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SNoGloDe: A Structured Nonlinear Global Decomposition Solver

Large-scale optimization problems often require decomposition strategies and customized algorithms to achieve optimal solutions within a reasonable time. Building on the work of Cao and Zavala (2019) for solving nonlinear two-stage stochastic programs to global optimality, we implement and extend their approach. We generalize to optimization problems reformulated with a block-angular constraint structure (e.g., temporal decomposition). Our framework, written in Python using Pyomo, is highly customizable and enables parallel execution of the decomposition. SNoGloDe allows tailored branching strategies, lower bounding problems, and candidate generators to leverage problem-specific knowledge. To demonstrate effectiveness, we compare SNoGloDe’s performance with Gurobi on a temporally decomposed produced water case study.

algorithms

High-voltage and electrode system for a cryogenic experiment to search for the neutron electric dipole moment

The cryogenic approach to the search for the neutron electric dipole moment—performing the experiment in superfluid liquid helium—holds promise for a substantial increase in sensitivity, potentially enabling a sensitivity level of 10 −28 𝑒cm. A crucial component in realizing such an experiment is the high-voltage and electrode system capable of providing an electric field of 75 kV/cm. This, in turn, requires an electric potential of 635 kV to be applied to the high-voltage electrode, while simultaneously satisfying other experimental constraints, such as those on heat load and magnetic noise requirements. In this work, we describe the outcome of a comprehensive development program addressing these challenges. It outlines the system requirements, discusses new insights into relevant physical phenomena, and details selected technical solutions with their corresponding experimental demonstrations and expected performance. The results collectively demonstrate the successful development of the necessary technology for the high-voltage and electrode system for this approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS

Sensitivity to low-mass WIMPs with an improved liquid argon ionization response model within the DarkSide program

Dark matter detection experiments using liquid argon rely on a precise characterization of the ionization response to nuclear recoils, especially in the keV energy range relevant for light dark matter interactions. In this work, we present a comprehensive analysis that combines new measurements from the ReD setup, part of the DarkSide experimental program, with calibration data from DarkSide-50, as well as results from the ARIS and SCENE experiments. These combined datasets enable improved constraints on atomic screening effects in the modeling of the ionization response of liquid argon to nuclear recoils. The analysis is performed within the Thomas-Imel recombination framework adopted in previous DarkSide studies, and is here further constrained by the inclusion of ReD data, which allow the screening function to be determined from calibration measurements. By including the updated ionization model into the DarkSide-50 analysis framework, we obtain stronger exclusion limits on low-mass weakly interacting massive particle (WIMP) interactions, setting new world-leading constraints in the 1 – 3 GeV / c 2 WIMP mass range. Finally, we recast the sensitivity projections for the upcoming DarkSide-20k detector, demonstrating a significantly enhanced discovery potential for low-mass dark matter candidates.

Acerbi, F. [Fond. Bruno Kessler, Trento]

PDPTW-DB: MILP-Based Offline Route Planning for PDPTW with Driver Breaks

The Pickup and Delivery Problem with Time Windows (PDPTW) involves optimizing routes for vehicles to meet pickup and delivery requests within specific time constraints, a challenge commonly faced in logistics and transportation. Microtransit, a flexible and demand-responsive service using smaller vehicles within defined zones, can be effectively modeled as a PDPTW. Yet, the need for driver breaks—a key human constraint—is frequently overlooked in PDPTW solutions, despite being necessary for regulatory compliance. This study presents a novel mixed-integer linear programming formulation for the Pickup and Delivery Problem with Time Windows and Driver Breaks (PDPTW-DB). To the best of our knowledge this formulation is the first to consider mandatory periodic driver breaks within optimized Microtransit routes. The proposed model incorporates regulatory compliant break scheduling directly within the vehicle routing optimization framework. By considering driver break requirements as an integral component of the optimization process, rather than as a post-processing step, the model enables the generation of routes that respect hours of service regulations while minimizing operational costs. This integrated approach facilitates the generation of schedules that are operationally efficient and prioritize driver welfare through driver breaks. We work with a public transit agency from the southern USA, and highlight the specific nuances of driver break optimization, and present a Pickup and Delivery Problem with Time Windows formulation for optimizing Microtransit operations and scheduling driver breaks. We validate our approach using real-world data from the transit agency. Our results validate our formulation in producing cost-effective, and regulation-compliant solutions.

