Search

State-of-the-art retrieval models typically address a straightforward search scenario, where retrieval tasks are fixed (e.g., finding a passage to answer a specific question) and only a single modality is supported for both queries and retrieved results. This paper introduces techniques for advancing information retrieval with multimodal large language models (MLLMs), enabling a broader search scenario, termed universal multimodal retrieval, where multiple modalities and diverse retrieval tasks are accommodated. To this end, we first study fine-tuning an MLLM as a bi-encoder retriever on 10 datasets with 16 retrieval tasks. Our empirical results show that the fine-tuned MLLM retriever is capable of understanding challenging queries, composed of both text and image, but underperforms a smaller CLIP retriever in cross-modal retrieval tasks due to modality bias from MLLMs. To address the issue, we propose modality-aware hard negative mining to mitigate the modality bias exhibited by MLLM retrievers. Second, we propose to continually fine-tune the universal multimodal retriever to enhance its text retrieval capability while maintaining multimodal retrieval capability. As a result, our model, MM-Embed, achieves state-of-the-art performance on the multimodal retrieval benchmark M-BEIR, which spans multiple domains and tasks, while also surpassing the state-of-the-art text retrieval model, NV-Embed-v1, on MTEB retrieval benchmark. Finally, we explore to prompt the off-the-shelf MLLMs as the zero-shot rerankers to refine the ranking of the candidates from the multimodal retriever. We find that through prompt-and-reranking, MLLMs can further improve multimodal retrieval when the user queries (e.g., text-image composed queries) are more complex and challenging to understand. These findings also pave the way to advance universal multimodal retrieval in the future., Comment: We release the model weights at: https://huggingface.co/nvidia/MM-Embed

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

As the scale of models and training data continues to grow, there is an expanding reliance on more GPUs to train large-scale models, which inevitably increases the likelihood of encountering dynamic stragglers that some devices lag behind in performance occasionally. However, hybrid parallel training, one of the de facto paradigms to train large models, is typically sensitive to the stragglers. This paper presents Malleus, a straggler-resilient hybrid parallel training framework for large-scale models. Malleus captures the dynamic straggler issues at the nuanced, per-GPU granularity during training. Once a shift in the GPU ability is detected, Malleus adaptively adjusts the parallelization of GPU devices, pipeline stages, model layers, and training data through a novel planning algorithm, accommodating the dynamic stragglers in real time. In addition, Malleus seamlessly and efficiently migrates the model states to fulfill the adjusted parallelization plan on the fly, without sacrificing the stability of the training tasks. Empirical results on large language models with up to 110B parameters show that Malleus consistently outperforms existing parallel training frameworks under various straggler situations, delivering on average 2.63-5.28 times of efficiency improvement.

Inverse Compton (IC) emission associated with the non-thermal component of the intracluster medium (ICM) has been a long sought phenomenon in cluster physics. Traditional spectral fitting often suffers from the degeneracy between the two-temperature thermal spectrum (2T) and the one-temperature plus IC power-law spectrum (1T+IC). We present a semi-supervised deep learning approach to search for IC emission in galaxy clusters. We employ a conditional autoencoder (CAE), which is based on an autoencoder with latent representations trained to constrain the thermal parameters of the ICM. The algorithm is trained and tested using synthetic NuSTAR X-ray spectra with instrumental and astrophysical backgrounds included. The training data set only contains 2T spectra, which is more common than 1T+IC spectra. Anomaly detection is performed on the validation and test datasets, consisting of 2T spectra as the normal set and 1T+IC spectra as anomalies. With a threshold anomaly score, chosen based on cross-validation, our algorithm is able to identify spectra that contain an IC component in the test dataset, with a balanced accuracy (BAcc) of 0.64, which outperforms traditional spectral fitting (BAcc = 0.55) and ordinary autoencoder (BAcc = 0.55). Traditional spectral fitting is better at identifying IC cases among true IC spectra (a better recall), while IC predictions made by CAE have a higher chance of being true IC cases (a better precision), demonstrating their mutual complement to each other., Comment: 11 pages, 8 figures, accepted by ApJ

The strength of the force between heavy quarks is studied for heavy quarkonium ($\mathrm{Q\bar{Q}}$) and doubly heavy baryons ($\mathrm{QQq}$) at finite temperature and rapidity using the gauge/gravity duality in this paper. The strength of the interaction is defined as an effective running coupling $\alpha$ from lattice. By considering the $\mathrm{Q\bar{Q}}$ and $\mathrm{QQq}$ moving through the thermal quark-gluon plasma, we find that the interaction of heavy quarks for $\mathrm{Q\bar{Q}}$ is small and remains almost constant when $L<0.2\,\mathrm{fm}$. The strength of the interaction for $\mathrm{QQq}$ is always less than that for $\mathrm{Q\bar{Q}}$ and approaches a constant when $L<0.4\,\mathrm{fm}$. The $\alpha$ and the maximum of effective running coupling $\tilde{\alpha}$ for both $\mathrm{Q\bar{Q}}$ and $\mathrm{QQq}$ decrease with increasing temperature or rapidity. Through comparison, we find that $\mathrm{QQq}$ is more sensitive to changes in temperature and rapidity, and the interaction force between quarks is always less than that of $\mathrm{Q\bar{Q}}$, indicating that $\mathrm{Q\bar{Q}}$ is more stable than $\mathrm{QQq}$ in the presence of temperature and rapidity.

Information retrieval across different languages is an increasingly important challenge in natural language processing. Recent approaches based on multilingual pre-trained language models have achieved remarkable success, yet they often optimize for either monolingual, cross-lingual, or multilingual retrieval performance at the expense of others. This paper proposes a novel hybrid batch training strategy to simultaneously improve zero-shot retrieval performance across monolingual, cross-lingual, and multilingual settings while mitigating language bias. The approach fine-tunes multilingual language models using a mix of monolingual and cross-lingual question-answer pair batches sampled based on dataset size. Experiments on XQuAD-R, MLQA-R, and MIRACL benchmark datasets show that the proposed method consistently achieves comparable or superior results in zero-shot retrieval across various languages and retrieval tasks compared to monolingual-only or cross-lingual-only training. Hybrid batch training also substantially reduces language bias in multilingual retrieval compared to monolingual training. These results demonstrate the effectiveness of the proposed approach for learning language-agnostic representations that enable strong zero-shot retrieval performance across diverse languages., Comment: 15 pages, 2 figures, 13 tables

The holographic Schwinger effect is investigated in systems with Nf = 0, Nf = 2, and Nf = 2+1 using the Einstein-Maxwell-Dilaton (EMD) model, incorporating equation of state and baryon number susceptibility information from lattice QCD. It is found that the critical electric field is smallest for Nf = 0, indicating that the Schwinger effect is more likely to occur than in systems with Nf = 2 and Nf = 2+ 1. The critical electric field decreases with increasing chemical potential and temperature across all systems. Additionally, potential analysis confirms that the maximum total potential energy increases with the number of flavors, suggesting that existing particles may reduce the probability of particle pair production.

Advances in attosecond core-level spectroscopies have successfully unlocked the fastest dynamics involving high-energy electrons. Yet, these techniques are not conventionally regarded as an appropriate probe for low-energy quasiparticle interactions that govern the ground state of quantum materials, nor for studying long-range order because of their limited sensitivity to local charge environments. Here, by employing a unique cryogenic attosecond beamline, we identified clear core-level signatures of long-range charge-density-wave (CDW) formation in a quasi-2D excitonic insulator candidate, even though equilibrium photoemission and absorption measurements of the same core levels showed no spectroscopic singularity at the phase transition. Leveraging the high time resolution and intrinsic sensitivity to short-range charge excitations in attosecond core-level absorption, we observed compelling time-domain evidence for excitonic correlations in the normal-state of the material, whose presence has been subjected to a long-standing debate in equilibrium experiments because of interfering phonon fluctuations in a similar part of the phase space. Our findings support the scenario that short-range excitonic fluctuations prelude long-range order formation in the ground state, providing important insights in the mechanism of exciton condensation in a quasi-low-dimensional system. These results further demonstrate the importance of a simultaneous access to long- and short-range order with underlying dynamical processes spanning a multitude of time- and energy-scales, making attosecond spectroscopy an indispensable tool for both understanding the equilibrium phase diagram and for discovering novel, nonequilibrium states in strongly correlated materials.

