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We propose a data-driven pressure distribution rendering method that uses the interpolation of experimentally obtained pressure values. The pressure data were collected using a pressure sensor array. The prediction was performed using linear interpolation, assuming that the pressure distribution is dependent on pushing displacement and contact angle. Leap Motion Controller was used to implement the prediction based on user input. The proposed prediction model was found to be fast and reproduce the measured data well., Comment: Part of proceedings of 6th International Conference AsiaHaptics 2024

This paper is concerned with the initial value problem for a system of one-dimensional fourth-order dispersive partial differential equations on the torus with nonlinearity involving derivatives up to second order. This paper gives sufficient conditions on the coefficients of the system for the initial value problem to be time-locally well-posed in Sobolev spaces with high regularity. The proof is based on the energy method combined with the idea of a gauge transformation and the technique of Bona-Smith type parabolic regularization. The sufficient conditions can been found in connection with geometric analysis on a fourth-order geometric dispersive partial differential equation for curve flows on a compact locally Hermitian symmetric space., Comment: 37 pages

We present deep Subaru/FOCAS spectra for two extreme emission line galaxies (EELGs) at $z\sim 1$ with strong {\sc[Oiii]}$\lambda$5007 emission lines, exhibiting equivalent widths (EWs) of $2905^{+946}_{-578}$ \AA\ and $2000^{+188}_{-159}$ \AA, comparable to those of EELGs at high redshifts that are now routinely identified with JWST spectroscopy. Adding a similarly large {\sc [Oiii]} EW ($2508^{+1487}_{-689}$ \AA) EELG found at $z\sim 2$ in the JWST CEERS survey to our sample, we explore for the physical origins of the large {\sc [Oiii]} EWs of these three galaxies with the Subaru spectra and various public data including JWST/NIRSpec, NIRCam, and MIRI data. While there are no clear signatures of AGN identified by the optical line diagnostics, we find that two out of two galaxies covered by the MIRI data show strong near-infrared excess in the spectral energy distributions (SEDs) indicating obscured AGN. Because none of the three galaxies show clear broad H$\beta$ lines, the upper limits on the flux ratios of broad-H$\beta$ to {\sc [Oiii]} lines are small, $\lesssim 0.15$ that are comparable with Seyfert $1.8-2.0$ galaxies. We conduct \texttt{Cloudy} modeling with the stellar and AGN incident spectra, allowing a wide range of parameters including metallicities and ionization parameters. We find that the large {\sc [Oiii]} EWs are not self-consistently reproduced by the spectra of stars or unobscured AGN, but obscured AGN that efficiently produces O$^{++}$ ionizing photons with weak nuclear and stellar continua that are consistent with the SED shapes., Comment: submitted to ApJ

We study the asymptotic behavior of a multiple series of Mordell-Tornheim type and its integral analogue at x=0. Our approach is to show a relation between the multiple series and its integral analogue by using Abel's summation formula, and to deeply investigate the behavior of the integral analogue. Additionally, we establish some nontrivial relations among multiple polylogarithms by comparing two seemingly different asymptotic formulas for the integral analogue., Comment: 30pages

This paper investigates the initial value problem for a system of one-dimensional fourth-order dispersive partial differential-integral equations with nonlinearity involving derivatives up to second order. Examples of the system arise in relation with nonlinear science and geometric analysis. Applying the energy method based on the idea of a gauge transformation and Bona-Smith approximation technique, we prove that the initial value problem is time-locally well-posed on the real line for initial data in a Sobolev space with high regularity., Comment: 42 pages

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models., Comment: 29 pages, 4 figures

The fidelity of certain gates on noisy quantum computers may be improved when they are implemented using more than two levels of the involved transmons. The main impediments to achieving this potential are the dynamic gate phase errors that cannot be corrected via calibration. The standard tool for countering such phase errors in two-level qubits is the echo protocol, often referred to as the dynamical decoupling sequence, where the evolution of a qubit is punctuated by an even number of X gates. We introduce basis cycling, which is a direct generalization of the qubit echo to general qudits, and provide an analytic framework for designing gate sequences to produce desired effects using this technique. We then apply basis cycling to a Toffoli gate decomposition incorporating a qutrit and obtain CCZ gate fidelity values up to 93.8$\pm$0.1%, measured by quantum process tomography, on IBM quantum computers. The gate fidelity remains stable without recalibration even while the resonant frequency of the qutrit fluctuates, highlighting the dynamical nature of phase error cancellation through basis cycling. Our results demonstrate that one of the biggest difficulties in implementing qudit-based gate decompositions on superconducting quantum computers can be systematically overcome when certain conditions are met, and thus open a path toward fulfilling the promise of qudits as circuit optimization agents., Comment: 17 pages, 9 figures

