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Vladimir Zamdzhiev - One of the best experts on this subject based on the ideXlab platform.

  • Quantum Programming with Inductive Datatypes
    2020
    Co-Authors: Romain Péchoux, Simon Perdrix, Mathys Rennela, Vladimir Zamdzhiev
    Abstract:

    Inductive datatypes in programming languages allow users to define useful data structures such as natural numbers, lists, trees, and others. In this paper we show how inductive datatypes may be added to the quantum programming language QPL. We construct a sound Categorical Model for the language and by doing so we provide the first detailed semantic treatment of user-defined inductive datatypes in quantum programming. We also show our denotational interpretation is invariant with respect to big-step reduction, thereby establishing another novel result for quantum programming. Compared to classical programming, this property is considerably more difficult to prove and we demonstrate its usefulness by showing how it immediately implies computational adequacy at all types. To further cement our results, our semantics is entirely based on a physically natural Model of von Neumann algebras, which are mathematical structures used by physicists to study quantum mechanics.

  • Quantum Programming with Inductive Datatypes: Causality and Affine Type Theory
    2020
    Co-Authors: Romain Péchoux, Simon Perdrix, Mathys Rennela, Vladimir Zamdzhiev
    Abstract:

    Inductive datatypes in programming languages allow users to define useful data structures such as natural numbers, lists, trees, and others. In this paper we show how inductive datatypes may be added to the quantum programming language QPL. We construct a sound Categorical Model for the language and by doing so we provide the first detailed semantic treatment of user-defined inductive datatypes in quantum programming. We also show our denotational interpretation is invariant with respect to big-step reduction, thereby establishing another novel result for quantum programming. Compared to classical programming, this property is considerably more difficult to prove and we demonstrate its usefulness by showing how it immediately implies computational adequacy at all types. To further cement our results, our semantics is entirely based on a physically natural Model of von Neumann algebras, which are mathematical structures used by physicists to study quantum mechanics.

  • FoSSaCS - Quantum Programming with Inductive Datatypes: Causality and Affine Type Theory
    Lecture Notes in Computer Science, 2020
    Co-Authors: Romain Péchoux, Simon Perdrix, Mathys Rennela, Vladimir Zamdzhiev
    Abstract:

    Inductive datatypes in programming languages allow users to define useful data structures such as natural numbers, lists, trees, and others. In this paper we show how inductive datatypes may be added to the quantum programming language QPL. We construct a sound Categorical Model for the language and by doing so we provide the first detailed semantic treatment of user-defined inductive datatypes in quantum programming. We also show our denotational interpretation is invariant with respect to big-step reduction, thereby establishing another novel result for quantum programming. Compared to classical programming, this property is considerably more difficult to prove and we demonstrate its usefulness by showing how it immediately implies computational adequacy at all types. To further cement our results, our semantics is entirely based on a physically natural Model of von Neumann algebras, which are mathematical structures used by physicists to study quantum mechanics.

Takeshi Tsukada - One of the best experts on this subject based on the ideXlab platform.

  • a Categorical Model of an mathbf i o typed pi calculus
    European Symposium on Programming, 2019
    Co-Authors: Ken Sakayori, Takeshi Tsukada
    Abstract:

    This paper introduces a new Categorical structure that is a Model of a variant of the \( \mathbf {i/o} \)-typed \( \pi \)-calculus, in the same way that a cartesian closed category is a Model of the \( \lambda \)-calculus. To the best of our knowledge, no Categorical Model has been given for the \( \mathbf {i/o} \)-typed \( \pi \)-calculus, in contrast to session-typed calculi, to which corresponding logic and Categorical structure were given. The Categorical structure introduced in this paper has a simple definition, combining two well-known structures, namely, closed Freyd category and compact closed category. The former is a Model of effectful computation in a general setting, and the latter describes connections via channels, which cause the effect we focus on in this paper. To demonstrate the relevance of the Categorical Model, we show by a semantic consideration that the \( \pi \)-calculus is equivalent to a core calculus of Concurrent ML.

  • ESOP - A Categorical Model of an \(\mathbf {i/o}\)-typed \(\pi \)-calculus
    Programming Languages and Systems, 2019
    Co-Authors: Ken Sakayori, Takeshi Tsukada
    Abstract:

    This paper introduces a new Categorical structure that is a Model of a variant of the \( \mathbf {i/o} \)-typed \( \pi \)-calculus, in the same way that a cartesian closed category is a Model of the \( \lambda \)-calculus. To the best of our knowledge, no Categorical Model has been given for the \( \mathbf {i/o} \)-typed \( \pi \)-calculus, in contrast to session-typed calculi, to which corresponding logic and Categorical structure were given. The Categorical structure introduced in this paper has a simple definition, combining two well-known structures, namely, closed Freyd category and compact closed category. The former is a Model of effectful computation in a general setting, and the latter describes connections via channels, which cause the effect we focus on in this paper. To demonstrate the relevance of the Categorical Model, we show by a semantic consideration that the \( \pi \)-calculus is equivalent to a core calculus of Concurrent ML.

