The Experts below are selected from a list of 35853 Experts worldwide ranked by ideXlab platform

Alexander J Mccaskey - One of the best experts on this subject based on the ideXlab platform.

  • qcor a language extension specification for the heterogeneous quantum Classical model of computation
    ACM Journal on Emerging Technologies in Computing Systems, 2020
    Co-Authors: Tiffany M Mintz, Alexander J Mccaskey, Eugene F Dumitrescu, Shirley Moore, Sarah Powers, Pavel Lougovski
    Abstract:

    Quantum computing (QC) is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing Programming models for QC were designed with fault-tolerant hardware in mind, envisioning stand-alone applications. However, the susceptibility of near-term quantum computers to noise limits their stand-alone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their Classical counterparts in a heterogeneous fashion. This modus operandi calls out for a Programming model and a high-level Programming language that natively and seamlessly supports heterogeneous quantum-Classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called QCOR, which enables single-source quantum-Classical Programming. Programs written using the QCOR library–based language extensions can be compiled to produce functional hybrid binary executables. After defining QCOR’s Programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed-forward QC. QCOR approaches quantum-Classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high-performance computing. The high level of abstraction in the language extension is intended to accelerate the adoption of QC by researchers familiar with Classical high-performance computing.

  • qcor a language extension specification for the heterogeneous quantum Classical model of computation
    arXiv: Programming Languages, 2019
    Co-Authors: Tiffany M Mintz, Alexander J Mccaskey, Eugene F Dumitrescu, Shirley Moore, Sarah Powers, Pavel Lougovski
    Abstract:

    Quantum computing is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing Programming models for quantum computing were designed with fault-tolerant hardware in mind, envisioning standalone applications. However, near-term quantum computers are susceptible to noise which limits their standalone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their Classical counterparts in a heterogeneous fashion. This {\it modus operandi} calls out for a Programming model and a high-level Programming language that natively and seamlessly supports heterogeneous quantum-Classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called QCOR, that enables single-source quantum-Classical Programming. Programs written using the QCOR library and directives based language extensions can be compiled to produce functional hybrid binary executables. After defining the QCOR's Programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed forward quantum computing. QCOR approaches quantum-Classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high performance computing (HPC). The high level of abstraction in the developed language is intended to accelerate the adoption of quantum computing by researchers familiar with Classical HPC.

  • validating quantum Classical Programming models with tensor network simulations
    PLOS ONE, 2018
    Co-Authors: Alexander J Mccaskey, Eugene F Dumitrescu, Mengsu Chen, Dmitry I Lyakh, Travis S Humble
    Abstract:

    The exploration of hybrid quantum-Classical algorithms and Programming models on noisy near-term quantum hardware has begun. As hybrid programs scale towards Classical intractability, validation and benchmarking are critical to understanding the utility of the hybrid computational model. In this paper, we demonstrate a newly developed quantum circuit simulator based on tensor network theory that enables intermediate-scale verification and validation of hybrid quantum-Classical computing frameworks and Programming models. We present our tensor-network quantum virtual machine (TNQVM) simulator which stores a multi-qubit wavefunction in a compressed (factorized) form as a matrix product state, thus enabling single-node simulations of larger qubit registers, as compared to brute-force state-vector simulators. Our simulator is designed to be extensible in both the tensor network form and the Classical hardware used to run the simulation (multicore, GPU, distributed). The extensibility of the TNQVM simulator with respect to the simulation hardware type is achieved via a pluggable interface for different numerical backends (e.g., ITensor and ExaTENSOR numerical libraries). We demonstrate the utility of our TNQVM quantum circuit simulator through the verification of randomized quantum circuits and the variational quantum eigensolver algorithm, both expressed within the eXtreme-scale ACCelerator (XACC) Programming model.

Eugene F Dumitrescu - One of the best experts on this subject based on the ideXlab platform.

  • qcor a language extension specification for the heterogeneous quantum Classical model of computation
    ACM Journal on Emerging Technologies in Computing Systems, 2020
    Co-Authors: Tiffany M Mintz, Alexander J Mccaskey, Eugene F Dumitrescu, Shirley Moore, Sarah Powers, Pavel Lougovski
    Abstract:

    Quantum computing (QC) is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing Programming models for QC were designed with fault-tolerant hardware in mind, envisioning stand-alone applications. However, the susceptibility of near-term quantum computers to noise limits their stand-alone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their Classical counterparts in a heterogeneous fashion. This modus operandi calls out for a Programming model and a high-level Programming language that natively and seamlessly supports heterogeneous quantum-Classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called QCOR, which enables single-source quantum-Classical Programming. Programs written using the QCOR library–based language extensions can be compiled to produce functional hybrid binary executables. After defining QCOR’s Programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed-forward QC. QCOR approaches quantum-Classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high-performance computing. The high level of abstraction in the language extension is intended to accelerate the adoption of QC by researchers familiar with Classical high-performance computing.

  • qcor a language extension specification for the heterogeneous quantum Classical model of computation
    arXiv: Programming Languages, 2019
    Co-Authors: Tiffany M Mintz, Alexander J Mccaskey, Eugene F Dumitrescu, Shirley Moore, Sarah Powers, Pavel Lougovski
    Abstract:

    Quantum computing is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing Programming models for quantum computing were designed with fault-tolerant hardware in mind, envisioning standalone applications. However, near-term quantum computers are susceptible to noise which limits their standalone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their Classical counterparts in a heterogeneous fashion. This {\it modus operandi} calls out for a Programming model and a high-level Programming language that natively and seamlessly supports heterogeneous quantum-Classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called QCOR, that enables single-source quantum-Classical Programming. Programs written using the QCOR library and directives based language extensions can be compiled to produce functional hybrid binary executables. After defining the QCOR's Programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed forward quantum computing. QCOR approaches quantum-Classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high performance computing (HPC). The high level of abstraction in the developed language is intended to accelerate the adoption of quantum computing by researchers familiar with Classical HPC.