This paper focuses on a one-dimensional fourth-order nonlinear dispersive partial differential equation for curve flows on a K\"ahler manifold. The equation arises as a fourth-order extension of the one-dimensional Schr\"odinger flow equation, with physical and geometrical backgrounds. First, this paper presents a framework that can transform the equation into a system of fourth-order nonlinear dispersive partial differential-integral equations for complex-valued functions. This is achieved by developing the so-called generalized Hasimoto transformation, which enables us to handle general higher-dimensional compact K\"ahler manifolds. Second, this paper demonstrates the computations to obtain the explicit expression of the derived system for three examples of the compact K\"ahler manifolds, dealing with the complex Grassmannian as an example in detail., Comment: 46 pages

We measure the evolution of the rest-frame $NUV-V$ colors for early-type galaxies in clusters at $03$. The blue UV colors of early-type galaxies at low-intermediate redshifts are likely driven by this sub-population being enriched in helium up to $\sim44\%$. At $z>0.8$ (when the extra UV component has not yet appeared) the data allows us to constrain the star formation histories of galaxies by fitting models to the evolution of their UV colors: we find that the epoch at which the stellar populations formed ranges between $3

Solving partial differential equations for extremely large-scale systems within a feasible computation time serves in accelerating engineering developments. Quantum computing algorithms, particularly the Hamiltonian simulations, present a potential and promising approach to achieve this purpose. Actually, there are several oracle-based Hamiltonian simulations with potential quantum speedup, but their detailed implementations and accordingly the detailed computational complexities are all unclear. This paper presents a method that enables us to explicitly implement the quantum circuit for Hamiltonian simulation; the key technique is the explicit gate construction of differential operators contained in the target partial differential equation discretized by the finite difference method. Moreover, we show that the space and time complexities of the constructed circuit are exponentially smaller than those of conventional classical algorithms. We also provide numerical experiments and an experiment on a real device for the wave equation to demonstrate the validity of our proposed method., Comment: 25 pages, 6 figures

On-chip photonic processors for neural networks have potential benefits in both speed and energy efficiency but have not yet reached the scale at which they can outperform electronic processors. The dominant paradigm for designing on-chip photonics is to make networks of relatively bulky discrete components connected by one-dimensional waveguides. A far more compact alternative is to avoid explicitly defining any components and instead sculpt the continuous substrate of the photonic processor to directly perform the computation using waves freely propagating in two dimensions. We propose and demonstrate a device whose refractive index as a function of space, $n(x,z)$, can be rapidly reprogrammed, allowing arbitrary control over the wave propagation in the device. Our device, a 2D-programmable waveguide, combines photoconductive gain with the electro-optic effect to achieve massively parallel modulation of the refractive index of a slab waveguide, with an index modulation depth of $10^{-3}$ and approximately $10^4$ programmable degrees of freedom. We used a prototype device with a functional area of $12\,\text{mm}^2$ to perform neural-network inference with up to 49-dimensional input vectors in a single pass, achieving 96% accuracy on vowel classification and 86% accuracy on $7 \times 7$-pixel MNIST handwritten-digit classification. This is a scale beyond that of previous photonic chips relying on discrete components, illustrating the benefit of the continuous-waves paradigm. In principle, with large enough chip area, the reprogrammability of the device's refractive index distribution enables the reconfigurable realization of any passive, linear photonic circuit or device. This promises the development of more compact and versatile photonic systems for a wide range of applications, including optical processing, smart sensing, spectroscopy, and optical communications.

