Results for “optimization methods” — Page 67

DOE OSTI2026

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗

DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Monaco treatment planning system tools and optimization processes

The Monaco treatment planning system combines Monte Carlo dose calculation accuracy with robust optimization tools to provide high-quality radiotherapy treatment plans for three-dimensional conformal radiotherapy (3D CRT), intensity modulated radiotherapy (IMRT), volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), and stereotactic body radiotherapy (SBRT). Recent technology advances have allowed for fast calculation speeds, which allow clinicians and patients to benefit from the accuracy of the Monte Carlo algorithm while reducing overall planning time. A collection of biological and physical dose-based planning tools and templates simplify the planning process and allow for consistent results across organizations. At the same time, multicriteria optimization (MCO) ensures critical organs are spared to the greatest possible degree while maintaining target coverage. Monaco encompasses a full suite of treatment modalities, including conventional radiotherapy and particle therapy, and is paving the way for real-time adaptive treatments with developments in magnetic resonance (MR)-guided radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

Algebraic Multigrid with Filtering: An Efficient Preconditioner for Interior Point Methods in Large-Scale Contact Mechanics Optimization

Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by minimizing an energy functional; however, these problems are often nonlinear, nonconvex, and increasingly difficult to solve as mesh resolution increases. In this work, we employ a Newton-based interior-point (IP) filter line-search method, an effective approach for large-scale constrained optimization. While this method converges rapidly, each iteration requires solving a large saddle-point linear system that becomes ill-conditioned as the optimization process converges, largely due to IP treatment of the contact constraints. Such ill-conditioning can hinder solver scalability and increase iteration counts with mesh refinement. Here, to address this, we introduce a novel preconditioner, algebraic multigrid with filtering (AMGF), tailored to the Schur complement of the saddle-point system. Building on the classical AMG solver, commonly used for elasticity, we augment it with a specialized subspace correction that filters near null space components introduced by contact interface constraints. Through theoretical analysis and numerical experiments on a range of linear and nonlinear contact problems, we demonstrate that the proposed solver achieves mesh independent convergence and maintains robustness against the ill-conditioning that notoriously plagues IP methods. These results indicate that AMGF makes contact mechanics simulations more tractable and broadens the applicability of Newton-based IP methods in challenging engineering scenarios. More broadly, AMGF is well suited for problems, optimization or otherwise, where solver performance is limited by a low-dimensional subspace, such as those arising from localized constraints, interface conditions, or model heterogeneities. This makes the method widely applicable beyond contact mechanics and constrained optimization.

Mathematics and Computing↗

DOE OSTI2018

RayStation: External beam treatment planning system

RaySearch Laboratories is a world leader in the field of advanced software and creator of the RayStation treatment planning system for radiation therapy. The aim with RayStation is to deliver an unmatched user experience and leading functionality. Unique features described here include multiatlas based autosegmentation for contouring, deformable registration with 2 different algorithms, multicriteria optimization, Plan Explorer, fallback planning, ultrafast computation speed, and 4-dimensional (4D) adaptive radiation therapy. RayStation can be used to plan for electrons and photons on traditional linacs, for protons on various delivery systems, and for Accuray's helical TomoTherapy system. This paper describes some of these modalities, with reference to clinical cases and including descriptions of the impact on workflow.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

SU-G-TeP3-13: The Role of Nanoscale Energy Deposition in the Development of Gold Nanoparticle-Enhanced Radiotherapy

