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Cooperative Interplay of van der Waals Forces and Quantum Nuclear Effects on Adsorption: H at Graphene and at Coronene
The energetic barriers that atoms and molecules typically experience when binding to surfaces are incredibly important to a myriad of chemical and physical processes. Yet, these barriers are difficult to describe accurately with current computer simulation approaches. Two prominent contemporary challenges faced by simulation are the role of van der Waals forces and nuclear quantum effects. In this work, we examine the widely studied model systems of hydrogen on graphene and coronene using a van der Waals inclusive density functional theory approach together with path integral molecular dynamics at 50 K. We find that both van der Waals and quantum nuclear effects work together in a cooperative manner to dramatically reduce the barriers for hydrogen atoms to adsorb. This predicts that the low temperature hydrogenation of graphene is easier than previously thought and in more general terms that the combined roles of van der Waals and quantum tunnelling can lead to qualitative changes in adsorption.
Simulation of micro-magnet stray-field dynamics for spin qubit manipulation
High-fidelity control and unprecedented long dephasing times in silicon-based single spin qubits have recently confirmed the prospects of solid-state quantum computation. We investigate the feasibility of using a micro-magnet stray field for all-electrical, addressable spin qubit control in a Si/SiGe double quantum dot. For a micro-magnet geometry optimized for high Rabi-frequency, addressability, and robustness to fabrication misalignment as previously demonstrated by Yoneda et al. [Phys. Rev. Lett. 113, 267601 (2014)], we simulate the qubit decoherence due to magnetic stray-field fluctuations, which may dominate in nuclear spin-free systems, e.g., quantum dots in Si/SiGe, Si-MOS structures and (bilayer) graphene. With calculated Rabi-frequencies of 15 MHz, a qubit addressability error below 10{sup −3} is achievable. Magnetic fluctuations from a micro-magnet limits the spin relaxation time to T{sub 1} ≳ 3 s, while pure spin dephasing is negligible. Our results show that micro-magnets are a promising tool for spin qubit computation in nuclear spin-free systems.
A Monte Carlo approach to validation of FFF VMAT treatment plans for the TrueBeam linac
Purpose: To commission and benchmark a vendor-supplied (Varian Medical Systems) Monte Carlo phase-space data for the 6 MV flattening filter free (FFF) energy mode on a TrueBeam linear accelerator for the purpose of quality assurance of clinical volumetric modulated arc therapy (VMAT) treatment plans. A method for rendering the phase-space data compatible with BEAMnrc/DOSXYZnrc simulation software package is presented. Methods: Monte Carlo (MC) simulations were performed to benchmark the TrueBeam 6 MV FFF phase space data that have been released by the Varian MC Research team. The simulations to benchmark the phase space data were done in three steps. First, the original phase space which was created on a cylindrical surface was converted into a format that was compatible with BEAMnrc. Second, BEAMnrc was used to create field size specific phase spaces located underneath the jaws. Third, doses were calculated with DOSXYZnrc in a water phantom for fields ranging from 1 Multiplication-Sign 1 to 40 Multiplication-Sign 40 cm{sup 2}. Calculated percent depth doses (PDD), transverse profiles, and output factors were compared with measurements for all the fields simulated. After completing the benchmarking study, three stereotactic body radiotherapy (SBRT) VMAT plans created with the Eclipse treatment planning system (TPS) were calculated with Monte Carlo. Ion chamber and film measurements were also performed on these plans. 3D gamma analysis was used to compare Monte Carlo calculation with TPS calculations and with film measurement. Results: For the benchmarking study, MC calculated and measured values agreed within 1% and 1.5% for PDDs and in-field transverse profiles, respectively, for field sizes >1 Multiplication-Sign 1 cm{sup 2}. Agreements in the 80%-20% penumbra widths were better than 2 mm for all the fields that were compared. With the exception of the 1 Multiplication-Sign 1 cm{sup 2} field, the agreement between measured and calculated output factors was within 1%. It is of note that excellent agreement in output factors for all field sizes including highly asymmetric fields was achieved without accounting for backscatter into the beam monitor chamber. For the SBRT VMAT plans, the agreement between Monte Carlo and ion chamber point dose measurements was within 1%. Excellent agreement between Monte Carlo, treatment planning system and Gafchromic film dose distribution was observed with over 99% of the points in the high dose volume passing the 3%, 3 mm gamma test. Conclusions: The authors have presented a method for making the Varian IAEA compliant 6 MV FFF phase space file of the TrueBeam linac compatible with BEAMnrc/DOSXYZnrc. After benchmarking the modified phase space against measurement, they have demonstrated its potential for use in MC based quality assurance of complex delivery techniques.
