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Here, photoluminescence (PL) mapping was utilized to investigate damage in β-Ga₂O₃ epilayers induced by 1064 nm laser pulses. The intensity and position of the intrinsic UV band were determined and plotted as a false-color image. Two types of damage were identified: circular damage and damage cracks. Circular damage shows lower UV PL intensity than the surrounding material with color centers in a “halo” around the damaged region. Damage cracks are aligned with the a and c axes and show higher PL intensity than undamaged material. Defects in the as-grown material were revealed by shifts in the UV band energy.

Open Source Exascale Computational Chemistry Software

The rapid development of quantum computing (QC) in the NISQ era urgently demands a low-level benchmark suite and insightful evaluation metrics for characterizing the properties of prototype NISQ devices, the efficiency of QC programming compilers, schedulers and assemblers, and the capability of quantum system simulators in a classical computer. In this work, we fill this gap by proposing a low-level, easy-to-use benchmark suite called QASMBench based on the OpenQASM assembly representation. It consolidates commonly used quantum routines and kernels from a variety of domains including chemistry, simulation, linear algebra, searching, optimization, arithmetic, machine learning, fault tolerance, cryptography, and so on, trading-off between generality and usability. To analyze these kernels in terms of NISQ device execution, in addition to circuit width and depth, we propose four circuit metrics including gate density, retention lifespan, measurement density, and entanglement variance, to extract more insights about the execution efficiency, the susceptibility to NISQ error, and the potential gain from machine-specific optimizations. Applications in QASMBench can be launched and verified on several NISQ platforms, including IBM-Q, Rigetti, IonQ and Quantinuum. For evaluation, we measure the execution fidelity of a subset of QASMBench applications on 12 IBM-Q machines through density matrix state tomography, comprising 25K circuit evaluations. In addition we also compare the fidelity of executions among the IBM-Q machines, the IonQ QPU and the Rigetti Aspen M-1 system.

Early but promising results in quantum computing have been enabled by the concurrent development of quantum algorithms, devices, and materials. Classical simulation of quantum programs has enabled the design and analysis of algorithms and implementation strategies targeting current and anticipated quantum device architectures. In this paper, we present a graph-based approach to achieve efficient quantum circuit simulation. Our approach involves partitioning the graph representation of a given quantum circuit into sub-graphs/circuits that exhibit better data locality. Simulation of each sub-circuit is organized hierarchically, with the iterative construction and simulation of smaller state vectors, improving overall performance. Also, this partitioning reduces the number of passes through data, improving the total computation time. We present three partitioning strategies and observe that acyclic graph partitioning typically results in the best time-to-solution. In contrast, other strategies reduce the partitioning time at the expense of potentially increased simulation times. Experimental evaluation demonstrates the effectiveness of our approach.

Green's Function Coupled Cluster Library SPEC GFCC library enables calculations for spectral functions and binding energies of molecular systems using highly scalable implementation of the Green's function coupled cluster (GFCC) approach

Tensor algebra operations such as contractions in computational chemistry consume a significant fraction of the computing time on large-scale computing platforms. The widespread use of tensor contractions between large multi-dimensional tensors in describing electronic structure theory has motivated the development of multiple tensor algebra frameworks targeting heterogeneous computing platforms. In this paper, we present Tensor Algebra for Many-body Methods (TAMM), a framework for productive and performance-portable development of scalable computational chemistry methods. TAMM decouples the specification of the computation from the execution of these operations on available high-performance computing systems. With this design choice, the scientific application developers (domain scientists) can focus on the algorithmic requirements using the tensor algebra interface provided by TAMM, whereas high-performance computing developers can direct their attention to various optimizations on the underlying constructs, such as efficient data distribution, optimized scheduling algorithms, and efficient use of intra-node resources (e.g., graphics processing units). The modular structure of TAMM allows it to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to the sustainable development of scalable ground- and excited-state electronic structure methods. We present case studies highlighting the ease of use, including the performance and productivity gains compared to other frameworks.

