Simulating Many-Body Systems with a Projective Quantum Eigensolver
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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Virtualized Logical Qubits: A 2.5D Architecture for Error-Corrected Quantum Computing
Current, near-term quantum devices have shown great progress in the last several years culminating recently with a demonstration of quantum supremacy. In the medium-term, however, quantum machines will need to transition to greater reliability through error correction, likely through promising techniques like surface codes which are well suited for near-term devices with limited qubit connectivity. We discover quantum memory, particularly resonant cavities with transmon qubits arranged in a 2.5D architecture, can efficiently implement surface codes with substantial hardware savings and performance/fidelity gains. Specifically, we virtualize logical qubits by storing them in layers of qubit memories connected to each transmon. Surprisingly, distributing each logical qubit across many memories has a minimal impact on fault tolerance and results in substantially more efficient operations. Our design permits fast transversal application of CNOT operations between logical qubits sharing the same physical address (same set of cavities) which are 6x faster than standard lattice surgery CNOTs. We develop a novel embedding which saves approximately 10x in transmons with another 2x savings from an additional optimization for compactness. Although qubit virtualization pays a 10x penalty in serialization, advantages in the transversal CNOT and in area efficiency result in fault-tolerance and performance comparable to conventional 2D transmon-only architectures. Our simulations show our system can achieve fault tolerance comparable to conventional two-dimensional grids while saving substantial hardware. Furthermore, our architecture can produce magic states at 1.22x the baseline rate given a fixed number of transmon qubits. Here, this is a critical benchmark for future fault-tolerant quantum computers as magic states are essential and machines will spend the majority of their resources continuously producing them. This architecture substantially reduces the hardware requirements for fault-tolerant quantum computing and puts within reach a proof-of-concept experimental demonstration of around 10 logical qubits, requiring only 11 transmons and 9 attached cavities in total.
quantum computing↗
Non-equilibrium plasma generation via nano-second multi-mode laser pulses
The formation and growth of plasma kernels generated via nano-second mode-beating laser pulses is investigated here via a non-equilibrium self-consistent computational model. Chemically reactive Navier–Stokes equations are used to describe the hydrodynamics, and non-equilibrium effects are taken into account with a two-temperature model. Inverse Bremsstrahlung and multiphoton ionization are included self-consistently in the model via a coupled solution of the plasma governing equations and the radiative transfer equation (that describes the laser beam propagation and attenuation). A self-consistent approach (despite carrying additional challenges) minimizes the empiricism and it allows for a more accurate description since it prevents both the utilization of artificial plasma seeds to trigger the breakdown and the implementation of tuning parameters to simulate the laser-energy deposition. The advantages of this approach are confirmed by the good agreement between the numerically predicted and the experimentally measured plasma boundary evolution and absorbed energy. This also holds true for the periodic plasma kernel structures that, as suggested by the experiments and confirmed by the simulations presented here, are connected to the modulating frequency.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Qubit-efficient entanglement spectroscopy using qubit resets
One strategy to fit larger problems on NISQ devices is to exploit a tradeoff between circuit width and circuit depth. Unfortunately, this tradeoff still limits the size of tractable problems since the increased depth is often not realizable before noise dominates. Here, we develop qubit-efficient quantum algorithms for entanglement spectroscopy which avoid this tradeoff. In particular, we develop algorithms for computing the trace of the n -th power of the density operator of a quantum system, T r ( ρ n ) , (related to the Rényi entropy of order n ) that use fewer qubits than any previous efficient algorithm while achieving similar performance in the presence of noise, thus enabling spectroscopy of larger quantum systems on NISQ devices. Our algorithms, which require a number of qubits independent of n , are variants of previous algorithms with width proportional to n , an asymptotic difference. The crucial ingredient in these new algorithms is the ability to measure and reinitialize subsets of qubits in the course of the computation, allowing us to reuse qubits and increase the circuit depth without suffering the usual noisy consequences. We also introduce the notion of effective circuit depth as a generalization of standard circuit depth suitable for circuits with qubit resets. This tool helps explain the noise-resilience of our qubit-efficient algorithms and should aid in designing future algorithms. We perform numerical simulations to compare our algorithms to the original variants and show they perform similarly when subjected to noise. Additionally, we experimentally implement one of our qubit-efficient algorithms on the Honeywell System Model H0, estimating T r ( ρ n ) for larger n than possible with previous algorithms.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis
Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.
Hydrocarbons↗
Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties
X-ray Thomson scattering (XRTS) constitutes an essential technique for diagnosing material properties under extreme conditions, such as high pressures and intense laser heating. Time-dependent density functional theory (TDDFT) is one of the most accurate available ab initio methods for modeling XRTS spectra, as well as a host of other dynamic material properties. However, strong thermal excitations, along with the need to account for variations in temperature and density as well as the finite size of the detector significantly increase the computational cost of TDDFT simulations compared to ambient conditions. In this work, we present a broadly applicable method for optimizing and enhancing the efficiency of TDDFT calculations. Our approach is based on a one-to-one mapping between the dynamic structure factor and the imaginary time density–density correlation function, which naturally emerges in Feynman’s path integral formulation of quantum many-body theory. Specifically, we combine rigorous convergence tests in the imaginary time domain with a constraints-based attenuation of narrow-band fluctuations to improve the efficiency of TDDFT modeling without the introduction of any significant bias. As a result, we can report a speed-up by up to an order of magnitude, thus substantially reducing the burden of computational cost required for XRTS analysis.
Moldabekov, Zhandos A. [Helmholtz-Zentrum Dresden-↗
Transferability of data-driven, many-body models for CO 2 simulations in the vapor and liquid phases
Here, extending on the previous work by Riera et al. [J. Chem. Theory Comput. 16, 2246–2257 (2020)], we introduce a second generation family of data-driven many-body MB-nrg models for CO 2 and systematically assess how the strength and anisotropy of the CO 2 –CO 2 interactions affect the models’ ability to predict vapor, liquid, and vapor–liquid equilibrium properties. Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting one-body and two-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer training sets. Advancing from the first generation models, we employ the charge model 5 scheme to determine the atomic charges and systematically scale the two-body energies to obtain more accurate descriptions of vapor, liquid, and vapor–liquid equilibrium properties. Challenges in model construction arise due to the anisotropic nature and small magnitude of the interaction energies in CO 2 , calling for the necessity of highly accurate descriptions of the multidimensional energy landscape of liquid CO 2 . These findings emphasize the key role played by the training set quality in the development of transferable, data-driven models, which, accurately representing high-dimensional many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Neutrino fast flavor oscillations with moments: Linear stability analysis and application to neutron star mergers
Providing an accurate modeling of neutrino physics in dense astrophysical environments such as binary neutron star mergers presents a challenge for hydrodynamic simulations. Nevertheless, understanding how flavor transformation can occur and affect the dynamics, the mass ejection, and the nucleosynthesis will need to be achieved in the future. Computationally expensive, large-scale simulations frequently evolve the first classical angular moments of the neutrino distributions. By promoting these quantities to matrices in flavor space, we develop a linear stability analysis of fast flavor oscillations using only the first two “quantum” moments, which notably requires generalizing the classical closure relations that appropriately truncate the hierarchy of moment equations in order to treat quantum flavor coherence. After showing the efficiency of this method on a well-understood test situation, we perform a systematic search of the occurrence of fast flavor instabilities in a neutron star merger simulation. Here, we discuss the successes and shortcomings of moment linear stability analysis, as this framework provides a time-efficient way to design and study better closure prescriptions in the future.
79 ASTRONOMY AND ASTROPHYSICS↗
Ab Initio Quantum Information Processor Design with Single-Molecule Magnets: A Multiscale Modeling Approach (Final Report)
This final report summarizes the team's efforts to develop a multiscale modeling approach that ranges from different levels of ab-initio quantum chemistry simulations to effective models and time-dependent external control, and to use this approach to systematically design quantum information processors with TbPc 2 single-molecule magnets. The impact of the work is two-fold: (i) New quantum chemistry simulation techniques capable of treating complex, multiscale problems such as the TbPc 2 molecule were developed, and (ii) the prospects for building quantum processors based on single-molecule magnets coupled by superconducting transmission line resonators were analyzed. The outcomes of this project revealed that current technology is at the cusp of being able to realize the main components of such a processor, and they highlighted the need to achieve stronger molecule-resonator interactions to enhance the viability of this approach. The multiscale modeling techniques developed during this project are general and transferable to other molecules and will thus have a broad impact on the field of quantum chemistry. Methods for controlling and simulating many coupled qubits developed here will also impact other quantum information technologies.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Machine learning framework for quantum sampling of highly constrained, continuous optimization problems
In recent years, there is growing interest in using quantum computers for solving combinatorial optimization problems. In this work, we developed a generic, machine learning-based framework for mapping continuous-space inverse design problems into surrogate quadratic unconstrained binary optimization (QUBO) problems by employing a binary variational autoencoder and a factorization machine. The factorization machine is trained as a low-dimensional, binary surrogate model for the continuous design space and sampled using various QUBO samplers. Using the D-Wave Advantage hybrid sampler and simulated annealing, we demonstrate that by repeated resampling and retraining of the factorization machine, our framework finds designs that exhibit figures of merit exceeding those of its training set. We showcase the framework’s performance on two inverse design problems by optimizing (i) thermal emitter topologies for thermophotovoltaic applications and (ii) diffractive meta-gratings for highly efficient beam steering. This technique can be further scaled to leverage future developments in quantum optimization to solve advanced inverse design problems for science and engineering applications.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Neural-network model for force prediction in multi-principal-element alloys
Atomistic simulations can provide useful insights into the physical properties of multi-principal-element alloys. However, classical potentials mostly fail to capture key quantum (electronic-structure) effects. Here we present a deep 3D convolutional neural network (3D CNN) based framework combined with a voxelization technique to design interatomic potentials for chemically complex alloys. We highlight the performance of the 3D CNN model and its efficacy in computing potentials using the medium-entropy alloy TaNbMo. In order to provide insights into the effect of voxel resolution, we implemented two approaches based on the inner and outer bounding boxes. An efficient 3D CNN model, which is as accurate as the density-functional theory (DFT) approach, for calculating potentials will provide a promising schema for accurate atomistic simulations of structure and dynamics of general multi-principle element alloys.
