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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Learning a general model of single phase flow in complex 3D porous media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Learning a General Model of Single Phase Flow in Complex 3D Porous Media

Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

Feasibility of Micro-Multilayer Multifunctional Electrical Insulation (MMEI) System for High Voltage Applications

The newly patented micro-multilayer multifunctional electrical insulation (MMEI) system was developed for future electric aircraft applications which critically require lightweight but high voltage (HV), high temperature, and corona or partial discharge (PD) resistant insulation. During the initial development stages, the concept and practicability of the MMEI system were successfully validated with its exceptionally high dielectric breakdown voltages. The multilayer structures were optimized in terms of material type, individual layer thickness, and overall layer configuration along with potential mechanisms identified for its superior performance. Subsequently, scalability, manufacturability, and commercial applicability of the MMEI system were demonstrated with the 1 meter long, 3-phase HV, high power (HP) bus bar prototypes. Two prototypes, one with the conventional SOA insulation system including Mica sheet and the other with an optimized MMEI, were designed, fabricated, and tested successfully. Both prototypes passed both HiPot and PD tests up to the highest test voltage available, 15 kVAC, although the latter showed a slight increase in PD activities at 12.5 kV. However, the prototype with MMEI was 15% lighter or 12% thinner than the other one. Current efforts to significantly enhance the PD resistance of the MMEI system by employing semiconductive shielding layers, which can be also multifunctional, e.g., electromagnetic interference shielding, moisture blocking, heat dissipation, for various HV applications are also discussed in this paper.

Novel Electrical Insulation↗
NASA NTRSDate not supplied

Feasibility of Micro-Multilayer Multifunctional Electrical Insulation (MMEI) System for High Voltage Applications

The newly patented micro-multilayer multifunctional electrical insulation (MMEI) system was developed for future electric aircraft applications which critically require lightweight but high voltage (HV), high temperature, and corona or partial discharge (PD) resistant insulation. During the initial development stages, the concept and practicability of the MMEI system were successfully validated with its exceptionally high dielectric breakdown voltages. The multilayer structures were optimized in terms of material type, individual layer thickness, and overall layer configuration along with potential mechanisms identified for its superior performance. Subsequently, scalability, manufacturability, and commercial applicability of the MMEI system were demonstrated with the 1 meter long, 3-phase HV, high power (HP) bus bar prototypes. Two prototypes, one with the conventional SOA insulation system including Mica sheet and the other with an optimized MMEI, were designed, fabricated, and tested successfully. Both prototypes passed both HiPot and PD tests up to the highest test voltage available, 15 kVAC, although the latter showed a slight increase in PD activities at 12.5 kV. However, the prototype with MMEI was 15% lighter or 12% thinner than the other one. Current efforts to significantly enhance the PD resistance of the MMEI system by employing semiconductive shielding layers, which can be also multifunctional, e.g., electromagnetic interference shielding, moisture blocking, heat dissipation, for various HV applications are also discussed in this paper.

Novel Electrical Insulation↗
NASA NTRS2023

Space Networking Implementation for Lunar Operations

The High-Rate Delay Tolerant Networking (HDTN) project at NASA has developed a performance optimized and open-source Delay Tolerant Networking (DTN) implementation. The primary goal is to create a scalable networking solution to increase the scientific data return rate of space missions. To reach this goal, HDTN must span multiple edge cases in space networking by including tools and configurations to accommodate a wide range of space systems. Typically, HDTN evaluations are conducted on a laboratory emulation test bed, made up of hardware accelerated x86 based systems capable of data rates over 10 Gbps. HDTN must have an effective implementation process on a wide range of systems to increase the sustainability of the design. One important implementation option is with low-level embedded systems which could be used on small robotic missions. This paper details the implementation process, benchmark testing, and performance results of HDTN in multiple configurations on Raspberry Pi 4 devices. By implementing HDTN on a Raspberry Pi 4, a process for building HDTN onto ARM processors was developed and utilized to conduct benchmark tests in multiple network configurations, achieving a data rate performance exceeding 600 Mbps. Based on these results, HDTN proved to run on small ARM based systems with slight modifications to the build procedure. These results were then extended to evaluating an implementation of the HDTN software parsed across several Raspberry Pi 4 nodes. To test this capability, HDTN was configured in a simplified cut-through setup and distributed among multiple Raspberry Pi 4 processors. This distributed architecture was benchmark tested in a similar fashion to the testing of a singular HDTN implementation. The results from the benchmark testing are used to examine how these implementation options and capabilities can expand the use cases for DTN, and particularly with small robotic missions.