Data Access Performance Through Parallelization and Vectored Access: Some Results
High Energy Physics data processing and analysis applications typically deal with the problem of accessing and processing data at high speed. Recent studies, development and test work have shown that the latencies due to data access can often be hidden by parallelizing them with the data processing, thus giving the ability to have applications which process remote data with a high level of efficiency. Techniques and algorithms able to reach this result have been implemented in the client side of the Scalla/xrootd system, and in this contribution we describe the results of some tests done in order to compare their performance and characteristics. These techniques, if used together with multiple streams data access, can also be effective in allowing to efficiently and transparently deal with data repositories accessible via a Wide Area Network.
Building a Universal Nuclear Energy Density Functional (UNEDF). SciDAC-2 Project
An understanding of the properties of atomic nuclei is crucial for a complete nuclear theory, for element formation, for properties of stars, and for present and future energy and defense applications. During the period of Dec. 1 2006 – Jun. 30, 2012, the UNEDF collaboration carried out a comprehensive study of all nuclei, based on the most accurate knowledge of the strong nuclear interaction, the most reliable theoretical approaches, the most advanced algorithms, and extensive computational resources, with a view towards scaling to the petaflop platforms and beyond. Until recently such an undertaking was hard to imagine, and even at the present time such an ambitious endeavor would be far beyond what a single researcher or a traditional research group could carry out. The UNEDF SciDAC project has developed several key computational codes and algorithms for reaching the goal of solving the nuclear quantum many-body problem throughout the chart of nuclei. Without such developments, scientific progress would not be possible. In addition the UNEDF SciDAC successfully applied these developments to solve many forefront research problems.
Nuclear reactor transient analysis via a quasi-static kinetics Monte Carlo method
The predictor-corrector quasi-static (PCQS) method is applied to the Monte Carlo (MC) calculation for reactor transient analysis. To solve the transient fixed-source problem of the PCQS method, fission source iteration is used and a linear approximation of fission source distributions during a macro-time step is introduced to provide delayed neutron source. The conventional particle-tracking procedure is modified to solve the transient fixed-source problem via MC calculation. The PCQS method with MC calculation is compared with the direct time-dependent method of characteristics (MOC) on a TWIGL two-group problem for verification of the computer code. Then, the results on a continuous-energy problem are presented.
Toward scalable quantum computations of atomic nuclei
We solve the nuclear two-body and three-body bound states via quantum simulations of pionless effective field theory on a lattice in position space. While the employed lattice remains small, the usage of local Hamiltonians including two- and three-body forces ensures that the number of Pauli terms scales linearly with increasing numbers of lattice sites. We use an adaptive ansatz grown from unitary coupled cluster theory to parametrize the ground states of the deuteron and 3 He, compute their corresponding energies, and analyze the scaling of the required computational resources. Our quantum simulations reproduce exact benchmarks for 2 H and 3 He within 100 keV, requiring at most 30 layers in the ansatz and thus resulting in modest circuit depths. Additionally, we find the number of shots required to reach a given precision scales linearly in the lattice size and more mildly in the system size. Furthermore, based on the agreement with exact benchmarks and mild scaling, we conclude that this can be an efficient, scalable approach for quantum computations of nuclear ground states, particularly to prepare initial states for quantum phase estimation or other filtering algorithms.
Precision sensing assisted by quantum-classical computation [Slides]
The Center for Nonlinear Studies (CNLS) seminar focus is on quantum sensing for room-temperature NMR, magnetic material, living system (e.g., C. elegance), gravitational wave detection, and dark matter.
Precision Tomography of a Three-Qubit Donor Quantum Processor in Silicon
Nuclear spins were among the first physical platforms to be considered for quantum information processing, because of their exceptional quantum coherence and atomic-scale footprint. However, their full potential for quantum computing has not yet been realized, owing to the lack of methods with which to link nuclear qubits within a scalable device combined with multi-qubit operations with sufficient fidelity to sustain fault-tolerant quantum computation. Here we demonstrate universal quantum logic operations using a pair of ion-implanted 31 P donor nuclei in a silicon nanoelectronic device. A nuclear two-qubit controlled-Z gate is obtained by imparting a geometric phase to a shared electron spin, and used to prepare entangled Bell states with fidelities up to 94.2(2.7)%. The quantum operations are precisely characterized using gate set tomography (GST), yielding one-qubit average gate fidelities up to 99.95(2)%, two-qubit average gate fidelity of 99.37(11)% and two-qubit preparation/measurement fidelities of 98.95(4)%. These three metrics indicate that nuclear spins in silicon are approaching the performance demanded in fault-tolerant quantum processors. We then demonstrate entanglement between the two nuclei and the shared electron by producing a Greenberger-Horne-Zeilinger three-qubit state with 92.5(1.0)% fidelity. Because electron spin qubits in semiconductors can be further coupled to other electrons or physically shuttled across different locations, these results establish a viable route for scalable quantum information processing using donor nuclear and electron spins.
LDRD FY25 Program Overview
As Lawrence Livermore National Laboratory’s (LLNL’s) Laboratory Directed Research and Development (LDRD) program enters its fifth decade of leading-edge research and development, its impact and importance have never been stronger. The program continues to advance strategic investments in pioneering science, technology, and engineering, ensuring LLNL will be ready to deliver on our mission as it evolves over the coming decades. Investing in LDRD research, and the people who perform this critical work, gives LLNL the ability to sustain our role as a leader in the Department of Energy and National Nuclear Security Administration enterprise. The LDRD program enables high-risk, high-payoff research that anticipates emerging threats and future mission needs. By nurturing the ingenuity of the Lab’s greatest asset, its people, LDRD funding advances not only our research but also grows and nurtures our workforce: engaging future innovators with student mentoring, challenging postdoctoral researchers to apply their skills to support national security, and strengthening the leadership skills of early career staff. This annual report documents how LDRD investments advance LLNL’s science, technology, and engineering across our mission space. To assess LDRD’s impact we track both short and long-term metrics such as peer-reviewed publications, number of students, or professional fellows. In addition to reviewing these metrics, I encourage you to delve deeper into the breadth of science and technology that illustrate the strategic value of this research portfolio. For instance, a recent exploratory research project used advanced manufacturing to construct miniaturized three-dimensional ion traps for a quantum computer with reduced quantum error rates to enable applications that address national security missions and support basic science. Another project has delved into studying detonation by examining deflagration to enhance the safety and security of the nuclear weapons stockpile. LDRD researchers are also deploying AI agents on two of the world’s most powerful supercomputers to automate and accelerate inertial confinement fusion experiments. Other teams are delivering more accurate optical constants to enable improved validation for aluminum to advance atomic and molecular physics models. LDRD-driven discoveries of how metals deform under extreme conditions strengthen our ability to model and design materials for demanding national security environments. National security challenges are increasingly complex and continuously evolving. LDRD focuses our most innovative science and technology on these challenges, ensuring the Laboratory is developing creative, forward-leaning solutions for our nation and the world. The following pages feature highlights of published scientific advances, patents, and honors that stem from LDRD investments. As you read this report, I hope you will understand how these investments position the Laboratory, and our partners, to meet the demands of the decades ahead.
High speed automated microtomography of nuclear emulsions and recent application
The development of high-speed automatic scanning systems was the key-factor for massive and successful emulsions application for big neutrino experiments like OPERA. The emulsion detector simplicity, the unprecedented sub-micron spatial resolution and the unique ability to provide intrinsically 3-dimensional spatial information make it a perfect device for short-living particles study, where the event topology should be precisely reconstructed in a 10-100 um scale vertex region. Recently the exceptional technological progress in image processing and automation together with intensive R&D done by Italian and Japanese microscopy groups permit to increase the scanning speed to unbelievable few years ago m{sup 2}/day scale and so greatly extend the range of the possible applications for emulsion-based detectors to other fields like: medical imaging, directional dark matter search, nuclear physics, geological and industrial applications.