Applied Computing, Transportation

A Linear Programming Approach to Backtracking for Single-Axis Trackers on Rolling Terrain

In this article, we present a computationally efficient method for determining optimal backtracking rotations for single-axis solar trackers on nonuniform terrain. The method allows for ganged tracking, mechanical rotation constraints, uneven row spacing, and arbitrary maximum allowable shaded fractions (to enable “fractional backtracking”). As with previous 2-D approaches, the method is suitable for terrain that varies in the transverse direction with respect to the rotation axis of the trackers. The novelty of the method lies in formulating the problem of shade avoidance as a linear problem, which is achieved by using the row interception width as the optimization variable instead of rotation angles. Formulating backtracking as a linear problem enables the use of extremely efficient linear programming algorithms, making the method highly scalable, requiring less than 1 min to compute optimal rotation schedules for hundreds of trackers. It also produces more effective backtracking rotations, reducing the frequency of shading by 4× and improving system energy output by 1%–2%.

Optimization

Jacobian-based model diagnostics and application to equation oriented modeling of a carbon capture system

It can be difficult to identify the specific variables or equations responsible for convergence issues in large mathematical programming models. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES-IP) contains a tool to identify poorly scaled constraints and variables by searching for rows and columns of the Jacobian matrix with small L2-norms. A singular value decomposition is then performed to identify degenerate sets of equations and remaining scaling issues. Here, this work presents a flowsheet developed for post-combustion carbon capture using a monoethanolamine (MEA) solvent system as a case study. This work takes the reader through the entire process of model diagnostics and reformulation, from a basic introduction to the mathematics behind these model diagnostics to the reformulations necessary to make the model numerically robust, including a significantly modified enhancement factor model.

IDAES

Requirements Description of DASSH-F

This report reviews the modeling and simulation capabilities of Argonne National Laboratory’s DASSH code that is used in present reactor analysis activities. These capabilities will be used to establish the set of verification tasks necessary to verify DASSH for use on commercial projects. A similar approach was taken for the PERSENT, REBUS and DIF3D software packages. The DASSH program is a thermal analysis code designed to rapidly allow a reactor design engineer to obtain flow rates requirements that satisfy peak temperature constraints in the domain. DASSH is a follow-on development to the SE2-ANL software and SUPERENERGY-2 software that it is based upon. DASSH was designed to account for both neutron and gamma heating and is inherently connected to the GAMSOR part of the ARC suite of fast reactor analysis software. SE2-ANL is a developed piece of software from the 1980s while DASSH is a modern implementation with notable improvements in geometry handling. The most important upgrade of DASSH relative to SE2-ANL is that it can analyze multiple time points in a single run where SE2-ANL can only treat a single time point. This allows the user to understand the impact of and search the flow distribution for the entire operational period of a reactor design considering pressure drop, peak coolant and fuel temperatures, and thermal striping. DASSH has three input paths that have to be verified. The first input path builds the geometry and power distribution based upon the DIF3D model but ignores the gamma heating aspects of the problem. The second input path also builds the geometry from the DIF3D model but it takes the neutron and gamma heating distributions from GAMSOR. The third input path is to take the geometry and power distribution directly from user input (i.e. not coupled to DIF3D or GAMSOR). DASSH also has many built in correlations for material properties along with a user defined specification of the fuel, structure, and coolant properties. There are correlations for flow split, mixing, pressure drop, and heat transfer coefficients (subchannel rather than a direct methodology). In total, verification of DASSH will require an extensive testing to cover all possible user features of the software.

22 GENERAL STUDIES OF NUCLEAR REACTORS

Joint scheduling of energy, fast and primary frequency response reserves in integrated transmission–distribution networks

Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.