Multimode squeezed states of light have been proposed as a resource for achieving quantum advantage in computing and sensing. Recent experiments that demonstrate multimode Gaussian states to this end have most commonly opted for spatial or temporal modes, whereas a complete system based on frequency modes has yet to be realized. Instead, we show how to use the frequency modes simultaneously squeezed in a conventional, single-spatial-mode, optical parametric amplifier when pumped by ultrashort pulses. Specifically, we show how adiabatic frequency conversion can be used not only to convert the quantum state from infrared to visible wavelengths, but to concurrently manipulate the joint spectrum. This near unity-efficiency quantum frequency conversion, over a bandwidth >45 THz and, to our knowledge, the broadest to date, allows us to measure the state with an electron-multiplying CCD (EMCCD) camera-based spectrometer, at non-cryogenic temperatures. We demonstrate the squeezing of >400 frequency modes, with a mean of approximately 700 visible photons per shot. Our work shows how many-mode quantum states of light can be generated, manipulated, and measured with efficient use of hardware resources -- in our case, using one pulsed laser, two nonlinear crystals, and one camera. This ability to produce, with modest hardware resources, large multimode squeezed states with partial programmability motivates the use of frequency encoding for photonics-based quantum information processing.

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training is efficient in the number of required runs of the quantum processor and takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on superconducting circuits to classify microwave signals that are analog -- continuous in time. However, while theoretical proposals of analog QRC exist, to date QRC has been implemented using circuit-model quantum systems -- imposing a discretization of the incoming signal in time, with each time point input by executing a gate operation. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our quantum system was operated without artificially discretizing the input data, directly taking in microwave signals. Our work does not attempt to address the question of whether QRCs could provide a quantum computational advantage in classifying pre-recorded classical signals. However, beyond illustrating that sophisticated tasks can be performed with a modest-size quantum system and inexpensive training, our work opens up the possibility of achieving a different kind of advantage than a purely computational advantage: superconducting circuits can act as extremely sensitive detectors of microwave photons; our work demonstrates processing of ultra-low-power microwave signals in our superconducting circuit, and by combining sensitive detection with QRC processing within the same system, one could achieve a quantum sensing-computational advantage, i.e., an advantage in the overall analysis of microwave signals comprising just a few photons.

We use K-band multi-object near-infrared spectroscopy with Keck/MOSFIRE to search for environmental imprints on the gas properties of 27 narrow-band selected H$\alpha$ emitters (HAEs) across the three major clumps of the assembling USS1558--003 protocluster at $z=2.53$. We target the H$\alpha$ and [NII]$\lambda$6584 emission lines to obtain star-formation rates (SFR) and gas-phase oxygen abundances for our sources, confirming the membership of 23 objects. HAEs belonging to this protocluster display enhanced SFRs with respect to the main sequence of star formation at the same cosmic epoch. This effect is more prominent for low-mass galaxies ($\mathrm{\log M_*/M_\odot<10.0}$), which may be experiencing a vigorous phase of mass assembly shortly after they were formed. We compute the individual and stacked gas-phase metallicities for our sources finding a metallicity deficit for low-mass objects when compared against the field mass-metallicity relation and the massive Spiderweb protocluster at $z=2.16$. These results suggest that HAEs within USS1558--003 may be less evolved than those in the Spiderweb protocluster. Finally, we explore the gas metallicity - gas fraction relation for a small sample of five galaxies with CO(3-2) molecular gas information. Assuming our objects are in equilibrium, we obtain a relatively wide range of mass loading factors ($\mathrm{\lambda=0.5-2}$) matching field samples at the cosmic noon but in contrast with our previous results in the Spiderweb protocluster. We speculate that these discrepancies between protoclusters may be (partly) driven by differences in their current dynamical and mass assembly stages, hinting at the co-evolution of protoclusters and their galaxy populations at $2