Machine learning applied to classifying neutron resonances
The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit resonance structure whose shape is determined in part by the angular momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is therefore of paramount importance. In this project, we apply a machine learning technique, namely decision trees, to automate the quantum number assignments. The tree is trained from simulated data generated to mimic the errors found in real data. We explore the use of several physics-motivated features for training our trees, including the nearest neighbor spacing distribution, cumulative level distribution, and channel width distributions. Initial results using random matrix theory motivated fits which demonstrated that we can determine resonance spin groups somewhat reliably. If we use these fits as features in our trees, we can train them to spot outliers corresponding to misassigned resonances. We found that with the large number of features used in this project that the decision tree tended to over t training data resulting in poor performance with respect to the test data. By reducing the number of features, we can achieve nearly perfect assignment of quantum numbers with our training data.
Preparation of excited states for nuclear dynamics on a quantum computer
We study two different methods to prepare excited states on a quantum computer, a key initial step to study dynamics within linear response theory. The first method uses unitary evolution for a short time T = O(√1 - F) to approximate the action of an excitation operator Ô with fidelity F and success probability P ≈ 1 – F. The second method probabilistically applies the excitation operator using the Linear Combination of Unitaries (LCU) algorithm. We benchmark these techniques on emulated and real quantum devices, using a toy model for thermal neutron-proton capture. Despite its larger memory footprint, the LCU-based method is efficient even on current generation noisy devices and can be implemented at a lower gate cost than a naive analysis would suggest. Here, these findings show that quantum techniques designed to achieve good asymptotic scaling on fault tolerant quantum devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
Multi-Neutrino Entanglement and Correlations in Dense Neutrino Systems
The time-evolution of multi-neutrino entanglement and correlations are studied in two-flavor collective neutrino oscillations, relevant for dense neutrino environments, building upon previous works. Specifically, simulations performed of systems with up to 12 neutrinos using Quantinuum’s H1-1 20 qubit trapped-ion quantum computer are used to compute n-tangles, and two- and three- body correlations, probing beyond mean-field descriptions. n-tangle re-scalings are found to converge for large system sizes, signaling the presence of genuine multi-neutrino entanglement.
Multi-hadron systems via Lattice QCD
This project supported a broad set of research in theoretical nuclear physics which has been designed to support current and future experimental DOE facilities. The broad summary of physical observables of interest in this project were the bread-and-butter observables obtained in experimental nuclear and particle physics experiments, namely scattering amplitude including multi-hadronic states. The primary goal of this project was to design a systematically improvable paradigm for accessing such amplitudes directly from the Standard Model of Particle Physics. The main tools that were used and explored in this project were scattering theory, lattice QCD, and future quantum computers. The physics outputs included various novel formalisms, exploratory lattice QCD calculations, and a new proposal for studying any scattering observable using future quantum computers.
Quantum Monte Carlo calculations in configuration space with three-nucleon forces
Neutron matter, through its connection to neutron stars as well as systems like cold atom gases, is one of the most interesting yet computationally accessible systems in nuclear physics. The Configuration-Interaction Monte Carlo (CIMC) method is a stochastic many-body technique allowing to tackle strongly coupled systems. In contrast to other Quantum Monte Carlo methods employed in nuclear physics, the CIMC method can be formulated directly in momentum space allowing for an efficient use of non-local interactions. In this work we extend CIMC method to include three-nucleon interactions through the normal-ordered two-body approximation. Here, we present results for the equation of state of neutron matter in line with other many-body calculations that employ low resolution chiral interactions, and provide predictions for the momentum distribution and the static structure factor.
Fission with Exotic Nuclei (Full Technical Report)
Despite its importance for stockpile stewardship or nuclear forensics, data on fission is fragmentary: most experiments have only been performed on stable actinide nuclei and are often incomplete, and theoretical simulations often contain many parameters hard to constrain, which results in large uncertainties. Consequently, nuclear libraries have large gaps for important isotopes. The goal of this project was to prepare to take advantage of the unprecedented yields of radioactive isotopes at the upcoming DOE Facility for Rare Ion Beams and to leverage recent progress in the field of machine learning to develop a comprehensive program of fission studies that could address some of the most pressing problems in nuclear fission over the next several decades. Our project leveraged synergies between experimental nuclear physics, nuclear theory, and data science expertise at LLNL. On the experimental front, our main objective was to develop in-house expertise for inverse kinematics reactions with relativistic beams as well as to field and test new detectors to perform correlated measurements of fission properties. On the theoretical side, the objective was to develop a novel, high-fidelity and scalable approach of fissionfragment calculations based on emulating computationally expensive, quantum-mechanical calculations of nuclear properties with deep neural networks.