Purpose: Gold nanoparticles (GNPs) can enhance radiotherapy effects. The high photoelectric cross section of gold relative to tissue, particularly at lower energies, leads to localized dose enhancement. However in a clinical context, photon energies must also be sufficient to reach a target volume at a given depth. These properties must be balanced to optimize such a therapy. Given that nanoscale energy deposition patterns around GNPs play a role in determining biological outcomes, in this work we seek to establish their role in this optimization process. Methods: The PENELOPE Monte Carlo code was used to generate spherical dose deposition kernels in 1000 nm diameter spheres around 50 nm diameter GNPs in response to monoenergetic photons incident on the GNP. Induced “lesions” were estimated by either a local effect model (LEM) or a mean dose model (MDM). The ratio of these estimates was examined for a range of photon energies (10 keV to 2 MeV), for three sets of linear-quadratic parameters. Results: The models produce distinct differences in expected lesion values, the lower the alpha-beta ratio, the greater the difference. The ratio of expected lesion values remained constant within 5% for energies of 40 keV and above across all parameter sets and rose to a difference of 35% for lower energies only for the lowest alpha-beta ratio. Conclusion: Consistent with other work, these calculations suggest nanoscale energy deposition patterns matter in predicting biological response to GNP-enhanced radiotherapy. However the ratio of expected lesions between the different models is largely independent of energy, indicating that GNP-enhanced radiotherapy scenarios can be optimized in photon energy without consideration of the nanoscale patterns. Special attention may be warranted for energies of 20 keV or below and low alpha-beta ratios.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-B-BRB-00: Optimizing the Treatment Planning Process

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Maximizing the U.S. Army’s Future Contribution to Global Security Using the Capability Portfolio Analysis Tool (CPAT)

We report that recent budget reductions have posed tremendous challenges to the U.S. Army in managing its portfolio of ground combat systems (tanks and other fighting vehicles), thus placing many important programs at risk. To address these challenges, the Army and a supporting team developed and applied the Capability Portfolio Analysis Tool (CPAT) to optimally invest in ground combat modernization over the next 25–35 years. CPAT provides the Army with the analytical rigor needed to help senior Army decision makers allocate scarce modernization dollars to protect soldiers and maintain capability overmatch. CPAT delivers unparalleled insight into multiple-decade modernization planning using a novel multiphase mixed-integer linear programming technique and illustrates a cultural shift toward analytics in the Army’s acquisition thinking and processes. CPAT analysis helped shape decisions to continue modernization of the $10 billion Stryker family of vehicles (originally slated for cancellation) and to strategically reallocate over $20 billion to existing modernization programs by not pursuing the Ground Combat Vehicle program as originally envisioned. Ultimately, more than 40 studies have been completed using CPAT, applying operations research methods to optimally prioritize billions of taxpayer dollars and allowing Army acquisition executives to base investment decisions on analytically rigorous evaluations of portfolio trade-offs.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2015

MO-B-BRB-02: Maintain the Quality of Treatment Planning for Time-Constraint Cases

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-B-BRB-03: Systems Engineering Tools for Treatment Planning Process Optimization in Radiation Medicine

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-B-BRB-01: Optimize Treatment Planning Process in Clinical Environment

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-T-546: Modeling and Validation of the a New Proton Therapy System Using a Monte-Carlo Environment Optimized for Protons

8 particle histories were run. Results: Range measurements of the Monte-Carlo simulations matched the measured data within 1mm. Distal fall-off of the simulated fields matched within <1mm. Lateral penumbra and field size measurements of the standard-sized square and half-beam blocked fields matched within 1mm at all three planes compared. A small difference was seen in the in-air profiles at doses <0%. The suspected cause of the difference was the aperture shape. The measured data utilized a divergent aperture. The Monte-Carlo calculation used a non-divergent aperture. Conclusion: The validation measurements indicate that we were able to accurately model the MEVION s250 Proton therapy system using Monte-Carlo Calculations. This may reduce the commissioning time for future users. Purpose: Monte-Carlo modeling is an important tool for understanding the behavior of therapeutic proton beams in a heterogeneous media such as the patient. To gain confidence that a Monte-Carlo model is accurate in complex geometries and media, it must first be compared with measurement in simple situations. This study documents the validation of our Monte-Carlo Model. Methods: A model of the MEVION s250 Proton therapy system was created in the TOPAS Monte-Carlo environment using machine geometry and field shaping system information provided by the vendor. For each of 24 options, validation of the TOPAS model was performed by comparing the dose scored by TOPAS to the dose measurements obtained during the commissioning of the treatment planning system. The measurements compared consisted of: pristine peak depth-dose profiles, in-air profiles for a standard-sized square field (20cm×20cm or 10cm×10cm depending on the maximum field size for each option) at isocenter and at 20cm upstream and downstream of isocenter, and in-air profiles with a half-beam blocked aperture at isocenter and at 20cm upstream and downstream of isocenter. For all Monte-Carlo simulations,.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