Over the last few decades, nonlinear optics has become significantly more nonlinear, traversing nearly a billionfold improvement in energy efficiency, with ultrafast nonlinear nanophotonics in particular emerging as a frontier for combining both spatial and temporal engineering. At present, cutting-edge experiments in nonlinear nanophotonics place us just above the mesoscopic regime, where a few hundred photons suffice to trigger nonlinear saturation. In contrast to classical or deep-quantum optics, the mesoscale is characterized by dynamical interactions between mean-field, Gaussian, and non-Gaussian quantum features, all within a close hierarchy of scales. When combined with the inherent multimode complexity of optical fields, such hybrid quantum-classical dynamics present theoretical, experimental, and engineering challenges to the contemporary framework of quantum optics. In this review, we highlight the unique physics that emerges in multimode nonlinear optics at the mesoscale and outline key principles for exploiting both classical and quantum features to engineer novel functionalities. We briefly survey the experimental landscape and draw attention to outstanding technical challenges in materials, dispersion engineering, and device design for accessing mesoscopic operation. Finally, we speculate on how these capabilities might usher in some new paradigms in quantum photonics, from quantum-augmented information processing to nonclassical-light-driven dynamics and phenomena to all-optical non-Gaussian measurement and sensing. The physics unlocked at the mesoscale present significant challenges and opportunities in theory and experiment alike, and this review is intended to serve as a guidepost as we begin to navigate this new frontier in ultrafast quantum nonlinear optics., Comment: The first two authors contributed equally to this work; 26 pages, 7 figures

We report on discovery of a concentration of massive quiescent galaxies located at z=4. The concentration is first identified using high-quality photometric redshifts based on deep, mutli-band data in Subaru/XMM-Newton Deep Field. Follow-up near-infrared spectroscopic observations with MOSFIRE on Keck confirm a massive (~10^{11} Msun) quiescent galaxy at z=3.99. Our spectral energy distribution (SED) analyses reveal that the galaxy experienced an episode of starburst about 500 Myr prior to the observed epoch, followed by rapid quenching. As its spectrum is sufficiently good to measure the stellar velocity dispersion, we infer its dynamical mass and find that it is consistent with its stellar mass. The galaxy is surrounded by 4 massive (>10^{10} Msun) quiescent galaxies on a ~1 physical Mpc scale, all of which are consistent with being located at the same redshift based on high-accuracy spectro-photometric redshifts. This is likely a (proto-)cluster dominated by quiescent galaxies, the first of the kind reported at such a high redshift as z=4. Interestingly, it is in a large-scale structure revealed by spectroscopic redshifts from VANDELS. Furthermore, it exhibits the red sequence, adding further support to the physical concentration of the galaxies. We find no such concentration in the Illustris-TNG300 simulation; it may be that the cluster is such a rare system that the simulation box is not sufficiently large to reproduce it. The total halo mass of the quiescent galaxies is ~10^{13} Msun, suggesting that they form a group-sized halo once they collapse together. We discuss implications of our findings for the quenching physics and conclude with future prospects., Comment: 16 pages, 12 figures, submitted to ApJ

In broadband quantum optical systems, nonlinear interactions among a large number of frequency components induce complex dynamics that may defy heuristic analysis. In this work we introduce a perturbative framework for factoring out reservoir degrees of freedom and establishing a concise effective model (effective field theory) for the remaining system. Our approach combines approximate diagonalization of judiciously partitioned subsystems with master equation techniques. We consider cascaded optical $\chi^{(2)}$ (quadratic) nonlinearities as an example and show that the dynamics can be construed (to leading order) as self-phase modulations of dressed fundamental modes plus cross-phase modulations of dressed fundamental and second-harmonic modes. We then formally eliminate the second-harmonic degrees of freedom and identify emergent features of the fundamental wave dynamics, such as two-photon loss channels, and examine conditions for accuracy of the reduced model in dispersive and dissipative parameter regimes. Our results highlight the utility of system-reservoir methods for deriving accurate, intuitive reduced models for complex dynamics in broadband nonlinear quantum photonics., Comment: 25 pages, 8 figures