While quantum algorithms for simulation exhibit better asymptotic scaling than their classical counterparts, they currently cannot be implemented on real-world devices. Instead, chemists and computer scientists rely on costly classical simulations of these quantum algorithms. In particular, the quantum phase estimation (QPE) algorithm is among several approaches that have attracted much attention in recent years for its genuine quantum character. However, it is memory-intensive to simulate and intractable for moderate system sizes. Herein, this paper discusses the performance and applicability of QPESIM, a new simulation of the QPE algorithm designed to take advantage of modest computational resources. In particular, we demonstrate the versatility of QPESIM in simulating various electronic states by examining the ground and core-level states of H₂O. For these states, we also discuss the effect of the active-space size on the quality of the calculated energies. For the high-energy core-level states, we demonstrate that new QPE simulations for active spaces defined by 15 active orbitals significantly reduce the errors in core-level excitation energies compared to earlier QPE simulations using smaller active spaces.

We demonstrate a new substrate cleaning and buffer growth scheme in β-Ga ₂ O ₃ epitaxial thin films using metal–organic vapor phase epitaxy (MOVPE). For the channel structure, a low-temperature (LT, 600 °C) un-doped Ga ₂ O ₃ buffer was grown, followed by a transition layer to a high-temperature (HT, 810 °C) Si-doped Ga ₂ O ₃ channel layers without growth interruption. The (010) Ga ₂ O ₃ Fe-doped substrate cleaning uses solvent cleaning, followed by additional hydrofluoric acid (49% in water) treatment for 30 min before the epilayer growth. This step is shown to compensate the parasitic Si channel at the epilayer–substrate interface that originates from the substrate polishing process or contamination from the ambient. From secondary ion mass spectroscopy (SIMS) analysis, the Si peak atomic density at the substrate interface is found to be several times lower than the Fe atomic density in the substrate—indicating full compensation. The elimination of the parasitic electron channel at the epi–substrate interface was also verified by electrical (capacitance–voltage profiling) measurements. In the LT-grown (600 °C) buffer layers, it is seen that the Fe forward decay tail from the substrate is very sharp, with a decay rate of ∼9 nm/dec. X-ray off-axis rocking curve ω-scans show very narrow full width at half maximum (FWHM) values, similar to the as-received substrates. These channels show record high electron mobility in the range of 196–85 cm ² /V⋅s in unintentionally doped and Si-doped films in the doping range of 2 × 10 ¹⁶ –1 × 10 ²⁰  cm ⁻³ . Si delta-doped channels were also grown utilizing this substrate cleaning and the hybrid LT buffers. Record high electron Hall mobility of 110 cm ² /V⋅s was measured for sheet charge density of 9.2 × 10 ¹²  cm ⁻² . This substrate cleaning, combined with the LT buffer scheme, shows the potential of designing Si-doped β-Ga ₂ O ₃ channels with exceptional transport properties for high-performance Ga ₂ O ₃ -based electron devices.

The computational power increases over the past decades have greatly enhanced the ability to simulate chemical reactions and understand ever more complex transformations. Tensor contractions are the fundamental computational building block of these simulations. These simulations have often been tied to one platform and restricted in generality by the interface provided to the user. The expanding prevalence of accelerators and researcher demands necessitate a more general approach which is not tied to specific hardware or requires contortion of algorithms to specific hardware platforms. In this paper we present COMET, a domain-specific programming language and compiler infrastructure for tensor contractions targeting heterogeneous accelerators. We present a system of progressive lowering through multiple layers of abstraction and optimization that achieves up to 1.98×speedup for 30 tensor contractions commonly used in computational chemistry and beyond.

NWQSim is a quantum circuit simulation environment developed at PNNL. It currently includes two major components: a state-vector simulator (SV-Sim) and a density matrix simulator (DM-Sim) and we may add more components, such as a Clifford simulator, in the future effort. NWQSim has two language interface: C/C++ and Python. It supports Q#/QDK frontend through QIR and QIR-runtime. It supports Qiskit and Cirq frontends through OpenQASM. NWQSim runs on several backends: Intel-CPU, Intel-Xeon-Phi, AMD-CPU, AMD-GPU, NVIDIA-GPU, and IBM-CPU. It supports three modes: (1) single processor, such as a single CPU (with and without AVX2 and AVX512 acceleration), a single NVIDIA GPU or a single AMD GPU; (2) single-node-multi-processors, such as multi-CPUs/Xeon-Phis, multi-NVIDA/AMD GPUs; (3) multi-nodes, such as a CPU cluster, a Xeon-Phi cluster (e.g., ANL Theta, NERSC Cori), an NVIDIA cluster (e.g., ORNL Summit, NERSC Perlmutter).