In the real world, documents are organized in different formats and varied modalities. Traditional retrieval pipelines require tailored document parsing techniques and content extraction modules to prepare input for indexing. This process is tedious, prone to errors, and has information loss. To this end, we propose Document Screenshot Embedding} (DSE), a novel retrieval paradigm that regards document screenshots as a unified input format, which does not require any content extraction preprocess and preserves all the information in a document (e.g., text, image and layout). DSE leverages a large vision-language model to directly encode document screenshots into dense representations for retrieval. To evaluate our method, we first craft the dataset of Wiki-SS, a 1.3M Wikipedia web page screenshots as the corpus to answer the questions from the Natural Questions dataset. In such a text-intensive document retrieval setting, DSE shows competitive effectiveness compared to other text retrieval methods relying on parsing. For example, DSE outperforms BM25 by 17 points in top-1 retrieval accuracy. Additionally, in a mixed-modality task of slide retrieval, DSE significantly outperforms OCR text retrieval methods by over 15 points in nDCG@10. These experiments show that DSE is an effective document retrieval paradigm for diverse types of documents. Model checkpoints, code, and Wiki-SS collection will be released.

Molecular deuteration is commonly seen in starless cores and is expected to occur on a timescale comparable to that of the core contraction. Thus, the deuteration serves as a chemical clock, allowing us to investigate dynamical theories of core formation. We aim to provide a 3D cloud description for the starless core L 1498 located in the nearby low-mass star-forming region Taurus, and explore the possible core formation mechanism of L 1498. We carried out non-local thermal equilibrium radiative transfer with multi-transition observations of the high-density tracer N$_2$H$^+$ to derive the density and temperature profiles of the L 1498 core. Combining with the spectral observations of the deuterated species, ortho-H$_2$D$^+$, N$_2$D$^+$, and DCO$^+$, we derived the abundance profiles for observed species and performed chemical modeling of the deuteration profiles across L 1498 to constrain the contraction timescale. We present the first ortho-H$_2$D$^+$ (1$_{10}$-1$_{11}$) detection toward L 1498. We find a peak molecular hydrogen density of $1.6_{-0.3}^{+3.0}\times10^{5}$~cm$^{-3}$, a temperature of 7.5$_{-0.5}^{+0.7}$~K, and a N$_2$H$^+$ deuteration of 0.27$_{-0.15}^{+0.12}$ in the center. We derive a lower limit of the core age for L 1498 of 0.16~Ma which is compatible with the typical free-fall time, indicating that L 1498 likely formed rapidly., Comment: 21 pages, 12 figures, accepted for publication in A&A

Nanowire-based plasmonic lasers are now established as nano-sources of coherent radiation, appearing as suitable candidates for integration into next-generation nanophotonic circuitry. However, compared to their photonic counterparts, their relatively high losses and large lasing thresholds still pose a burdening constraint on their scalability. In this study, the lasing characteristics of ZnO nanowires on Ag and Al substrates, operating as optically-pumped short-wavelength plasmonic nanolasers, are systematically investigated in combination with the size-dependent performance of the hybrid cavity. A hybrid nanomanipulation-assisted single nanowire optical characterization combined with high-throughput PL spectroscopy enables the correlation of the lasing characteristics to the metal substrate and the nanowire diameter. The results evidence that the coupling between excitons and surface plasmons is closely tied to the relationship between substrate dispersive behavior and nanowire diameter. Such coupling dictates the degree to which the lasing character, be it more plasmonic- or photonic-like, can define the stimulated emission features and, as a result, the device performance., Comment: 21 pages, 12 figures, journal paper draft

Alignment is a standard procedure to fine-tune pre-trained large language models (LLMs) to follow natural language instructions and serve as helpful AI assistants. We have observed, however, that the conventional alignment process fails to enhance the factual accuracy of LLMs, and often leads to the generation of more false facts (i.e. hallucination). In this paper, we study how to make the LLM alignment process more factual, by first identifying factors that lead to hallucination in both alignment steps:\ supervised fine-tuning (SFT) and reinforcement learning (RL). In particular, we find that training the LLM on new knowledge or unfamiliar texts can encourage hallucination. This makes SFT less factual as it trains on human labeled data that may be novel to the LLM. Furthermore, reward functions used in standard RL can also encourage hallucination, because it guides the LLM to provide more helpful responses on a diverse set of instructions, often preferring longer and more detailed responses. Based on these observations, we propose factuality-aware alignment, comprised of factuality-aware SFT and factuality-aware RL through direct preference optimization. Experiments show that our proposed factuality-aware alignment guides LLMs to output more factual responses while maintaining instruction-following capability.

Protostars are born in magnetized environments. As a consequence, the formation of protostellar disks can be suppressed by the magnetic field efficiently removing angular momentum of the infalling material. Non-ideal MHD effects are proposed to as one way to allow protostellar disks to form. Thus, it is important to understand their contributions in observations of protostellar systems. We derive an analytical equation to estimate the ambipolar diffusivity coefficient at the edge of the protostellar disk in the Class 0/I protostar, HOPS-370, for the first time, under the assumption that the disk radius is set by ambipolar diffusion. Using previous results of the protostellar mass, disk mass, disk radius, density and temperature profiles and magnetic field strength, we estimate the ambipolar diffusivity coefficient to be $1.7^{+1.5}_{-1.4}\times10^{19}\,\mathrm{cm^{2}\,s^{-1}}$. We quantify the contribution of ambipolar diffusion by estimating its dimensionless Els\"{a}sser number to be $\sim1.7^{+1.0}_{-1.0}$, indicating its dynamical importance in this region. We compare to chemical calculations of the ambipolar diffusivity coefficient using the Non-Ideal magnetohydrodynamics Coefficients and Ionisation Library (NICIL), which is consistent with our results. In addition, we compare our derived ambipolar diffusivity coefficient to the diffusivity coefficients for Ohmic dissipation and the Hall effect, and find ambipolar diffusion is dominant in our density regime. These results demonstrate a new methodology to understand non-ideal MHD effects in observations of protostellar disks. More detailed modeling of the magnetic field, envelope and microphysics, along with a larger sample of protostellar systems is needed to further understand the contributions of non-ideal MHD., Comment: 20 pages, 5 figures. Accepted for publication in ApJ

We report 850 $\mu$m continuum polarization observations toward the filamentary high-mass star-forming region NGC 2264, taken as part of the B-fields In STar forming Regions Observations (BISTRO) large program on the James Clerk Maxwell Telescope (JCMT). These data reveal a well-structured non-uniform magnetic field in the NGC 2264C and 2264D regions with a prevailing orientation around 30 deg from north to east. Field strengths estimates and a virial analysis for the major clumps indicate that NGC 2264C is globally dominated by gravity while in 2264D magnetic, gravitational, and kinetic energies are roughly balanced. We present an analysis scheme that utilizes the locally resolved magnetic field structures, together with the locally measured gravitational vector field and the extracted filamentary network. From this, we infer statistical trends showing that this network consists of two main groups of filaments oriented approximately perpendicular to one another. Additionally, gravity shows one dominating converging direction that is roughly perpendicular to one of the filament orientations, which is suggestive of mass accretion along this direction. Beyond these statistical trends, we identify two types of filaments. The type-I filament is perpendicular to the magnetic field with local gravity transitioning from parallel to perpendicular to the magnetic field from the outside to the filament ridge. The type-II filament is parallel to the magnetic field and local gravity. We interpret these two types of filaments as originating from the competition between radial collapsing, driven by filament self-gravity, and the longitudinal collapsing, driven by the region's global gravity., Comment: Accepted for publication in the Astrophysical Journal. 43 pages, 32 figures, and 4 tables (including Appendix)

The dielectric loss of silicon nitride (Si$_3$N$_4$) limits the performance of microwave-frequency devices that rely on this material for sensing, signal processing, and quantum communication. Using superconducting resonant circuits, we measure the cryogenic loss tangent of either as-deposited or high-temperature annealed stoichiometric Si$_3$N$_4$ as a function of drive strength and temperature. The internal loss behavior of the electrical resonators is largely consistent with the standard tunneling model of two-level systems (TLS), including damping caused by resonant energy exchange with TLS and by the relaxation of non-resonant TLS. We further supplement the TLS model with a self-heating effect to explain an increase in the loss observed in as-deposited films at large drive powers. Critically, we demonstrate that annealing remedies this anomalous power-induced loss, reduces the relaxation-type damping by more than two orders of magnitude, and reduces the resonant-type damping by a factor of three. Employing infrared absorption spectroscopy, we find that annealing reduces the concentration of hydrogen in the Si$_3$N$_4$, suggesting that hydrogen impurities cause substantial dissipation., Comment: 11 pages, 7 figures