A term in a corpus is said to be ``bursty'' (or overdispersed) when its occurrences are concentrated in few out of many documents. In this paper, we propose Residual Inverse Collection Frequency (RICF), a statistical significance test inspired heuristic for quantifying term burstiness. The chi-squared test is, to our knowledge, the sole test of statistical significance among existing term burstiness measures. Chi-squared test term burstiness scores are computed from the collection frequency statistic (i.e., the proportion that a specified term constitutes in relation to all terms within a corpus). However, the document frequency of a term (i.e., the proportion of documents within a corpus in which a specific term occurs) is exploited by certain other widely used term burstiness measures. RICF addresses this shortcoming of the chi-squared test by virtue of its term burstiness scores systematically incorporating both the collection frequency and document frequency statistics. We evaluate the RICF measure on a domain-specific technical terminology extraction task using the GENIA Term corpus benchmark, which comprises 2,000 annotated biomedical article abstracts. RICF generally outperformed the chi-squared test in terms of precision at k score with percent improvements of 0.00% (P@10), 6.38% (P@50), 6.38% (P@100), 2.27% (P@500), 2.61% (P@1000), and 1.90% (P@5000). Furthermore, RICF performance was competitive with the performances of other well-established measures of term burstiness. Based on these findings, we consider our contributions in this paper as a promising starting point for future exploration in leveraging statistical significance testing in text analysis., Comment: 19 pages, 1 figure, 6 tables

We report on the spectroscopic confirmation of a massive quiescent galaxy at $z_\mathrm{spec}=4.53$ in the COSMOS field. The object was first identified as a galaxy with suppressed star formation at $z_\mathrm{phot}\sim4.65$ from the COSMOS2020 catalog. The follow-up spectroscopy with Keck/MOSFIRE in the $K$-band reveals faint [OII] emission and the Balmer break, indicative of evolved stellar populations. We perform the spectral energy distribution fitting using photometry and spectrum to infer physical properties. The obtained stellar mass is high ($M_*\sim 10^{10.8}\,M_\odot$) and the current star formation rate is more than 1 dex below that of main-sequence galaxies at $z=4.5$. Its star formation history suggests that this galaxy experienced rapid quenching from $z\sim 5$. The galaxy is among the youngest quiescent galaxies confirmed so far at $z_\mathrm{spec}>3$ with $z_\mathrm{form}\sim5.2$ ($200\,\mathrm{Myr}$ ago), which is the epoch when 50\% of total stellar mass was formed. A unique aspect of the galaxy is that it is in an extremely dense region; there are four massive star-forming galaxies at $4.4

This paper presents an ensemble data assimilation method using the pseudo ensembles generated by denoising diffusion probabilistic model. Since the model is trained against noisy and sparse observation data, this model can produce divergent ensembles close to observations. Thanks to the variance in generated ensembles, our proposed method displays better performance than the well-established ensemble data assimilation method when the simulation model is imperfect.

The ensemble data assimilation of computational fluid dynamics simulations based on the lattice Boltzmann method (LBM) and the local ensemble transform Kalman filter (LETKF) is implemented and optimized on a GPU supercomputer based on NVIDIA A100 GPUs. To connect the LBM and LETKF parts, data transpose communication is optimized by overlapping computation, file I/O, and communication based on data dependency in each LETKF kernel. In two dimensional forced isotropic turbulence simulations with the ensemble size of $M=64$ and the number of grid points of $N_x=128^2$, the optimized implementation achieved $\times3.80$ speedup from the naive implementation, in which the LETKF part is not parallelized. The main computing kernel of the local problem is the eigenvalue decomposition (EVD) of $M\times M$ real symmetric dense matrices, which is computed by a newly developed batched EVD in $\verb|EigenG|$. The batched EVD in $\verb|EigenG|$ outperforms that in $\verb|cuSOLVER|$, and $\times65.3$ speedup was achieved.

We investigate the applicability of the data assimilation (DA) to large eddy simulations (LESs) based on the lattice Boltzmann method (LBM). We carry out the observing system simulation experiment of a two-dimensional (2D) forced isotropic turbulence, and examine the DA accuracy of the nudging and the local ensemble transform Kalman filter (LETKF) with spatially sparse and noisy observation data of flow fields. The advantage of the LETKF is that it does not require computing spatial interpolation and/or an inverse problem between the macroscopic variables (the density and the pressure) and the velocity distribution function of the LBM, while the nudging introduces additional models for them. The numerical experiments with $256\times256$ grids and $10\%$ observation noise in the velocity showed that the root mean square error of the velocity in the LETKF with $8\times 8$ observation points ($\sim 0.1\%$ of the total grids) and 64 ensemble members becomes smaller than the observation noise, while the nudging requires an order of magnitude larger number of observation points to achieve the same accuracy. Another advantage of the LETKF is that it well keeps the amplitude of the energy spectrum, while only the phase error becomes larger with more sparse observation. We also see that a lack of observation data in the LETKF produces a spurious energy injection in high wavenumber regimes, leading to numerical instability. Such numerical instability is known as the catastrophic filter divergence problem, which can be suppressed by increasing the number of ensemble members. From these results, it was shown that the LETKF enables robust and accurate DA for the 2D LBM with sparse and noisy observation data., Comment: 27 pages, 14 figures