Optimization of Beidellite/Polyaniline Production Conditions by Central Composite Design for Removal of Acid Yellow 194

In the adsorption process, the adsorbent structure and its interaction with wastewater are the main determinant of the process efficiency. For this reason, the correct selection of the production conditions for the produced adsorbents is critical. In this study, the reaction time and the concentration of acid, oxidant, substrate, and monomer were selected as independent variables which effect the structure of the polyaniline based composite material. These production variables of polyaniline/beidellite (PANI/BEI) composites were designed by response surface methodology (RSM). According to RSM, 46 different PANI/BEI composites were synthesized and were used as an adsorbent in the removal of acid yellow 194. Thus, the optimum PANI/BEI production conditions were determined. After the identification of the optimum PANI/BEI production conditions, the effect of adsorption conditions such as pH, mixing time, and dye concentration were studied by batch experiments. The maximum adsorption capacity was obtained as 123 (mg g{sup −1}) at the T = 25 °C, t = 24 h, pH 3, m{sub s} = 0.15 g adsorbent, V{sub s} = 50 ml dye solution, and ω = 200 rpm. The results presented in this study revealed that the central composite design is a suitable method to optimize the production conditions of PANI/BEI and the prepared PANI/BEI composites has an important potential as an adsorbent for the removal of metal-complex dyes such as AY194. In addition, the polymerization of aniline and the removal of dye were confirmed by different analytical techniques such as BET, XRD, SEM, and EDAX.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2014

The self-trapping transition in the non-half-filled strongly correlated extended Holstein-Hubbard model in two-dimensions

The two-dimensional extended Holstein-Hubbard model (EHH) has been considered at strong correlation regime in the non-half-filled band case to understand the self-trapping transition of electrons in strongly correlated electron system. We have used the method of optimized canonical transformations to transform an EHH model into an effective extended Hubbard (EEH) model. In the strong on-site correlation limit an EH model can be transformed into a t-J model which is finally solved using Hartree-Fock approximation (HFA). We found that, for non-half-filled band case, the transition is abrupt in the adiabatic region whereas it is continuous in the anti-adiabatic region.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

TurboRVB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo

TurboRVB is a computational package for ab initio Quantum Monte Carlo (QMC) simulations of both molecular and bulk electronic systems. The code implements two types of well established QMC algorithms: Variational Monte Carlo (VMC) and diffusion Monte Carlo in its robust and efficient lattice regularized variant. A key feature of the code is the possibility of using strongly correlated many-body wave functions (WFs), capable of describing several materials with very high accuracy, even when standard mean-field approaches [e.g., density functional theory (DFT)] fail. The electronic WF is obtained by applying a Jastrow factor, which takes into account dynamical correlations, to the most general mean-field ground state, written either as an antisymmetrized geminal power with spin-singlet pairing or as a Pfaffian, including both singlet and triplet correlations. This WF can be viewed as an efficient implementation of the so-called resonating valence bond (RVB) Ansatz, first proposed by Pauling and Anderson in quantum chemistry [L. Pauling, The Nature of the Chemical Bond (Cornell University Press, 1960)] and condensed matter physics [P.W. Anderson, Mat. Res. Bull 8, 153 (1973)], respectively. The RVB Ansatz implemented in TurboRVB has a large variational freedom, including the Jastrow correlated Slater determinant as its simplest, but nontrivial case. Moreover, it has the remarkable advantage of remaining with an affordable computational cost, proportional to the one spent for the evaluation of a single Slater determinant. Therefore, its application to large systems is computationally feasible. The WF is expanded in a localized basis set. Several basis set functions are implemented, such as Gaussian, Slater, and mixed types, with no restriction on the choice of their contraction. The code implements the adjoint algorithmic differentiation that enables a very efficient evaluation of energy derivatives, comprising the ionic forces. Thus, one can perform structural optimizations and molecular dynamics in the canonical NVT ensemble at the VMC level. For the electronic part, a full WF optimization (Jastrow and antisymmetric parts together) is made possible, thanks to state-of-the-art stochastic algorithms for energy minimization. In the optimization procedure, the first guess can be obtained at the mean-field level by a built-in DFT driver. The code was efficiently parallelized by using a hybrid MPI-OpenMP protocol, which is also an ideal environment for exploiting the computational power of modern Graphics Processing Unit accelerators.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Optimal filtering and generation of entangled photons for quantum applications in the presence of noise