We present JCMT POL-2 850 um dust polarization observations and Mimir H band stellar polarization observations toward the starless core L1512. We detect the highly-ordered core-scale magnetic field traced by the POL-2 data, of which the field orientation is consistent with the parsec-scale magnetic fields traced by Planck data, suggesting the large-scale fields thread from the low-density region to the dense core region in this cloud. The surrounding magnetic field traced by the Mimir data shows a wider variation in the field orientation, suggesting there could be a transition of magnetic field morphology at the envelope scale. L1512 was suggested to be presumably older than 1.4 Myr in a previous study via time-dependent chemical analysis, hinting that the magnetic field could be strong enough to slow the collapse of L1512. In this study, we use the Davis-Chandrasekhar-Fermi method to derive a plane-of-sky magnetic field strength ($B_{pos}$) of 18$\pm$7 uG and an observed mass-to-flux ratio ($\lambda_{obs}$) of 3.5$\pm$2.4, suggesting that L1512 is magnetically supercritical. However, the absence of significant infall motion and the presence of an oscillating envelope are inconsistent with the magnetically supercritical condition. Using a Virial analysis, we suggest the presence of a hitherto hidden line-of-sight magnetic field strength of ~27 uG with a mass-to-flux ratio ($\lambda_{tot}$) of ~1.6, in which case both magnetic and kinetic pressures are important in supporting the L1512 core. On the other hand, L1512 may have just reached supercriticality and will collapse at any time., Comment: 25 pages, 10 figures, accepted for publication in ApJ

The internal structure of the prestellar core G208.68-19.02-N2 (G208-N2) in the Orion Molecular Cloud 3 (OMC-3) region has been studied with the Atacama Large Millimeter/submillimeter Array (ALMA). The dust continuum emission revealed a filamentary structure with a length of $\sim$5000 au and an average H$_2$ volume density of $\sim$6 $\times$ 10$^7$ cm$^{-3}$. At the tip of this filamentary structure, there is a compact object, which we call a ``nucleus", with a radius of $\sim$150--200 au and a mass of $\sim$0.1 M$_{\odot}$. The nucleus has a central density of $\sim$2 $\times$ 10$^9$ cm$^{-3}$ with a radial density profile of $r^{-1.87{\pm}0.11}$. The density scaling of the nucleus is $\sim$3.7 times higher than that of the singular isothermal sphere. This as well as the very low virial parameter of 0.39 suggest that the gravity is dominant over the pressure everywhere in the nucleus. However, there is no sign of CO outflow localized to this nucleus. The filamentary structure is traced by the N$_2$D$^+$ 3--2 emission, but not by the C$^{18}$O 2--1 emission, implying the significant CO depletion due to high density and cold temperature. Toward the nucleus, the N$_2$D$^+$ also shows the signature of depletion. This could imply either the depletion of the parent molecule, N$_2$, or the presence of the embedded very-low luminosity central source that could sublimate the CO in the very small area. The nucleus in G208-N2 is considered to be a prestellar core on the verge of first hydrostatic core (FHSC) formation or a candidate for the FHSC., Comment: 27 pages, 16 figures