36 MATERIALS SCIENCE↗
A Tensor Network-Based Quantum Algorithm for the Nonlinear 1D Burgers' Equation
In this work, we implement a tensor network-based quantum algorithm to solve unsteady, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the compressible 1-dimensional (1D) Burgers' equation as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts to solve nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. Our framework is based on matrix product states (MPSs) and matrix product operators (MPOs). For example, the velocity field is represented by MPS, whereas the linear and nonlinear spatial differential terms of the velocity field are processed by MPOs. Our primary focus herein is to verify and validate the various tensor network components of the algorithm using solutions obtained by the classical algorithms on high performance computing (HPC) architectures. We use a classical time marching method to demonstrate the functionality of the tensor network operations to model the PDE and their robustness with the time evolution of the system. Our classical simulation results demonstrate the utility of tensor network-based operations in modeling nonlinear PDEs and highlight the necessity as well as potential advantages of using quantum simulations for these techniques.
Gopalakrishnan Meena, Murali [ORNL] (ORCID:0000000↗
Computational Insights into Malononitrile-Based Carbanions for CO 2 Capture
Although anionic N and O sites have been widely used in chemisorption of CO 2 , carbanions are much less explored for CO 2 capture. Here, in this study, we employ ab initio calculations and quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) simulations to examine the interaction between CO 2 and the malononitrile carbanion, [CH(CN) 2 ] – . We have explored the potential energy surface of CO 2 binding by scanning the C–C distance between CO 2 and the central C site of the carbanion. We find that CO 2 prefers to bind to the nitrile group physically rather than to form a C–C bond via the carboxylation reaction at the sp 2 C site. Moreover, the two –CN groups can attract two CO 2 molecules at equal strength. The presence of an alkali metal ion enhances both physical and chemical interactions of CO 2 with the malononitrile carbanion. QM/MM MD simulations further confirm the preference of physical interaction in the condensed ionic liquid phase with a phosphonium cation. Our findings suggest that ionic liquids based on the malononitrile carbanion may have a high CO 2 solubility for carbon capture.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Highly selective Si 3 N 4 /SiO 2 etching using an NF 3 /N 2 /O 2 /H 2 remote plasma. I. Plasma source and critical fluxes
Highly selective plasma etching of silicon nitride (Si 3 N 4 ), while not etching silicon dioxide (SiO 2 ), is a critical step in the fabrication of microelectronics devices. In many applications, this etching must be damage-free and isotropic, which then motivates the use of remote plasmas where the reactants interacting with the substrate are dominantly neutral species. In this paper and Paper II, mechanisms for highly selective Si 3 N 4 etching in remote plasmas are discussed based on results from experiments and simulations. It has been shown experimentally that high Si 3 N 4 /SiO 2 etch selectivity (≈380) can be achieved in the downstream effluent of an NF 3 /N 2 /O 2 /H 2 plasma. The authors found that H2 plays a principal role in the reaction mechanism as Si 3 N 4 /SiO 2 selectivity shows a sharp maximum as a function of the H2 flow rate. Based on this observation, and measured densities of F-atoms and H2 in the process chamber, a mechanism of selective Si 3 N 4 /SiO 2 etching is proposed in which HF molecules in vibrationally excited states accelerate etching reactions. A reaction mechanism for NF 3 /N 2 /O 2 /H 2 plasmas and its afterglow was developed to computationally determine the species densities and fluxes on the wafer level, validated by comparing with experimentally measured F-atom and H 2 densities. The calculated species densities and fluxes were used as input to an analytical model of Si 3 N 4 and SiO 2 etching based on the results of quantum chemistry simulations. This paper presents experimental results (etching data and species densities), the reaction mechanism for NF 3 /N 2 /O 2 /H 2 plasmas, and the results of simulations of gas phase chemistry. Quantum chemistry simulations of elementary etching reactions, description of the analytical model of Si 3 N 4 and SiO 2 etching, calculations of the etch rates, and Si 3 N 4 and SiO 2 selectivity with this model are presented in Paper II.
