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At least 19 records

Tough Errors Are no Match (TEAM): Optimizing the quantum compiler for noise resilience

This report summarizes Unitary Fund’s contributions to the Department of Energy’s TEAM project (DE-SC0020266) under Thrust 2: Quantum Programming and Compilation. The central outcomes of this work have been the development of Mitiq, an open-source Python toolkit for applying quantum error mitigation (QEM) techniques to noisy quantum programs, and the invention, benchmarking and theoretical investigation of novel QEM techniques. Additional outcomes include the development of other open source software packages for the usage, simulation and control of quantum computers.

97 MATHEMATICS AND COMPUTING

Tough Errors are no Match (TEAM): Optimizing the Quantum Compiler for Noise Resilience

This report summarizes our contributions to the Department of Energy’s Tough Errors are no Match (TEAM) project (DE-SC0020377) under Thrust 2: Quantum Programming and Compilation. The central outcomes of this work included a novel efficient quantum compiling algorithm which works without requiring the quantum computer to exactly invert its operations, answering a longstanding open problem in quantum compiling. Additional results include the implementation of zero-noise extrapolation error mitigation in collaboration with the Unitary Fund, as well as novel quantum algorithms for entanglement detection and pseudorandomness.

Bouland, Adam [Stanford Univ., CA (United States)]

ASDF: A Compiler for Qwerty, a Basis-Oriented Quantum Programming Language

Qwerty is a high-level quantum programming language built on bases and functions rather than circuits. This new paradigm introduces new challenges in compilation, namely synthesizing circuits from basis translations and automatically specializing adjoint or predicated forms of functions. This paper presents ASDF, an open-source compiler for Qwerty that answers these challenges in compiling basis-oriented languages. Enabled with a novel high-level quantum IR implemented in the MLIR framework, our compiler produces OpenQASM 3 or QIR for either simulation or execution on hardware. Our compiler is evaluated by comparing the fault-tolerant resource requirements of generated circuits with other compilers, finding that ASDF produces circuits with comparable cost to prior circuit-oriented compilers.

Adams, Austin J [Georgia Tech]

Can Large Language Models Understand Intermediate Representations?

Intermediate Representations (IRs) are essential in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored. This paper presents a pioneering empirical study to investigate the capabilities of LLMs, including GPT-4, GPT-3, Gemma 2, LLaMA 3.1, and Code Llama, in understanding IRs. We analyze their performance across four tasks: Control Flow Graph (CFG) reconstruction, decompilation, code summarization, and execution reasoning. Our results indicate that while LLMs demonstrate competence in parsing IR syntax and recognizing high-level structures, they struggle with control flow reasoning, execution semantics, and loop handling. Specifically, they often misinterpret branching instructions, omit critical IR operations, and rely on heuristic-based reasoning, leading to errors in CFG reconstruction, IR decompilation, and execution reasoning. The study underscores the necessity for IR-specific enhancements in LLMs, recommending fine-tuning on structured IR datasets and integration of explicit control flow models to augment their comprehension and handling of IR-related tasks.

Jiang, Hailong

Blueprint for DOE Quantum Supercomputing: Ensuring U.S. Leadership in the Quantum Decade

Quantum computing stands at the threshold of a transformative decade, where the field will evolve from small-scale demonstrations toward practical scientific computing at scale. This Blueprint identifies fault-tolerant quantum computers (FTQCs) as a viable, scalable, and broadly applicable path to achieving “quantum scientific utility,” defined as solving scientifically valuable problems beyond the reach of conventional, classical computers. This capability is expected to show scientific demonstrations in the late 2020s and to mature in the early-to-mid 2030s. This Blueprint outlines a strategy to prepare the U.S. Department of Energy (DOE) for FTQCs and their integration into the U.S. national scientific computing infrastructure. Its purpose is to identify the steps, milestones, and research directions necessary for DOE to enable initial deployment of FTQCs in 2028 as a scientific tool for the nation and mature this capability into the 2030s. DOE has a long history of supporting quantum information science and technology, contributing significantly to research advancements, training a quantum-ready workforce, and providing access to early small-scale quantum hardware. Given recent demonstrations of logical operations on error-corrected logical qubits and the advancement of commercial hardware roadmaps, DOE should begin preparations for large-scale, fault-tolerant quantum computing deployment for DOE science missions. This Blueprint proposes that DOE focus on (1) deploying first-generation scientifically relevant quantum computers with at least 100 logical qubits and performing at least 10,000 to 100,000 hard logical operations in scientifically relevant calculations; (2) developing essential FTQC programming competencies, system software, and facility readiness; and (3) investing in cutting edge focused R&D that fosters breakthroughs in scientific applications, algorithms, and logical architectures needed to accelerate the advent of scientific utility. This effort will position DOE to transition to larger systems: production-scale quantum computers that comprise 1,000 to 10,000 logical qubits, perform 1 to 10 billion hard logical operations, and execute scientifically useful computations at scale. Achieving these goals will require DOE facilities to evolve with urgency to support scientific campaigns that integrate quantum and classical computing resources into efficient workflows, novel software and firmware environments for compiling and routing quantum programs on FTQC machines, and suitable infrastructure for quantum hardware. It will also require further development and optimization of scientific applications from the fields of materials science, quantum chemistry, and high-energy and nuclear physics. The Blueprint calls for transformative R&D and collective action to accelerate the advent of scientific quantum utility and bring it within reach by 2028.

97 MATHEMATICS AND COMPUTING

Mojo: MLIR-based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem

We explore the performance and portability of the novel Mojo language for scientific computing workloads on GPUs. As the first language based on the LLVM’s Multi-Level Intermediate Representation (MLIR) compiler infrastructure, Mojo aims to close performance and productivity gaps by combining Python’s interoperability and CUDA-like syntax for compile-time portable GPU programming. We target four scientific workloads: a seven-point stencil (memory-bound), BabelStream (memory-bound), miniBUDE (compute-bound), and Hartree–Fock (compute-bound with atomic operations); and compare their performance against vendor baselines on NVIDIA H100 and AMD MI300A GPUs. We show that Mojo’s performance is competitive with CUDA and HIP for memory-bound kernels, whereas gaps exist on AMD GPUs for atomic operations and for fast-math compute-bound kernels on both AMD and NVIDIA GPUs. Although the learning curve and programming requirements are still fairly low-level, Mojo can close significant gaps in the fragmented Python ecosystem in the convergence of scientific computing and AI.

Godoy, William [ORNL] (ORCID:0000000225905178)

ROSE

Developed at Lawrence Livermore National Laboratory (LLNL), ROSE is an open source compiler infrastructure to build source-to-source program transformation and analysis tools for large-scale C (C89 to C23), C++ (C++98 to C++23), UPC, Fortran (Fortran4, 66, 77, 95, 2003), OpenMP, Java, Python, and Binary applications. ROSE users range from experienced compiler researchers to library and tool developers who may have minimal compiler experience. ROSE is particularly well suited for building custom tools for static analysis, program optimization, arbitrary program transformation, domain-specific optimizations, complex loop optimizations, performance analysis, and cyber-security. ROSE is: A library (and set of associated tools) to quickly and easily apply compiler techniques to one's code in order to improve application performance and developer productivity. A research and development compiler infrastructure for for writing custom source-to-source translators to perform source code transformations, analysis, and optimizations. Is

Pinnow, NathanT [Lawrence Livermore National Labor

JuTrack: A Julia package for auto-differentiable accelerator modeling and particle tracking

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. Here we demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

43 PARTICLE ACCELERATORS

Tough Errors Are no Match (TEAM): Optimizing the Quantum Compiler for Noise Resilience

This report summarizes research performed under the Tough Errors Are no Match (TEAM) project. The primary focus of TEAM has been to research and develop a compilation toolbox leveraging techniques from quantum characterization and control, probabilistic programming, and approximate computing. Our goal was to develop robust protocols that can be integrated into quantum compilers to optimize and enhance the robustness of noisy computation. Here, we provide a summary of TEAM work focused on characterization and control of quantum systems.

97 MATHEMATICS AND COMPUTING

LLM Benchmarking with LLaMA2: Evaluating Code Development Performance Across Multiple Programming Languages

The rapid evolution of large language models (LLMs) has opened new possibilities for automating various tasks in software development. This paper evaluates the capabilities of the LLaMA 2-70B model in automating these tasks for scientific applications written in commonly used programming languages. Using representative test problems, we assess the model's capacity to generate code, documentation, and unit tests, as well as its ability to translate existing code between commonly used programming languages. Our comprehensive analysis evaluates the compilation, runtime behavior, and correctness of the generated and translated code. Additionally, we assess the quality of automatically generated code, documentation, and unit tests. Here, our results indicate that while LLaMA 2-70B frequently generates syntactically correct and functional code for simpler numerical tasks, it encounters substantial difficulties with more complex, parallelized, or distributed computations, requiring considerable manual corrections. We identify key limitations and suggest areas for future improvements to better leverage AI-driven automation in scientific computing workflows.

97 MATHEMATICS AND COMPUTING

Tough Errors are no Match (TEAM): Optimizing the Quantum Compiler for Noise Resilience

This project builds toward a comprehensive error-mitigating toolkit that makes quantum programming more robust and adaptive to the noisy, resource-limited nature of today’s quantum hardware. To that end, it integrates established error-mitigation methods — such as zero-noise extrapolation and dynamical decoupling — directly into compiler infrastructures. These techniques will be packaged as modules that can automatically adjust and combine based on performance analysis, enabling compilers to explore large design spaces and produce optimized, low-noise quantum programs with minimal manual intervention. In parallel, this project also explores new approaches to analog quantum programming or quantum simulation, and has developed the programming language SimuQ which treats quantum Hamiltonian evolution as the central object.

97 MATHEMATICS AND COMPUTING

Oak Ridge National Laboratory Modernizing the Kokkos Build System: Using CMake to Encapsulate the Complexity of Build Instructions for Performance Portable Libraries

Kokkos, a C++ library focused on performance portability, requires a build system that can work with a variety of compilers and hardware. Ideally, users need only select the compiler and architecture and should not have to know or specify how programs using Kokkos are built. CMake can be used to create a flexible, robust build system and automatically configures compilers and settings based on the user’s inputs. Nevertheless, Kokkos’ requirements as a performance portability library for the build system exceed CMake’s current capabilities. This report describes the requirements, solutions, and testing of various implementations to create a CMake-based build system suitable for Kokkos. It compares the strengths and shortcomings of the approaches and evaluates the implementations with respect to the requirements. Because no solution was found to meet all of the requirements, the Kokkos team engaged with the CMake development team to discuss and plan a path toward support for performance-portable build systems in CMake in the future.

97 MATHEMATICS AND COMPUTING

2009 National Household Travel Survey - California Add-On

The California add-on survey supplements the 2009 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The NHTS was conducted by the Federal Highway Administration, which collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The California Department of Transportation participated in the NHTS add-on program and received a total of 21,225 household samples in California, from March 2008 through May 2009.

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2017 National Household Travel Survey - California Add-On

The California add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The NHTS was conducted by the Federal Highway Administration, which collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The California Department of Transportation participated in the NHTS add-on program and received a total of 26,095 household samples in California, from April 2016 through April 2017.

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2001 National Household Travel Survey - Wisconsin Add-On

The Wisconsin add-on survey supplements the 2001 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The Federal Highway Administration and Wisconsin Department of Transportation conducted the survey. The NHTS ran from March 2001 through May 2002, with data collection for the Wisconsin add-on occurring May 1, 2001, to May 6, 2002. The NHTS collected detailed information on travel behavior nationwide, as well as data on the demographic and socioeconomic composition of households. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Wisconsin Department of Transportation participated in the NHTS add-on program and received a total of 17,610 household samples in Wisconsin. (The total number of household samples nationwide is 26,038.)

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2009 National Household Travel Survey - Wisconsin Add-On

The Wisconsin add-on survey supplements the 2009 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The Federal Highway Administration and Wisconsin Department of Transportation conducted the survey from March 2008 through May 2009. It collected detailed information on travel behavior nationwide, as well as data on the demographic and socioeconomic composition of households. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Wisconsin Department of Transportation participated in the NHTS add-on program and received a total of 1,707 household samples in Wisconsin. (The total number of household samples nationwide is 26,000.)

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2017 National Household Travel Survey - Wisconsin Add-On

The Wisconsin add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The Federal Highway Administration and Wisconsin Department of Transportation conducted the survey. The NHTS ran from April 19, 2016, through April 25, 2017. It collected detailed information on travel behavior nationwide, as well as data on the demographic and socioeconomic composition of households. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Wisconsin Department of Transportation participated in the NHTS add-on program and received a total of 11,675 household samples in Wisconsin. (The total number of household samples nationwide is 129,112.)

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2017 National Household Travel Survey—Georgia Add-On

The National Household Travel Survey (NHTS) ran from April 19, 2016, through April 25, 2017. It collected data on the demographic and socioeconomic composition of households, as well as detailed information on travel behavior nationwide. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Georgia Department of Transportation participated in the NHTS add-on program and received a total of 8,631 household samples in Georgia. (The total number of household samples nationwide is 129,112.)

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