
Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence.
In the early years of fault-tolerant quantum computing (FTQC), it is expected that the available code distance and the number of magic states will be restricted due to the limited scalability of quantum devices and the insufficient computational power of classical decoding units.
Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields.
The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution.
To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches.
A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties.
no code implementations • 5 Mar 2019 • Philip Bambade, Tim Barklow, Ties Behnke, Mikael Berggren, James Brau, Philip Burrows, Dmitri Denisov, Angeles Faus-Golfe, Brian Foster, Keisuke Fujii, Juan Fuster, Frank Gaede, Paul Grannis, Christophe Grojean, Andrew Hutton, Benno List, Jenny List, Shinichiro Michizono, Akiya Miyamoto, Olivier Napoly, Michael Peskin, Roman Poeschl, Frank Simon, Jan Strube, Junping Tian, Maksym Titov, Marcel Vos, Andrew White, Graham Wilson, Akira Yamamoto, Hitoshi Yamamoto, Kaoru Yokoya
In this report, we review of all aspects of the ILC program: the physics motivation, the accelerator design, the run plan, the proposed detectors, the experimental measurements on the Higgs boson, the top quark, the couplings of the W and Z bosons, and searches for new particles.
High Energy Physics - Experiment High Energy Physics - Phenomenology Accelerator Physics
However, in certain cases, the indirect measurement can be reduced to the direct measurement, where a quantum state is destructively measured.
In this paper, we formulate Koopman spectral analysis for NLDSs with structures among observables and propose an estimation algorithm for this problem.
The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely.
The development of a metric for structural data is a long-term problem in pattern recognition and machine learning.
Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.
To reduce this requirement, we propose a high-threshold fault-tolerant quantum computation with GKP qubits using topologically protected measurement-based quantum computation with the surface code.
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end.