42 ENGINEERING↗
Generating and processing optical waveforms using spectral singularities
Here, we show that a laser at threshold can be utilized to generate the class of coherent and transform-limited waveforms (vt — z) m e i(kz—ωt) at optical frequencies. We derive these properties analytically and demonstrate them in semiclassical time-domain laser simulations. We then utilize these waveforms to expand other waveforms with high modulation frequencies and demonstrate theoretically the feasibility of complex-frequency coherent absorption at optical frequencies, with efficient energy transduction and cavity loading. This approach has potential applications in quantum computing, photonic circuits, and biomedicine.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Predictive Model of Charge Mobilities in Organic Semiconductor Small Molecules with Force-Matched Potentials
Charge mobility of crystalline organic semiconductors (OSC) is limited by local dynamic disorder. Recently, the charge mobility for several high mobility OSCs, including TIPS-pentacene, were accurately predicted from a density functional theory (DFT) simulation constrained by the crystal structure and the inelastic neutron scattering spectrum, which provide direct measures of the structure and the dynamic disorder in the length scale and energy range of interest. However, the computational expense required for calculating all of the atomic and molecular forces is prohibitive. In this work, we demonstrate the use of density functional tight binding (DFTB), a semiempirical quantum mechanical method that is 2 to 3 orders of magnitude more efficient than DFT. We show that force matching a many-body interaction potential to DFT derived forces yields highly accurate DFTB models capable of reproducing the low-frequency intricacies of experimental inelastic neutron scattering (INS) spectra and accurately predicting charge mobility. We subsequently predicted charge mobilities from our DFTB model of a number of previously unstudied structural analogues to TIPS-pentacene using dynamic disorder from DFTB and transient localization theory. The approach we establish here could provide a truly rapid simulation pathway for accurate materials properties prediction, in our vision applied to new OSCs with tailored properties.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
mystic : software for autonomous discovery and design under uncertainty
Throughout the diverse range of science and engineering applications, there is a growing desire to develop computational methods that can reliably predict the behavior of complex systems. Specifically, there is a strategic need for tools that can robustly forecast the behavior of complex physical systems, where data may be high-dimensional, noisy, or sparse, and models of the system may be time-dependent or include uncertainty. We use mystic to build tools that leverage statistical learning, physics-informed learning, and active learning in the efficient generation of reliably predictive surrogates for complex physical systems. mystic is a robust, proven, open-source optimization and uncertainty quantification toolkit with over a decade of use in the design and optimization of neutron instrumentation, solar-powered drones, and gasguns, and in iterative tuning of models for Raman spectroscopy and elastoplastic materials strength. Recent developments have focused on automated learning of statistically robust surrogates under uncertainty, with applications in materials in extreme environments, nanostructures, materials simulations and strength models, and the failure of shielding under particle radiation. In 2020, McKerns demonstrated active learning of optimally robust surrogates with respect to new simulated data for molecular dynamics simulations of materials mixing in warm dense matter, and is currently applying active learning to the automated steering of particle accelerator beams and the optimal design and control of quantum optical sensor instrumentation.
42 ENGINEERING↗
Multi-species collisions for delta-f gyrokinetic simulations: Implementation and verification with GENE
we report a multi-species linearized collision operator based on the model developed by Sugama et al. has been implemented in the nonlinear gyrokinetic code, GENE. Such a model conserves particles, momentum, and energy to machine precision, and is shown to have negative definite free energy dissipation characteristics, satisfying Boltzmann’s H-theorem, including for realistic mass ratio. Finite Larmor Radius (FLR) effects have also been implemented into the local version of the code. For the global version of the code, the collision operator has been developed to allow for block-structured velocity space grids, allowing for computationally tractable collisional global simulations. The validity of the collision operator has been demonstrated by relaxation and conservation tests, as well as appropriate benchmarks. The newly implemented operator shall be used in future simulations to study magnetically confined fusion plasma turbulence and transport in more extreme regions with higher collisionality.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