We present the results of a systematic study of the rest-frame optical morphology of quiescent galaxies at $z \geq 3$ using the Near-Infrared Camera (NIRCam) onboard $JWST$. Based on a sample selected by $UVJ$ color or $NUVUVJ$ color, we focus on 26 quiescent galaxies with $9.8<\log{(M_\star/M_\odot)}<11.4$ at $2.810.3$ implies that our size - stellar mass relations are below those at lower redshifts, with the amplitude of $\sim0.6\, {\rm kpc}$ at $M_\star = 5\times 10^{10}\, M_\odot$. This value agrees with the extrapolation from the size evolution of quiescent galaxies at $z<3$ in the literature, implying that the size of quiescent galaxies increases monotonically from $z\sim3-5$. Our sample is mainly composed of galaxies with bulge-like structures according to their median S\'ersic index and axis ratio of $n\sim3-4$ and $q\sim0.6-0.8$, respectively. On the other hand, there is a trend of increasing fraction of galaxies with low S\'ersic index, suggesting $3

We present the results of ALMA-ACA 7 m-array observations in $^{12}$CO($J=2-1$), $^{13}$CO($J=2-1$), and C$^{18}$O($J=2-1$) line emission toward the molecular-gas disk in the Local Group spiral galaxy M33 at an angular resolution of 7".31 $\times$ 6".50 (30 pc $\times$ 26 pc). We combined the ACA 7 m-array $^{12}$CO($J=2-1$) data with the IRAM 30 m data to compensate for emission from diffuse molecular-gas components. The ACA+IRAM combined $^{12}$CO($J=2-1$) map clearly depicts the cloud-scale molecular-gas structure over the M33 disk. Based on the ACA+IRAM $^{12}$CO($J=2-1$) cube data, we cataloged 848 molecular clouds with a mass range from $10^3$ $M_{\odot}$ to $10^6$ $M_{\odot}$. We found that high-mass clouds ($\geq 10^5 M_{\odot}$) tend to associate with the $8 \mu$m-bright sources in the spiral arm region, while low-mass clouds ($< 10^5 M_{\odot}$) tend to be apart from such $8 \mu$m-bright sources and to exist in the inter-arm region. We compared the cataloged clouds with GMCs observed by the IRAM 30 m telescope at 49 pc resolution (IRAM GMC: Corbelli et al. 2017), and found that a small IRAM GMC is likely to be identified as a single molecular cloud even in ACA+IRAM CO data, while a large IRAM GMC can be resolved into multiple ACA+IRAM clouds. The velocity dispersion of a large IRAM GMC is mainly dominated by the line-of-sight velocity difference between small clouds inside the GMC rather than the internal cloud velocity broadening., Comment: 23 pages, 17 figures, accepted for publication in the Astrophysical Journal

Quantum machine learning with parametrised quantum circuits has attracted significant attention over the past years as an early application for the era of noisy quantum processors. However, the possibility of achieving concrete advantages over classical counterparts in practical learning tasks is yet to be demonstrated. A promising avenue to explore potential advantages is the learning of data generated by quantum mechanical systems and presented in an inherently quantum mechanical form. In this article, we explore the applicability of quantum-data learning to practical problems in high-energy physics, aiming to identify domain specific use-cases where quantum models can be employed. We consider quantum states governed by one-dimensional lattice gauge theories and a phenomenological quantum field theory in particle physics, generated by digital quantum simulations or variational methods to approximate target states. We make use of an ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states in the Schwinger model, (de)confinement phases from time-evolved states in the $\mathbb{Z}_2$ gauge theory, and that it can extract fermion flavor/coupling constants in a quantum simulation of parton shower. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics., Comment: 16 pages, 14 figures