  • validating quantum Classical Programming models with tensor network simulations
    PLOS ONE, 2018
    Co-Authors: Alexander J Mccaskey, Eugene F Dumitrescu, Mengsu Chen, Dmitry I Lyakh, Travis S Humble
    Abstract:

    The exploration of hybrid quantum-Classical algorithms and Programming models on noisy near-term quantum hardware has begun. As hybrid programs scale towards Classical intractability, validation and benchmarking are critical to understanding the utility of the hybrid computational model. In this paper, we demonstrate a newly developed quantum circuit simulator based on tensor network theory that enables intermediate-scale verification and validation of hybrid quantum-Classical computing frameworks and Programming models. We present our tensor-network quantum virtual machine (TNQVM) simulator which stores a multi-qubit wavefunction in a compressed (factorized) form as a matrix product state, thus enabling single-node simulations of larger qubit registers, as compared to brute-force state-vector simulators. Our simulator is designed to be extensible in both the tensor network form and the Classical hardware used to run the simulation (multicore, GPU, distributed). The extensibility of the TNQVM simulator with respect to the simulation hardware type is achieved via a pluggable interface for different numerical backends (e.g., ITensor and ExaTENSOR numerical libraries). We demonstrate the utility of our TNQVM quantum circuit simulator through the verification of randomized quantum circuits and the variational quantum eigensolver algorithm, both expressed within the eXtreme-scale ACCelerator (XACC) Programming model.

Travis S Humble - One of the best experts on this subject based on the ideXlab platform.

  • validating quantum Classical Programming models with tensor network simulations
    PLOS ONE, 2018
    Co-Authors: Alexander J Mccaskey, Eugene F Dumitrescu, Mengsu Chen, Dmitry I Lyakh, Travis S Humble
    Abstract:

    The exploration of hybrid quantum-Classical algorithms and Programming models on noisy near-term quantum hardware has begun. As hybrid programs scale towards Classical intractability, validation and benchmarking are critical to understanding the utility of the hybrid computational model. In this paper, we demonstrate a newly developed quantum circuit simulator based on tensor network theory that enables intermediate-scale verification and validation of hybrid quantum-Classical computing frameworks and Programming models. We present our tensor-network quantum virtual machine (TNQVM) simulator which stores a multi-qubit wavefunction in a compressed (factorized) form as a matrix product state, thus enabling single-node simulations of larger qubit registers, as compared to brute-force state-vector simulators. Our simulator is designed to be extensible in both the tensor network form and the Classical hardware used to run the simulation (multicore, GPU, distributed). The extensibility of the TNQVM simulator with respect to the simulation hardware type is achieved via a pluggable interface for different numerical backends (e.g., ITensor and ExaTENSOR numerical libraries). We demonstrate the utility of our TNQVM quantum circuit simulator through the verification of randomized quantum circuits and the variational quantum eigensolver algorithm, both expressed within the eXtreme-scale ACCelerator (XACC) Programming model.

Amaury Pouly - One of the best experts on this subject based on the ideXlab platform.

  • strong turing completeness of continuous chemical reaction networks and compilation of mixed analog digital programs
    Computational Methods in Systems Biology, 2017
    Co-Authors: Francois Fages, Guillaume Le Guludec, Olivier Bournez, Amaury Pouly
    Abstract:

    When seeking to understand how computation is carried out in the cell to maintain itself in its environment, process signals and make decisions, the continuous nature of protein interaction processes forces us to consider also analog computation models and mixed analog-digital computation programs. However, recent results in the theory of analog computability and complexity establish fundamental links with Classical Programming. In this paper, we derive from these results the strong (uniform computability) Turing completeness of chemical reaction networks over a finite set of molecular species under the differential semantics , solving a long standing open problem. Furthermore we derive from the proof a compiler of mathematical functions into elementary chemical reactions. We illustrate the reaction code generated by our compiler on trigonometric functions, and on various sigmoid functions which can serve as markers of presence or absence for implementing program control instructions in the cell and imperative programs. Then we start comparing our compiler-generated circuits to the natural circuit of the MAPK signaling network, which plays the role of an analog-digital converter in the cell with a Hill type sigmoid input/output functions.

Pouly Amaury - One of the best experts on this subject based on the ideXlab platform.

  • Strong Turing Completeness of Continuous Chemical Reaction Networks and Compilation of Mixed Analog-Digital Programs
    HAL CCSD, 2017
    Co-Authors: Fages François, Le Guludec Guillaume, Bournez Olivier, Pouly Amaury
    Abstract:

    Best paper awardInternational audienceWhen seeking to understand how computation is carried out in the cell to maintain itself in its environment, process signals and make decisions, the continuous nature of protein interaction processes forces us to consider also analog computation models and mixed analog-digital computation programs. However, recent results in the theory of analog computability and complexity establish fundamental links with Classical Programming. In this paper, we derive from these results the strong (uniform computability) Turing completeness of chemical reaction networks over a finite set of molecular species under the differential semantics , solving a long standing open problem. Furthermore we derive from the proof a compiler of mathematical functions into elementary chemical reactions. We illustrate the reaction code generated by our compiler on trigonometric functions, and on various sigmoid functions which can serve as markers of presence or absence for implementing program control instructions in the cell and imperative programs. Then we start comparing our compiler-generated circuits to the natural circuit of the MAPK signaling network, which plays the role of an analog-digital converter in the cell with a Hill type sigmoid input/output functions