Synthesized in the cores of stars and supernovae, most metals disperse over cosmic scales and are ultimately deposited well outside the gravitational potential of their host galaxies. Since their presence is well visible through their X-ray emission lines in the hot gas pervading galaxy clusters, measuring metal abundances in the intracluster medium (ICM) offers us a unique view of chemical enrichment of the Universe as a whole. Despite extraordinary progress in the field thanks to four decades of X-ray spectroscopy using CCD (and gratings) instruments, understanding the precise stellar origins of the bulk of metals, and when the latter were mixed on Mpc scales, requires an X-ray mission capable of spatial, non-dispersive high resolution spectroscopy covering at least the soft X-ray band over a large field of view. In this White Paper, we demonstrate how the Line Emission Mapper (LEM) probe mission concept will revolutionize our current picture of the ICM enrichment. Specifically, we show that LEM will be able to (i) spatially map the distribution of ten key chemical elements out to the virial radius of a nearby relaxed cluster and (ii) measure metal abundances in serendipitously discovered high-redshift protoclusters. Altogether, these key observables will allow us to constrain the chemical history of the largest gravitationally bound structures of the Universe. They will also solve key questions such as the universality of the initial mass function (IMF) and the initial metallicity of the stellar populations producing these metals, as well as the relative contribution of asymptotic giant branch (AGB) stars, core-collapse, and Type Ia supernovae to enrich the cosmic web over Mpc scales. Concrete observing strategies are also briefly discussed., Comment: 19 pages. White paper for a mission concept to be submitted for the 2023 NASA Astrophysics Probes opportunity

High-resolution spectroscopy in soft X-rays will open a new window to map multiphase gas in galaxy clusters and probe physics of the intracluster medium (ICM), including chemical enrichment histories, circulation of matter and energy during large-scale structure evolution, stellar and black hole feedback, halo virialization, and gas mixing processes. An eV-level spectral resolution, large field-of-view, and effective area are essential to separate cluster emissions from the Galactic foreground and efficiently map the cluster outskirts. Several mission concepts that meet these criteria have been proposed recently, e.g., LEM, HUBS, and SuperDIOS. This theoretical study explores what information on ICM physics could be recovered with such missions and the associated challenges. We emphasize the need for a comprehensive comparison between simulations and observations to interpret the high-resolution spectroscopic observations correctly. Using Line Emission Mapper (LEM) characteristics as an example, we demonstrate that it enables the use of soft X-ray emission lines (e.g., O VII/VIII and Fe-L complex) from the cluster outskirts to measure the thermodynamic, chemical, and kinematic properties of the gas up to $r_{200}$ and beyond. By generating mock observations with full backgrounds, analysing their images/spectra with observational approaches, and comparing the recovered characteristics with true ones from simulations, we develop six key science drivers for future missions, including the exploration of multiphase gas in galaxy clusters (e.g., temperature fluctuations, phase-space distributions), metallicity, ICM gas bulk motions and turbulence power spectra, ICM-cosmic filament interactions, and advances for cluster cosmology., Comment: 24 pages, 26 figures, submitted to MNRAS. Comments are welcome

Biologically-inspired Spiking Neural Networks (SNNs), processing information using discrete-time events known as spikes rather than continuous values, have garnered significant attention due to their hardware-friendly and energy-efficient characteristics. However, the training of SNNs necessitates a considerably large memory footprint, given the additional storage requirements for spikes or events, leading to a complex structure and dynamic setup. In this paper, to address memory constraint in SNN training, we introduce an innovative framework, characterized by a remarkably low memory footprint. We \textbf{(i)} design a reversible SNN node that retains a high level of accuracy. Our design is able to achieve a $\mathbf{58.65\times}$ reduction in memory usage compared to the current SNN node. We \textbf{(ii)} propose a unique algorithm to streamline the backpropagation process of our reversible SNN node. This significantly trims the backward Floating Point Operations Per Second (FLOPs), thereby accelerating the training process in comparison to current reversible layer backpropagation method. By using our algorithm, the training time is able to be curtailed by $\mathbf{23.8\%}$ relative to existing reversible layer architectures.

Tensor networks have recently found applications in machine learning for both supervised learning and unsupervised learning. The most common approaches for training these models are gradient descent methods. In this work, we consider an alternative training scheme utilizing basic tensor network operations, e.g., summation and compression. The training algorithm is based on compressing the superposition state constructed from all the training data in product state representation. The algorithm could be parallelized easily and only iterates through the dataset once. Hence, it serves as a pre-training algorithm. We benchmark the algorithm on the MNIST dataset and show reasonable results for generating new images and classification tasks. Furthermore, we provide an interpretation of the algorithm as a compressed quantum kernel density estimation for the probability amplitude of input data., Comment: 7+2 pages, 1+2 figures; Position paper in QTNML Workshop, NeurIPS 2021; See https://tensorworkshop.github.io/NeurIPS2021/accepted_papers/MPS_MNIST.pdf