Noh, Seung-Gil

Zero-Emission Transit Bus Needs Assessment

The transition to zero-emissions vehicles (ZEVs) in public transit has gained traction due to significant federal investments from the Bipartisan Infrastructure Law (BIL) and the Inflation Reduction Act (IRA). This needs assessment, commissioned by the Joint Office of Energy and Transportation and conducted by researchers at the Idaho National Laboratory, explores the current state of electrification in transit agencies, identifying barriers to implementation, potential funding sources, and operational considerations necessary for a successful transition. The assessment involved qualitative interviews with representatives from 19 transit service providers across diverse geographic regions. Key findings highlight the challenges related to bus facilities and operations, which require careful planning for charging infrastructure and maintenance capabilities to accommodate battery electric buses (BEBs) and hydrogen fuel cell buses (HFCBs). Agencies reported operational hurdles due to the shorter range of BEBs compared to diesel buses, necessitating revised scheduling and routing strategies. Despite these challenges, many agencies expressed optimism about their capacity to adapt. Funding availability emerged as a critical factor influencing the transition to ZEVs. While agencies welcomed increased financial support, particularly from the Low or No Emission Grant Program (Lo-No), concerns about the sustainability of this funding and the ongoing operational costs were prevalent. The need for a comprehensive funding inventory was underscored to ensure transit agencies are aware of all available resources. Technological constraints were significant barriers to ZEV adoption. The limited range of BEBs was frequently cited as a concern, leading to operational challenges and reliability issues. Agencies reported difficulties in sourcing replacement parts, which exacerbated downtime and maintenance challenges. Workforce development and training were identified as pivotal for a successful transition. Many agencies rely heavily on manufacturers for technician training, highlighting the need for scalable training programs that equip staff with the necessary skills to maintain electric powertrains effectively. This assessment offers actionable recommendations for the Joint Office, including enhancing outreach to transit agencies, developing resources for effective utility partnerships, and facilitating comprehensive training programs. Establishing a zero-emission bus evaluation program to track performance metrics such as cost, range, and reliability could provide valuable insights for transit agencies. The needs assessment provides a detailed examination of the challenges and opportunities facing transit agencies in their transition to zero-emissions bus fleets. By addressing these issues through targeted support, stakeholders can collaboratively work towards a cleaner, more sustainable public transportation system that benefits all communities.

33 - ADVANCED PROPULSION SYSTEMS

Closeout Report of BNL LDRD 23-050 A Second EIC Detector: Physics Case and Conceptual Design

This document is the closeout report for LDRD 23-050, a type-A LDRD project awarded in FY2022 under the title “ A Second EIC Detector: Physics Case and Conceptual Design ”. The project was motivated by the strong interest within the EIC community in a second general-purpose detector and interaction region, and by the recognition that such a detector is essential to fully exploit the scientific potential of the EIC over its multi-decade lifetime. The key goals of the LDRD were to (i) strengthen the case for a second EIC detector, building on the arguments already articulated in the community Yellow Report, (ii) provide a realistic detector concept that is complementary to the current project detector, ePIC, in terms of physics reach, precision, and control of systematics, and (iii) broaden the overall physics program of the EIC facility. Since a possible second detector is expected to be realized with a delay of several years relative to the first detector, the project explicitly aimed at identifying technologies that are not yet sufficiently mature for ePIC but could be deployed on the later timescale of a second detector, thereby providing genuine complementarity and room for innovation. LDRD 23-050 provided support for two postdoctoral researchers and partial support for several BNL staff members, and the effort ramped up in mid-2023. Work was carried out in close collaboration with colleagues from the EIC project, the Cold QCD and STAR groups at BNL, and with substantial input from members of the EIC User Group and the ePIC Collaboration as reflected in the author list. Over the period from October 2022 through September 2025, this team developed and refined a physics program tailored to a second detector, translated the resulting physics requirements into detector-level performance targets, and explored multiple conceptual detector layouts capable of meeting these targets within the constraints of the second interaction region. The expected outcome, as stated in the original proposal, was a document detailing the physics potential and requirements of a second EIC detector, accompanied by a comprehensive conceptual detector design and an outline of the remaining R&D needed to bring the relevant technologies to maturity. The present report summarizes the progress that has been achieved toward these goals. It consolidates the physics studies, detector concepts, and technology assessments developed under this LDRD and places them in the broader context of worldwide detector R&D. While the evolving priorities of the EIC project and the substantial community effort required to design and construct ePIC naturally limited the scope of EIC-wide activities toward a second detector during this period, the work documented here is intended to provide a foundation and a point of reference for future efforts. Whatever form a second EIC detector may ultimately take, we hope that this report will serve as a useful guide for colleagues who continue to advance this program in the near- and mid-term future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS

The putative center in NGC 1052

Context. Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood.Aims. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052.Methods. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. Our study is focused on the size and continuum spectrum of the innermost region containing the central engine and the footpoints of both jets. We employed a synchrotron-self absorption model to fit the continuum radio spectrum and we combined the size measurements from close to the central engine out to ∼1 pc to study the jet collimation.Results. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 43 μas perpendicular to the jet axes, corresponding to just around 250 RS (Schwarzschild radii). This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 3 × 103 RS and 1 × 104 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 μas from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at ∼130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 2.6 × 104 Gauss, which is consistent with previous measurements.Conclusions. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength.Key words: methods: observational / techniques: high angular resolution / techniques: interferometric / galaxies: active / galaxies: jets / galaxies: Seyfert⋆⋆ NASA Hubble Fellowship Program, Einstein Fellow.

79 ASTRONOMY AND ASTROPHYSICS

The DECADE cosmic shear project III: validation of analysis pipeline using spatially inhomogeneous data

We present the pipeline for the cosmic shear analysis of the Dark Energy Camera All Data Everywhere (DECADE) weak lensing dataset: a catalog consisting of 107 million galaxies observed by the Dark Energy Camera (DECam) in the northern Galactic cap. The catalog derives from a large number of disparate observing programs and is therefore more inhomogeneous across the sky compared to existing lensing surveys. First, we use simulated data-vectors to show the sensitivity of our constraints to different analysis choices in our inference pipeline, including sensitivity to residual systematics. Next we use simulations to validate our covariance modeling for inhomogeneous datasets. Finally, we show that our choices in the end-to-end cosmic shear pipeline are robust against inhomogeneities in the survey, by extracting relative shifts in the cosmology constraints across different subsets of the footprint/catalog and showing they are all consistent within 1σ to 2σ. This is done for forty-six subsets of the data and is carried out in a fully consistent manner: for each subset of the data, we re-derive the photometric redshift estimates, shear calibrations, survey transfer functions, the data vector, measurement covariance, and finally, the cosmological constraints. Our results show that existing analysis methods for weak lensing cosmology can be fairly resilient towards inhomogeneous datasets. This also motivates exploring a wider range of image data for pursuing such cosmological constraints.

79 ASTRONOMY AND ASTROPHYSICS

Energy Technology Proving Ground Program Plan

New methods of energy production and distribution are required to meet clean energy goals and demands across all U.S. energy sectors. Idaho National Laboratory’s (INL) Integrated Energy Systems (IES) initiative is enabling clean energy research, development, and demonstration (RD&D) activities. To date, IES demonstration programs have been limited by distributed infrastructure and a lack of large-scale facilities to accommodate industry-scale research of Technical Readiness Level (TRL) 6-8 technologies. The IES initiative plans to eliminate these constraints by establishing a new research complex at INL known as the Energy Technology Proving Ground (Proving Ground) to be led by the Energy and Environment Science and Technology Directorate. The Energy and Environment Science and Technology (EES&T) directorate, one of five Idaho National Laboratory (INL) RD&D organizations, focuses on clean energy technologies that anchor the industry-enabling research of the Proving Ground. The Proving Ground will combine diverse clean energy systems into lean integrated test bed of independent multiscale capabilities available to the government and commercial industries to perform research; and will enable INL’s goal of becoming a Net-Zero entity by 2031. This program encompasses existing and new research space at both the in-town Research and Education Campus (REC) and the Arco desert site (the Site). The Proving Ground will support the maturation of IES technologies from TRL 1 through 8 by providing the infrastructure and capabilities needed to sustain a continuum of RD&D from basic science to industry-scale. To establish The Proving Ground and meet INL’s net-zero goals by 2031, nine research program areas have been identified within the IES initiative that require expanded and new capital infrastructure. This program plan provides guidance for establishing the Proving Ground at the Site for plug-and-play pilot testing and proofing of integrated energy system functionality including fission and renewable energy sources for industry driven application platforms.