Space Networking↗
DOE OSTI2021

Atomic Layer Deposition with TiO2 for Enhanced Reactivity and Stability of Aromatic Hydrogenation Catalysts

Hydrogenation of aromatic molecules in fossil- and bio-derived fuels is essential for decreasing emissions of harmful combustion products and addressing growing concerns around urban air pollution. In this work, we used atomic layer deposition to significantly enhance the hydrogenation performance of a conventional supported Pd catalyst by applying an ultrathin coating of TiO2 in a scalable powder coating process. The TiO2-coated catalyst showed substantial gains in the conversion of multiple aromatic molecules, including a 5-fold improvement in turnover frequency versus the uncoated catalyst in the hydrogenation of naphthalene. This activity enhancement was maintained upon scaling the coating synthesis process from 3 to 100 g. Based on the results from Xray photoelectron spectroscopy, X-ray absorption spectroscopy, and computational modeling, the activity enhancement was attributed to ensemble effects resulting from partial TiO2 coverage of the Pd surface rather than fundamental changes to the Pd electronic structure. Additional durability testing confirmed that the TiO2 coating improved the thermal and hydrothermal stability of the catalyst as well as tolerance toward sulfur impurities in the reactant stream. Using an economic model of an industrial deep hydrogenation process, we found that an increase in catalyst activity or lifetime of 2× would justify even a relatively high estimate for the cost of TiO2 atomic layer deposition coatings at scale

atomic layer↗
DOE OSTI2021

Thermodynamically Stable, Plasmonic Transition Metal Oxide Nanoparticle Solar Selective Absorbers towards 95% Optical-to-Thermal Conversion Efficiency at 750 °C

Generation 3 (Gen) concentrating solar power (CSP) systems requires a high operation temperature ≥750°C to increase the power-cycle efficiency towards ≥50%. However, such a high operation temperature poses a notable challenge to high temperature materials. On the receiver side, a critical challenge is the lack of solar selective absorbers that demonstrate both high optical-to-thermal energy conversion efficiency η therm approaching 95% and long-term thermal stability at >750°C in air. Existing solar absorbers either have limited η therm ≤89% or deteriorate significantly within 500 h at 750°C; some of these also require costly vacuum deposition for stringent thickness control. Therefore, it is highly desirable to simultaneously achieve η therm ~95% AND high thermal stability at 750°C for future generations of CSP receivers. This project has investigated and developed low-cost, highly scalable spray-coated transition metal oxide nanoparticle (NP) pigmented solar selective coatings on various types of Inconel alloy tube sections that are thermodynamically stable at 750-800°C in air, maintaining η therm >94.3%(93.2%) under a solar concentration ratio of C=1000 after 60 simulated day-night cycles at 750ºC (800ºC) (1 cycle=12 h at 750 or 800°C and 12h ramping down to 25°C). We have also coated up to 48 inches long receiver tubes for preliminary solar testing under Norwich Technology’s parabolic trough systems, demonstrating notably improved performance in solar heating compared to benchmark Pyromark 2500 coatings. Two key innovations have been developed in this project: (1) We achieved an unprecedented high thermal efficiency >94% by optimizing the d-band optical absorption spectra of transition metal ions, engineering their valences and stoichiometry; (2) We were able to maintain or even slightly increase the efficiency when operating at 750°C in air by engineering the interdiffusion of transition metal ions between the coating and the Inconel substrate to our advantage. Featuring high-temperature solar spectral selectivity and thermodynamic stability in air, as well as low-cost, highly scalable solution-chemical synthesis and spray coating techniques, this innovation in solar selective absorber technology alone accounts for nearly 40% of the targeted reduction in the levelized cost of electricity (LCOE) from the receiver, thermal energy storage, and operation & maintenance combined by 2030, as proposed by the U.S. Department of Energy. For a 110 MWe CSP power plant, this LCOE reduction from the solar selective coating alone transfers to ~$\$ $1.9 M increase in annual profit based on a sales price of 10¢/kWh (or an annual sale of $50 M/year). The high solar absorptance (~98%) NP pigment materials developed in this project are equally applicable to volumetric receivers either as a coating or as bigger ceramic microspheres after sintering, potentially benefiting Gen3 falling particle CSP technologies. All these contributions facilitate the larger scale deployment of CSP systems with energy storage capability to address the intermittency issue of solar energy towards dispatchable solar electricity, bridging the temporal gap between peak solar electricity production and peak electricity consumption. The collaboration with Norwich Technologies and Brayton Energy in this project will also facilitate the future commercialization of this new solar selective coating technology developed in this SIPS project.

14 SOLAR ENERGY↗
DOE OSTI2022

A Fiber-Based 3D Lithium Host for Lean Electrolyte Lithium Metal Batteries

3D hosts are promising to extend the cycle life of lithium metal anodes but have rarely been implemented with lean electrolytes thus impacting the practical cell energy density. To overcome this challenge, a 3D host that is lightweight and easy to fabricate with optimum pore size that enables full utilization of its pore volume, essential for lean electrolyte operations, is reported. The host is fabricated by casting a VGCF (vapor-grown carbon fiber)-based slurry loaded with a sparingly soluble rubidium nitrate salt as an additive. The network of fibers generates uniform pores of ≈3 µm in diameter with a porosity of 80%, while the nitrate additive enhances lithiophilicity. This 3D host delivers an average coulombic efficiency of 99.36% at 1 mA cm -2 and 1 mAh cm -2 for over 860 cycles in half-cell tests. Full cells containing an anode with 1.35-fold excess lithium paired with LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathodes exhibit capacity retention of 80% over 176 cycles at C/2 under a lean electrolyte condition of 3 g Ah -1 . This work provides a facile and scalable method to advance 3D lithium hosts closer to practical lithium-metal batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A scalable digital platform for the use of digital twins in additive manufacturing

We report that while Additive Manufacturing promises to reshape the manufacturing landscape, challenges related to part, and process qualification hinder its widespread adoption. The Instance-Qualified approach seeks to qualify individual parts, even for processes with high variability, by leveraging the concept of a digital twin. This work proposes a scalable cyberphysical infrastructure to enable the construction and use of such digital twins. This work also introduces the concept of an Augmented Intelligence Relay, which allows Artificial Intelligence algorithms to predict component performance for a given application even when it is impractical to perform a large number of physical tests.

36 MATERIALS SCIENCE↗
DOE OSTI2024

PICOSEC-Micromegas Detector, an innovative solution for Lepton Time Tagging

The PICOSEC-Micromegas (PICOSEC-MM) detector is a novel gaseous detector designed for precise timing resolution in experimental measurements. It eliminates time jitter from charged particles in ionization gaps by using extreme UV Cherenkov light emitted in a crystal, detected by a Micromegas photodetector with an appropriate photocathode. The first single-channel prototype tested in 150 GeV/c muon beams achieved a timing resolution below 25 ps, a significant improvement compared to standard Micropattern Gaseous Detectors (MPGDs). This work explores the specifications for applying these detectors in monitored neutrino beams for the ENUBET Project. Key aspects include exploring resistive technologies, resilient photocathodes, and scalable electronics. New 7-pad resistive detectors are designed to handle the particle flux. In this paper, two potential scenarios are briefly considered: tagging electromagnetic showers with a timing resolution below 30ps in an electromagnetic calorimeter as well as individual particles (mainly muons) with about 20ps respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Design considerations of series type hybrid circuit breaker (S‐HCB)

Abstract The series‐type direct current (DC) hybrid circuit breaker (S‐HCB) concept was previously reported to offer better performance than solid‐state circuit breakers (SSCB) and hybrid circuit breakers (HCB). S‐HCB offers low conduction power loss like an HCB and ‐scale interruption time, which is even faster than an SSCB. It uses a pulse transformer to isolate the lower‐voltage high‐inductance power electronic circuit from the high‐voltage, low‐inductance main power loop. This paper provides analysis of the impact of the S‐HCB circuit components on the overall system performance and a scalable S‐HCB design guide for different DC system voltage and current ratings. In addition, system energy flow analysis is performed in the time domain to provide an understanding of how energy is delivered, dissipated, and released throughout the entire fault interruption process. The S‐HCB prototype was experimentally tested at 3 kV/30 A and 6 kV/150A with the results showing the interruption of the low fault current of 30 A and the high fault current of 150 A within 8 and maintaining the fault current at a near zero value for 300 to enable an arcless opening of a series mechanical switch. The key design challenges of S‐HCB at high voltage and high current ratings were discussed and possible solutions to mitigate those challenges were introduced.

Alashi, Mahmoud↗
DOE OSTI2022

Fully self-consistent finite-temperature $GW$ in Gaussian Bloch orbitals for solids

In this work, we present algorithmic and implementation details for the fully self-consistent finite-temperature $GW$ method in Gaussian Bloch orbitals for solids. Our implementation is based on the finite-temperature Green's function formalism in which all equations are solved on the imaginary axis, without resorting to analytical continuation during the self-consistency. No quasiparticle approximation is employed and all matrix elements of the self-energy are explicitly evaluated. The method is tested by evaluating the band gaps of selected semiconductors and insulators. We show agreement with other, differently formulated, finite-temperature sc ⁢$GW$ implementations when finite-size corrections and basis-set errors are taken into account. By migrating computationally intensive calculations to graphics processing units, we obtain scalable results on large supercomputers with nearly optimal performance. Our work demonstrates the applicability of Gaussian orbital based sc⁢ $GW$ for ab initio correlated material simulations and provides a sound starting point for embedding methods built on top of $GW$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Biomass Gasification for Chemicals Production Using Chemical Looping Techniques (Final Report)

The Ohio State University (OSU) is investigating the Biomass to Syngas (BTS) chemical looping technology to produce syngas for chemical production applications from biomass under US Department of Energy (DOE) Award #DE-EE0007530. The BTS process aligns with the programmatic area of interest of “Conversion, via biological, thermal, catalytic or chemical means, of acceptable feedstocks into advanced biofuels and/or biobased products including intermediate and end-use products”. Compared to conventional biomass gasification processes, the BTS process eliminates the need for air separation units and tar reforming reactors, which leads to energy efficiency improvement and capital cost reduction. The overall objective is to ascertain the potential of biomass gasification based on the chemical looping technique through mitigation of the possible techno-economic challenges in the steps of scale up for commercialization. The scope of work consists of 1) designing, constructing and operating a 10 kWth commercially scalable sub-pilot BTS system and; 2) completing a comprehensive techno-economic analysis (TEA) of the BTS process using methanol production as an example. Over the course of the project, the project team completed the design, fabrication, and operation of a 10 kWth sub-pilot scale test unit for the BTS process. Corn cob and wood pellets were successfully tested in the unit for high purity syngas generation in extended test campaigns that totals over 200 hours. Syngas purity (H2 and CO) of >70% was achieved with a CH 4 concentration of <6%. The H 2 /CO ratio was greater than 1.8. A comprehensive techno-economic analysis was performed to compare the BTS process and a reference indirectly heated gasification process for methanol synthesis. The result, updated with experimental results for BTS process performance, shows a methanol required selling price (MSP) of $\$ $1.15/gal, compared to $1.28/gal for the reference case.

08 HYDROGEN↗
DOE OSTI2024

Machine Learning Accelerates Innovation in Perovskite Manufacturing Scale-up (Final Technical Report (FTR))

We propose to address the challenge of the vast parameter space associated with perovskite manufacturing optimization, by developing a machine learning (ML)-assisted optimization framework for a scalable perovskite PV manufacturing tool. This framework will be interpretable, sequential, and rapidly adaptable to upgraded systems (e.g., via transfer learning). The tool is an open-air rapid spray plasma process (RSPP) of perovskite films, which has already been established at Stanford and is a unique platform to test and deploy the proposed ML-guided framework because the RSPP technique is able to conduct optimization experiments with a high throughput, and easily adjust a wide range of process variables.

14 SOLAR ENERGY↗
DOE OSTI2020

Distributed Ledger Technology Preliminary Performance Assessment for Applications in Utilities Operational Technology

This paper provides descriptions of the key components of different distributed ledger technology platforms. Distributed ledger technology (DLT) allows for distribution of databases among different organizations and devices. The platforms use cryptographically linked “blocks” to store and verify transactional information between these organizations. DLT increases data security, data integrity, trust among its participants. Different organizations are looking to deploy this distributed and decentralized approach to avoid the single-point-of-failure vulnerabilities associated with centralized data repositories. In this study we examine twelve different DLT platforms. There is agreement within the community that of all the platforms considered here, Hyperledger and Ethereum are the most mature when it comes to privacy and permissions. These DLT platforms are being used for applications such as transactive energy, health care, and the food and goods supply chain. However, further development is required to realize the full promise of DLT. Our assessment includes a general description of each DLT and its key characteristics. Such characteristics include consensus protocol and cryptography used, public vs. private, and permissioned or permissionless. The selection and implementation of a DLT architecture depends heavily on the use case and performance requirements. During this research we found key parameters to measure performance and existing tools for assessment. Four different parameters were identified 1) consensus, 2) throughput, 3) latency, and 4) scalability. The architectures of Hyperledger Caliper and Blockbench are described as different performance assessment frameworks. From this preliminary study it is evident that there are dissimilarities on the performance assessments methods developers and users are characterizing DLT architectures. The purpose of this paper is to identify key parameters to test performance, tools that are being used and provide information on results from previous studies.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Microwave Structure Construction Capability Year One Accomplishments

The Microwave Structure Construction Capability (MSCC) element, part of the Moon to Mars Planetary Autonomous Construction Project (MMPACT) was initiated in 2020. MSCC is responsible for creating horizontal and vertical infrastructure on the moon using microwave energy. Microwave energy was selected since it is the only method to volumetrically heat the regolith. All other sintering/melting methods rely on thermal conduction through the very low conductivity surface, resulting in an inefficient process. Advances were achieved in materials characterization and understanding, microwave sintering in vacuum, and microwave design and analyses. Two dielectric property testing systems have been developed at Radiance Technologies and JPL. These will examine dielectric properties at cryogenic temperatures and over a broad frequency range. Permittivity and permeability testing at -60 ̊C in vacuum from 0.05 to 3 GHz has been generated at JPL. Additional modifications will be made to go to -190 ̊C (LN2). Radiance Technologies created a test system to measure dielectric properties at greater than 10 GHz and initiated work on developing a vacuum capable, portable test system to measure dielectric properties of Apollo regolith and simulants from 100 MHz to 18 GHz. These tests are to identify optimal heating frequencies and protocols. During microwave sintering at about 1100 ̊C, volatiles were creating difficulty in achieving a reasonably dense specimen. Due to processing in vacuum and the nature of the lunar regolith, some volatiles and porosity are expected. However, the Earth produced simulants have non-lunar materials in them that create volatiles that aren’t representative of lunar regolith. Therefore, a five month effort was conducted to establish a heat treat method to remove these non-lunar materials. Tests were conducted using TGA mass spectrometry, heating in vacuum and conducting mass spectrometry, dielectric and DTA, Raman, BET, particle size analysis, morphological analysis, carbon and sulfur chemical content determination and microscopy. The process has been scaled-up to 6 kg batch size and undergoing evaluation. A 36 kg batch size is the target for JSC-1A and other limited availability simulants. These calcining protocols will be standard for NASA and beyond. MSCC has also created scalable processes for fabricating synthetic lunar materials. Processes to fabricate Anorthite (plagioclase CaAl2Si2O8), Diopside (pyroxene CaMgSi₂O₆), and Enstatite (pyroxene Mg2Si2O6) have been generated. These materials will enable generation of microwave sintering models to bound various composition ranges anticipated on the Moon, therefore mitigating the need for a precise simulant with respect to location on the Moon. Successful microwave sintering in air using a horn applicator was demonstrated. All previous microwave vacuum sintering in the literature was at small scale and in a contained enclosure thus taking advantage of reflections. This is the first to use a lunar like microwave applicator to sinter ceramic in a bed as it would be done on the Moon. Small scale and inert sintering were conducted to assist in developing protocols with quicker turnaround times than larger scale testing. Testing has anchored thermal analysis predicting heat flow in vacuum during microwave sintering. Thermal conductivity testing was also initiated. Microwave coupling to the regolith has been modeled by multiple organizations and with different software packages. At least six horn designs and applicator configurations for both magnetron and solid state sources are being examined. Optimal simulant container designs for microwaves have also been generated. The power and electronics design for the solid state microwave system has been initiated. Concept designs for a lander based microwave sintering have been evaluated.

microwave↗
DOE OSTI2023

Solutionization via Severe Plastic Deformation: Effect on Natural Aging in an Al-Mg-Si-(Mn) Alloy

Shear Assisted Processing and Extrusion (ShAPE), a severe plastic deformation technique that is fast and scalable, was used to produce thin-wall tubing from alloy 6082 (Al-0.8Mg-0.9Si-0.7Mn) with in-situ solutionization during processing quickly followed by quenching. Quench medium and input material heat treatment were varied and natural aging behavior (T1 heat treatment) was evaluated using tensile testing. As-cast input material was found to have higher strength early in post-ShAPE natural aging and homogenized input material was found to have strengthening during natural aging. Compared to air quenching, water quenching gave greater strength both early in and after natural aging. These observations along with microscopy suggest that: 1. air quenching was too slow; 2. as-cast material was not fully solutionized but plastic deformation broke up the coarse intermetallic particles, which provided strengthening; and 3. homogenized material was solutionized well, which led to good natural aging behavior.

Milligan, Brian K.↗
DOE OSTI2025

Adiabatic quantum support vector machines

Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine learning models. In this paper, we describe an adiabatic quantum approach for training support vector machines. We show that the time complexity of our quantum approach is an order of magnitude better than the classical approach. Next, we compare the test accuracy of our quantum approach against a classical approach that uses the Scikit-learn library in Python across five benchmark datasets (Iris, Wisconsin Breast Cancer (WBC), Wine, Digits, and Lambeq). We show that our quantum approach obtains accuracies on par with the classical approach. Finally, we perform a scalability study in which we compute the total training times of the quantum approach and the classical approach with an increasing number of features and an increasing number of data points in the training dataset. In conclusion, our scalability results show that the quantum approach obtains a 3.5–4.5x speedup over the classical approach on datasets with many (millions of) features.

Computational Complexity↗