Filtering is commonly used in quantum optics to reject noise photons, and also to enable interference between independent photons. However, filtering the joint spectrum of photon pairs can reduce the inherent coincidence probability or loss-independent heralding efficiency. Here we investigate filtering for multiphoton applications based on entanglement and interference (e.g., quantum teleportation). We multiplex C-band entangled photons and C-band classical communications into the same long-distance fibers, which enables scalable low-loss quantum networking but requires filtering of spontaneous Raman scattering noise from classical light. Using tunable-bandwidth filters, low-jitter detectors, and polarization filters, we distribute wavelength-degenerate time-bin entangled photons in the C band (1536.5 nm) over 25 km/25 km standard fibers (50 km) each carrying 10 Gbit/s C-band classical data signals. Narrow filtering enables milliwatt-level C-band power, which exceeds the power achieved by comparable studies by roughly an order of magnitude and could feasibly support terabit per second classical rates. We evaluate how performance depends on pump and filter bandwidths, multipair emission, filter shapes, loss, phase matching, and how quantum information is measured. We find a trade-off between reducing noise impact and increasing single-mode purity, and discuss mitigation methods toward optimal multiphoton applications. Importantly, these results apply to noise in free space and in quantum devices (sources, frequency converters, switches, detectors, etc.) and provide insight into filter-induced degradation of single-photon purity and rates even in noise-free environments.

Thomas, Jordan M. [Fermilab; Northwestern U.]↗

DOE OSTI2024

Direct pulse-level compilation of arbitrary quantum logic gates on superconducting qutrits

Advanced simulations and calculations on quantum computers require high-fidelity implementations of quantum operations. The universal gateset approach builds complex unitaries from a small set of primitive gates, often resulting in a long gate sequence, which is typically a leading factor in the total accumulated error. Compiling a complex unitary for processors with higher-dimensional logical elements, such as qutrits, exacerbates the accumulated error per unitary, since an even longer gate sequence is required. Optimal control methods promise time- and resource-efficient compact gate sequences and, therefore, higher fidelity. These methods generate pulses that can directly implement any complex unitary on a quantum device. In this work, we demonstrate that any arbitrary qubit and qutrit gate can be realized with high fidelity, which can significantly reduce the length of a gate sequence. We generate and test pulses for a large set of randomly selected arbitrary unitaries on several quantum processing units (QPUs): the Lawrence Livermore National Laboratory Quantum Device and Integration Testbed’s (QuDIT’s) standard QPU and three of Rigetti’s QPUs: Ankaa-2, Ankaa-9Q-1, and Aspen-M-3. On the QuDIT platform’s standard QPU, the average fidelity of random qutrit gates is 97.9 ± 0.5% measured with conventional QPT and 98.8 ± 0.6% from QPT with gate folding. Rigetti’s Ankaa-2 achieves random qubit gates with an average fidelity of 98.4 ± 0.5% (conventional QPT) and 99.7 ± 0.1% (QPT with gate folding). On Ankaa-9Q-1 and Aspen-M-3, the average fidelities with conventional qubit QPT measurements were higher than 99% (see Appendix). Here we show that optimal control gates are robust to drift for at least 3 h and that the same calibration parameters can be used for all implemented gates. Our work promises that the calibration overheads for optimal control gates can be made small enough to enable efficient quantum circuits based on this technique.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Optimization of the Radiation Parameters on Technological Installations

To ensure that technological installations using radionuclide sources of radiation operate at top efficiency, the effect of the irradiation system parameters on the radiation utilization factor was studied. It was found that the radiation utilization factor depends not on individual parameters of the irradiation system but rather on a certain combination of these parameters. A dimensionless criterion was determined on the basis of these investigations – a complex based on a set of parameters of installations with maximum radiation utilization factor. The radiation utilization factor was determined as a function of the irradiator–irradiated object distance. The numerical range of the criterion corresponding to the maximum radiation utilization factor is shown. Data by means of Monte Carlo calculations were selected by performing experimental studies using independent dosimetric systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