29 ENERGY PLANNING, POLICY, AND ECONOMY

Energy Technology Proving Ground FY-2026 Program Plan (Rev.1)

New methods of energy production and distribution are required to meet clean energy goals and demands across all U.S. energy sectors. Idaho National Laboratory’s (INL) Integrated Energy Systems (IES) initiative is enabling clean energy research, development, and demonstration (RD&D) activities. To date, IES demonstration programs have been limited by distributed infrastructure and a lack of large-scale facilities to accommodate industry-scale research of Technical Readiness Level (TRL) 6-8 technologies. The IES initiative plans to eliminate these constraints by establishing a new research complex at INL known as the Energy Technology Proving Ground (Proving Ground) to be led by the Energy and Environment Science and Technology Directorate. The Energy and Environment Science and Technology (EES&T) directorate, one of five Idaho National Laboratory (INL) RD&D organizations, focuses on clean energy technologies that anchor the industry-enabling research of the Proving Ground. The Proving Ground will combine diverse clean energy systems into lean integrated test bed of independent multiscale capabilities available to the government and commercial industries to perform research; and will enable INL’s goal of becoming a Net-Zero entity by 2031. This program encompasses existing and new research space at both the in-town Research and Education Campus (REC) and the Arco desert site (the Site). The Proving Ground will support the maturation of IES technologies from TRL 1 through 8 by providing the infrastructure and capabilities needed to sustain a continuum of RD&D from basic science to industry-scale. To establish The Proving Ground and meet INL’s net-zero goals by 2031, nine research program areas have been identified within the IES initiative that require expanded and new capital infrastructure. This program plan provides guidance for establishing the Proving Ground at the Site for plug-and-play pilot testing and proofing of integrated energy system functionality including fission and renewable energy sources for industry driven application platforms.

29 ENERGY PLANNING, POLICY, AND ECONOMY

ICARUS at the Short-Baseline Neutrino program: first results

The ICARUS collaboration employed the 760-ton T600 detector in a successful three-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous $\nu_e$ appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 $eV^2$. After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. In 2020 the cryogenic commissioning began with detector cool down, liquid argon filling and recirculation. ICARUS then started its operation collecting the first neutrino events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis, which were used to test the ICARUS event selection, reconstruction and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022, moving then to data taking for neutrino oscillation physics, aiming at first to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment. ICARUS will also perform measurement of neutrino cross sections in LAr with the NuMI beam and several Beyond Standard Model studies. After the first year of operations, ICARUS will search for evidence of sterile neutrinos jointly with the Short-Baseline Near Detector, within the Short-Baseline Neutrino program. In this presentation, preliminary results from the ICARUS data with the BNB and NuMI beams are presented both in terms of performance of all ICARUS subsystems and of capability to select and reconstruct neutrino events.

Artero Pons, Maria

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)

Correlated purification for restoring 𝑁-representability in quantum simulation

Experimentally measured reduced density matrices (RDMs) often violate constraints that ensure they represent N-electron states—known as N-representability conditions—because of statistical and hardware noise. In this work, we present a correlated purification framework based on semidefinite programming to restore the accuracy of a noisy, unphysical two-electron RDM (2-RDM). The method performs a bi-objective optimization that minimizes both the many-electron energy and the nuclear norm of the correction to the measured 2-RDM. The nuclear norm, often employed in matrix completion, promotes low-rank corrections, while the energy term acts as a regularization term that can improve the purity of the ground state. While the method is particularly effective for ground states, it can also be applied to excited and nonstationary states by decreasing the weight of the energy relative to the error norm. In an application to fermionic shadow tomography of large hydrogen chains, correlated purification yields substantial reductions in both energy and 2-RDM error, achieving chemical accuracy across dissociation curves. This framework provides a robust strategy for tomography in many-body quantum simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC