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At least 1,621 records · Page 90

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DOE OSTI2020

chimes_calculator

The Chebyshev Interaction Model for Efficient Simulation (ChIMES) is a machine-learned interatomic potential targeting chemistry in condensed phase systems. ChIMES models are able to approach quantum-accuracy through a systematically improvable explicitly many-bodied basis comprised of linear combinations of Chebyshev polynomials. Though originally developed as to enable description of organic molecular materials, ChIMES has successfully been applied to systems spanning ambient water to molten carbon, and leveraged as correction for density functional based tight binding simulations. The ChIMES calculator comprises a flexible toolset for evaluating ChIMES interactions (e.g. in simulations, single point calculations, etc). Users have the option of directly imbedding the ChIMES calculator within their codes, or evaluating interactions through the beginner-friendly serial interface, each of which have Python, C++, C, and Fortran API's.

Goldman, Nir↗

DOE OSTI2021

Code interoperability extends the scope of quantum simulations

The functionality of many materials is critically dependent on the integration of dissimilar components and on the interfaces that arise between them. The description of such heterogeneous components requires the development and deployment of first principles methods, coupled to appropriate dynamical descriptions of matter and advanced sampling techniques, in order to capture all the relevant length and time scales of importance to the materials’ performance. It is thus essential to build simple, streamlined computational schemes for the prediction and design of multiple properties of broad classes of materials, by developing interoperable codes which can be efficiently coupled to each other to perform complex tasks. We discuss the use of interoperable codes to simulate the structural and spectroscopic characterization of materials, including chemical reactions for catalysis, the description of defects for quantum information science, and heat and charge transport.

36 MATERIALS SCIENCE↗

DOE OSTI2023

QuComm: Optimizing Collective Communication for Distributed Quantum Computing

Distributed quantum computing (DQC) is a scalable way to build a large-scale quantum computing system while the error-prone nonlocal communication between DQC nodes may heavily degrade the fidelity of the distributed quantum program and thus demands specific compiler optimizations. Previous compilers on DQC communication optimization either assumes unlimited communication resource or a few communication qubits due to the hardware limitation. The former compilers may not be efficient when interfacing with communication-resource-constrained DQC hardware while the latter compilers lose the opportunities of optimizing collective communication and routing concurrent communication as they unnecessarily couple limited communication qubits with the implementation of expensive inter-node operations. In this paper, we invent the communication buffer, a communication facility consisting of idle qubits in each compute node, to decouple the execution of inter-node quantum operations from communication qubits: communication qubits are devoted to generating inter-node entanglement while internode operations are conducted in the communication buffer. The communication buffer provides an intermediate layer for inter-node communication and paves the way for collective communication optimization. We then propose QuComm, a buffer-based compiler framework that first performs smart buffer allocation according to communication characteristics of the distributed quantum program and then optimizes and collectively routes inter-node quantum operations. Experimental results on a hierarchical DQC system show that the proposed QuComm can reduce the most expensive inter-node communication request and the latency of various distributed quantum programs by 50.4% and 47.6% on average, respectively.

Wu, Anbang↗

DOE OSTI2023

Quantum Key Distribution for Critical Infrastructures: Towards Cyber-Physical Security for Hydropower and Dams

Hydropower facilities are often remotely monitored or controlled from a centralized remote control room. Additionally, major component manufacturers monitor the performance of installed components, increasingly via public communication infrastructures. While these communications enable efficiencies and increased reliability, they also expand the cyber-attack surface. Communications may use the internet to remote control a facility’s control systems, or it may involve sending control commands over a network from a control room to a machine. The content could be encrypted and decrypted using a public key to protect the communicated information. These cryptographic encoding and decoding schemes become vulnerable as more advances are made in computer technologies, such as quantum computing. In contrast, quantum key distribution (QKD) and other quantum cryptographic protocols are not based upon a computational problem, and offer an alternative to symmetric cryptography in some scenarios. Although the underlying mechanism of quantum cryptogrpahic protocols such as QKD ensure that any attempt by an adversary to observe the quantum part of the protocol will result in a detectable signature as an increased error rate, potentially even preventing key generation, it serves as a warning for further investigation. In QKD, when the error rate is low enough and enough photons have been detected, a shared private key can be generated known only to the sender and receiver. We describe how this novel technology and its several modalities could benefit the critical infrastructures of dams or hydropower facilities. The presented discussions may be viewed as a precursor to a quantum cybersecurity roadmap for the identification of relevant threats and mitigation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Optimized Lie–Trotter–Suzuki decompositions for two and three non-commuting terms

Lie–Trotter–Suzuki decompositions are an efficient way to approximate operator exponentials exp ( t H ) when H is a sum of n (non-commuting) terms which, individually, can be exponentiated easily. They are employed in time-evolution algorithms for tensor network states, digital quantum simulation protocols, path integral methods like quantum Monte Carlo, and splitting methods for symplectic integrators in classical Hamiltonian systems. Here, we provide optimized decompositions up to order t 6 . The leading error term is expanded in nested commutators (Hall bases) and we minimize the 1-norm of the coefficients. For n = 2 terms, several of the optima we find are close to those in McLachlan (1995). Generally, our results substantially improve over unoptimized decompositions by Forest, Ruth, Yoshida, and Suzuki. We explain why these decompositions are sufficient to efficiently simulate any one- or two-dimensional lattice model with finite-range interactions. This follows by solving a partitioning problem for the interaction graph.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Role of the junction voltage on the overflow current in light-emitting diodes

Quantum-well (QW)-based light emitters, such as light-emitting diodes (LEDs) and lasers, of various semiconductor materials experience a reduction in their efficiency when operating at higher temperatures, a phenomenon referred to as “thermal droop.” Among the various claims on the origins of thermal droop, an increased overflow current with increasing temperatures is a common contender. Since overflow of carriers can only occur when the junction voltage 𝑉 Junction approaches the built-in voltage 𝑉 BI of any diodes, we develop a simple method relating the difference between 𝑉 Junction and 𝑉 BI to approximate the upper limit of overflow occurring in QW-based light-emitting diodes. The measured difference between 𝑉 Junction and 𝑉 BI of state-of-the-art commercial blue and green In⁢Ga⁢N-based LEDs at temperatures up to ∼450 K suggests negligible overflow. To further experimentally verify the absence of overflow, we perform temperature-dependent electron emission spectroscopy on the same commercial blue and green LEDs and find no evidence of thermally enhanced overflow carriers up to ∼450 K. In agreement with our claims that 𝑉 Junction must approach 𝑉 BI for overflow to occur, two-dimensional temperature-dependent electrical simulations of violet, blue, and green LEDs including alloy disorder and V-defects demonstrate that overflow can be significant in violet LEDs, where the small band offset between the In⁢Ga⁢N QW and Ga⁢N cladding layers due to the larger QW bandgap requires larger 𝑉 Junction to reach standard operating current densities, thereby approaching 𝑉 BI . By contrast, simulations indicate that overflow is negligible in blue and green LEDs, whose smaller QW bandgaps result in smaller quasi-Fermi levels difference to reach significant carrier injection, resulting in a 𝑉 Junction much smaller than 𝑉 BI up to large operating current densities. Considering that overflow is negligible in blue and longer-wavelength LEDs, and our observations of the large thermal droop occurring at low current densities, where Shockley-Read-Hall (SRH) recombination dominates, we conclude that thermally enhanced SRH processes are the most significant contributor to thermal droop. Finally, we also simulate the carrier densities in the different QWs of a multiple-QW LED and observe a reduction in the total carrier density at a given operating current density, which results in a decrease in the total Auger-Meitner current of the LED from just the thermally enhanced carrier redistribution among QWs without taking any possible additional temperature dependence of their recombination coefficients. Taking all this into account, minimizing thermal droop effects in LEDs can be achieved by a reduction in defect density, using wider band gap p-n junction-defining cladding layers, and operating at higher currents.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Common workflows for computing material properties using different quantum engines

The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Effective Gaps Are Not Effective: Quasipolynomial Classical Simulation of Obstructed Stoquastic Hamiltonians

All known examples suggesting an exponential separation between classical simulation algorithms and stoquastic adiabatic quantum computing (StoqAQC) exploit symmetries that constrain adiabatic dynamics to effective, symmetric subspaces. The symmetries produce large effective eigenvalue gaps, which in turn make adiabatic computation efficient. We present a classical algorithm to subexponentially sample from an effective subspace of any k -local stoquastic Hamiltonian H , without a priori knowledge of its symmetries (or near symmetries). Our algorithm maps any k -local Hamiltonian to a graph G = ( V , E ) with | V | = O (poly (n)) , where n is the number of qubits. Given the well-known result of Babai [Graph isomorphism in quasipolynomial time, in Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing (2016), pp. 684–697], we exploit graph isomorphism to study the automorphisms of G and arrive at an algorithm quasipolynomial in | V | for producing samples from effective subspace eigenstates of H . Our results rule out exponential separations between StoqAQC and classical computation that arise from hidden symmetries in k -local Hamiltonians. Our graph representation of H is not limited to stoquastic Hamiltonians and may rule out corresponding obstructions in nonstoquastic cases, or be useful in studying additional properties of k -local Hamiltonians.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

On-Chip Single-Layer Integration of Diamond Spins with Microwave and Plasmonic Channels

On-chip scalable integration represents a major challenge for practical quantum devices. One particular challenge is to implement on-chip optical readout of spins in diamond. This readout requires simultaneous application of optical and microwave fields along with the efficient collection of fluorescence. These requirements are typically met using bulk optical components and dedicated microwave lines. Here, we experimentally demonstrate an on-chip integrated interface for diamond nitrogen vacancy (NV) spin-state preparation and readout, implemented in a planar single layer with only one patterning step. A nanodiamond with NV centers is positioned inside a v-groove waveguide milled in a thick gold film. The gold film carries the microwave control signal, while the v-groove acts as a fluorescence collector and, potentially, as an integrated pump filter. Hence, the device dimensions and fabrication complexity are substantially reduced. Our approach fosters further development of ultracompact nanoscale quantum sensors and quantum information processing devices on a monolithic platform.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Quantum Computing in Next-Generation Transportation Optimization

We explore how quantum computing (QC) can advance transportation optimization, with a focus on two high-impact areas: traffic signal control and vehicle electrification with grid integration. As transportation systems grow in complexity, classical optimization methods increasingly struggle to deliver scalable and efficient solutions, particularly for real-time, data-rich environments. This work identifies key challenges within these two domains where QC may offer advantages, particularly in handling combinatorial decision spaces and dynamic constraints. We begin by outlining the limitations of classical approaches for traffic signal control optimization and electric vehicle charging coordination, highlighting where computational limitations arise. Previous quantum formulations are presented and new formulations are proposed to demonstrate how emerging quantum algorithms, including quantum annealing and the Quantum Approximation Optimization Algorithm, could be leveraged to reformulate and address these problems. We also evaluate the suitability of current quantum hardware and discuss recent trends that indicate when QC may become a viable tool for transportation applications. While acknowledging the present limitations of QC technologies, this poster emphasizes the importance of preparing quantum-compatible models today. By reviewing and establishing formulations that align with the strengths of quantum algorithms, researchers and practitioners can better position themselves to take advantage of QC advancements as they occur. This work aims to provide a practical, forward-looking perspective on the near-term potential of quantum computing in transportation optimization.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Relativistic resolution-of-the-identity with Cholesky integral decomposition

In this study, we present an efficient integral decomposition approach called the restricted-kinetic-balance resolution-of-the-identity (RKB-RI) algorithm, which utilizes a tunable RI method based on the Cholesky integral decomposition for in-core relativistic quantum chemistry calculations. The RKB-RI algorithm incorporates the restricted-kinetic-balance condition and offers a versatile framework for accurate computations. Notably, the Cholesky integral decomposition is employed not only to approximate symmetric large-component electron repulsion integrals but also those involving small-component basis functions. In addition to comprehensive error analysis, we investigate crucial conditions, such as the kinetic balance condition and variational stability, which underlie the applicability of Dirac relativistic electronic structure theory. Here we compare the computational cost of the RKB-RI approach with the full in-core method to assess its efficiency. To evaluate the accuracy and reliability of the RKB-RI method proposed in this work, we employ actinyl oxides as benchmark systems, leveraging their properties for validation purposes. This investigation provides valuable insights into the capabilities and performance of the RKB-RI algorithm and establishes its potential as a powerful tool in the field of relativistic quantum chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Planar multifrequency mid-IR microwave-pumped lasers

The characteristics of planar repetitively pulsed diffusion-cooled HF, DF, HF – DF, Xe, and HF – DF – Xe(Kr) lasers pumped by a microwave discharge (2.45 GHz) are studied depending on the pump pulse duration (10 – 40 μs) and repletion rate (50 – 400 Hz), as well as on the composition and pressure of the working gas mixture (30 – 200 Torr) at low (to 0.6 L s{sup −1}) gas flow rates. It is demonstrated for the first time that a HF – DF – Xe gas-discharge laser may simultaneously operate as a HF – DF chemical molecular laser and a recombination laser based on xenon atom transitions and generate broadband (octave) radiation in the range 2.0 – 4.2 μm with an average output power of 43 mW and an efficiency of 0.9 %. With substitution of xenon for krypton, lasing in a HF – DF – Kr laser is obtained at wavelengths of 2.52 – 4.15 μm. Lasing in a HF – DF laser was achieved simultaneously in two spectral regions (2.7 – 2.9 and 3.6 – 4.2 μm) with an output power of ∼50 mW. It is found that a decrease in the pump pulse duration with a simultaneous increase in the pulse repletion rate for retaining the average energy deposition leads to an increase in the average output power and efficiency of the laser. At low pulse repetition rates (50 – 100 Hz) and a low gas mixture flow rate, helium buffer gas can be effectively substituted for neon. Operation of the Xe laser was achieved in the spectral range 2.03 – 3.65 μm with an average output power of 580 Mw at a pump pulse duration of 20 μs, a pulse repetition rate to 10 kHz, and a maximum efficiency of 55 %. The obtained experimental results demonstrate the possibility of creating broadband HF – DF – Xe lasers emitting in the frequency range 2 – 4 μm with a desired ratio of intensities in different spectral ranges. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Perturbative readout-error mitigation for near-term quantum computers

Readout errors on near-term quantum computers can introduce significant error to the empirical probability distribution sampled from the output of a quantum circuit. These errors can be mitigated by classical postprocessing given the access of an experimental response matrix that describes the error associated with the measurement of each computational basis state. However, the resources required to characterize a complete response matrix and to compute the corrected probability distribution scale exponentially with the number of qubits, n . In this work, we modify standard matrix inversion techniques using perturbative approximations with significantly reduced complexity and bounded error when the likelihood of high-order bit-flip events is strongly suppressed. Given a characteristic error rate q , we discuss a method to recover the probability of the all-zeros bit string p 0 by sampling only a small subspace of the response matrix before inverting readout error, resulting in a relative speedup of poly [ 2 n / ( n w ) ] , which we motivate using a simplified error model for which the approximation incurs only O ( q w ) error for some integer w . We then provide a generalized technique to efficiently recover full output distributions with O ( q w ) error in the perturbative limit. These approximate techniques for readout-error correction may greatly accelerate near-term quantum computing applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Molecular simulation and artificial intelligence for the circular economy of bioenergy and bioproducts

The concept of the circular bioeconomy is a carbon neutral, sustainable system with zero waste. One vision for such an economy is based upon lignocellulosic biomass. This lignocellulosic circular bioeconomy requires CO 2 absorption from biomass growth and the efficient deconstruction of recalcitrant biomass into solubilized and fractionated biopolymers, which are then used as precursors for the sustainable production of high-quality liquid fuels, chemical bioproducts, and bio-based materials. Here, in this study, we summarize the roles that molecular dynamics (MD) simulations and machine learning (ML) are playing in overcoming several fundamental challenges hindering the adoption of a circular bioeconomy. Specifically, we discuss the role of MD and ML/AI in overcoming lignocellulose recalcitrance by designing biomass pretreatment methods to efficiently produce solubilized cellulose/lignin/hemicellulose and of that in improving energy-intensive manufacturing of biomass-based materials and their structural and mechanical properties. Quantum mechanical methods and MD simulations, in addition to offering a mechanistic understanding of biomass deconstruction and biomaterials design, can provide meaningful structural, energetics, and physiochemical properties as inputs to train AI/ML models. The ML models can guide the experimental prioritization of materials/solvents and process parameters that significantly accelerate the development of biofuel and biomaterial components of the circular bioeconomy.

Smith, Jeremy C. [Oak Ridge National Laboratory (O↗

DOE OSTI2020

Model for the electrical conductivity in dense plasma mixtures

A new density functional theory, average atom based model for the electrical conductivity of dense plasmas with a mixture of ion species, containing no adjustable parameters, is presented herein. The model takes the temperature, mass density and relative abundances of the species as input. It takes into account partial ionization, ionic structure, and core-valence orthogonality, and uses quantum mechanical calculations of cross sections. Comparison to an existing high fidelity but computationally expensive method reveals good agreement. The new model is computationally efficient and can reach high temperatures. A new mixing rule is also presented that gives reasonably accurate conductivities for high temperature plasma mixtures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Thermoelectric current in a graphene Cooper pair splitter

Generation of electric voltage in a conductor by applying a temperature gradient is a fundamental phenomenon called the Seebeck effect. This effect and its inverse is widely exploited in diverse applications ranging from thermoelectric power generators to temperature sensing. Recently, a possibility of thermoelectricity arising from the interplay of the non-local Cooper pair splitting and the elastic co-tunneling in the hybrid normal metal-superconductor-normal metal structures was predicted. Here, we report the observation of the non-local Seebeck effect in a graphene-based Cooper pair splitting device comprising two quantum dots connected to an aluminum superconductor and present a theoretical description of this phenomenon. The observed non-local Seebeck effect offers an efficient tool for producing entangled electrons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Synergy of semiempirical models and machine learning in computational chemistry

Catalyzed by enormous success in the industrial sector, many research programs have been exploring data-driven, machine learning approaches. Performance can be poor when the model is extrapolated to new regions of chemical space, e.g., new bonding types, new many-body interactions. Another important limitation is the spatial locality assumption in model architecture, and this limitation cannot be overcome with larger or more diverse datasets. The outlined challenges are primarily associated with the lack of electronic structure information in surrogate models such as interatomic potentials. Given the fast development of machine learning and computational chemistry methods, we expect some limitations of surrogate models to be addressed in the near future; nevertheless spatial locality assumption will likely remain a limiting factor for their transferability. Here, we suggest focusing on an equally important effort—design of physics-informed models that leverage the domain knowledge and employ machine learning only as a corrective tool. In the context of material science, we will focus on semi-empirical quantum mechanics, using machine learning to predict corrections to the reduced-order Hamiltonian model parameters. The resulting models are broadly applicable, retain the speed of semiempirical chemistry, and frequently achieve accuracy on par with much more expensive ab initio calculations. These early results indicate that future work, in which machine learning and quantum chemistry methods are developed jointly, may provide the best of all worlds for chemistry applications that demand both high accuracy and high numerical efficiency.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Low-loss adiabatic fiber-optic coupler for cryogenic photonics

Recent developments in quantum light–matter coupled systems and quantum transducers have highlighted the need for cryogenic optical measurements. In this study, we present a packaged fiber-optic coupler with a coupling efficiency of over 50% for telecom wavelength light down to the mK temperature range. Besides the high coupling efficiency, our method enables sensitive photonic device measurements that are immune to mechanical vibrations present in cryogenic setups.

Optics↗