  • species profunctors and taylor expansion weighted by smcc a unified framework for Modelling nondeterministic probabilistic and quantum programs
    Logic in Computer Science, 2018
    Co-Authors: Takeshi Tsukada, Kazuyuki Asada, C Luke H Ong
    Abstract:

    Motivated by a tight connection between Joyal's combinatorial species and quantitative Models of linear logic, this paper introduces weighted generalised species (or weighted profunctors), where weights are morphisms of a given symmetric monoidal closed category (SMCC). For each SMCC W, we show that the category of W-weighted profunctors is a Lafont category, a Categorical Model of linear logic with exponential. As a Model of programming languages, the construction of this paper gives a unified framework that induces adequate Models of nondeterministic, probabilistic, algebraic and quantum programming languages by an appropriate choice of the weight SMCC.

Romain Péchoux - One of the best experts on this subject based on the ideXlab platform.

  • Quantum Programming with Inductive Datatypes
    2020
    Co-Authors: Romain Péchoux, Simon Perdrix, Mathys Rennela, Vladimir Zamdzhiev
    Abstract:

    Inductive datatypes in programming languages allow users to define useful data structures such as natural numbers, lists, trees, and others. In this paper we show how inductive datatypes may be added to the quantum programming language QPL. We construct a sound Categorical Model for the language and by doing so we provide the first detailed semantic treatment of user-defined inductive datatypes in quantum programming. We also show our denotational interpretation is invariant with respect to big-step reduction, thereby establishing another novel result for quantum programming. Compared to classical programming, this property is considerably more difficult to prove and we demonstrate its usefulness by showing how it immediately implies computational adequacy at all types. To further cement our results, our semantics is entirely based on a physically natural Model of von Neumann algebras, which are mathematical structures used by physicists to study quantum mechanics.

  • Quantum Programming with Inductive Datatypes: Causality and Affine Type Theory
    2020
    Co-Authors: Romain Péchoux, Simon Perdrix, Mathys Rennela, Vladimir Zamdzhiev
    Abstract:

    Inductive datatypes in programming languages allow users to define useful data structures such as natural numbers, lists, trees, and others. In this paper we show how inductive datatypes may be added to the quantum programming language QPL. We construct a sound Categorical Model for the language and by doing so we provide the first detailed semantic treatment of user-defined inductive datatypes in quantum programming. We also show our denotational interpretation is invariant with respect to big-step reduction, thereby establishing another novel result for quantum programming. Compared to classical programming, this property is considerably more difficult to prove and we demonstrate its usefulness by showing how it immediately implies computational adequacy at all types. To further cement our results, our semantics is entirely based on a physically natural Model of von Neumann algebras, which are mathematical structures used by physicists to study quantum mechanics.

  • FoSSaCS - Quantum Programming with Inductive Datatypes: Causality and Affine Type Theory
    Lecture Notes in Computer Science, 2020
    Co-Authors: Romain Péchoux, Simon Perdrix, Mathys Rennela, Vladimir Zamdzhiev
    Abstract:

    Inductive datatypes in programming languages allow users to define useful data structures such as natural numbers, lists, trees, and others. In this paper we show how inductive datatypes may be added to the quantum programming language QPL. We construct a sound Categorical Model for the language and by doing so we provide the first detailed semantic treatment of user-defined inductive datatypes in quantum programming. We also show our denotational interpretation is invariant with respect to big-step reduction, thereby establishing another novel result for quantum programming. Compared to classical programming, this property is considerably more difficult to prove and we demonstrate its usefulness by showing how it immediately implies computational adequacy at all types. To further cement our results, our semantics is entirely based on a physically natural Model of von Neumann algebras, which are mathematical structures used by physicists to study quantum mechanics.

Brett A Hauber - One of the best experts on this subject based on the ideXlab platform.

  • eliciting benefit risk preferences and probability weighted utility using choice format conjoint analysis
    Medical Decision Making, 2011
    Co-Authors: George Van Houtven, Reed F Johnson, Vikram Kilambi, Brett A Hauber
    Abstract:

    This study applies conjoint analysis to estimate health-related benefit-risk tradeoffs in a non-expected-utility framework. We demonstrate how this method can be used to test for and estimate nonlinear weighting of adverse-event probabilities and we explore the implications of nonlinear weighting on maximum acceptable risk (MAR) measures of risk tolerance. We obtained preference data from 570 Crohn’s disease patients using a web-enabled conjoint survey. Respondents were presented with choice tasks involving treatment options that involve different efficacy benefits and different mortality risks for 3 possible side effects. Using conditional logit maximum likelihood estimation, we estimate preference parameters using 3 Models that allow for nonlinear preference weighting of risks—a Categorical Model, a simple-weighting Model, and a rank dependent utility (RDU) Model. For the second 2 Models we specify and jointly estimate 1- and 2-parameter probability weighting functions. Although the 2-parameter function...

James S. Adelman - One of the best experts on this subject based on the ideXlab platform.

  • COMMENT REPLY Automatic Vigilance for Negative Words Is Categorical and General
    2008
    Co-Authors: Zachary Estes, James S. Adelman
    Abstract:

    With other factors controlled, negative words elicit slower lexical decisions and naming than positive words (Estes & Adelman, 2008). Moreover, this marked difference in responding to negative words and to positive words (i.e., between-category discontinuity) was accompanied by relatively uniform responding among negative words (i.e., within-category equivalence), thus suggesting a Categorical Model of automatic vigilance. Larsen, Mercer, Balota, and Strube (this issue) corroborated our observation that valence predicts lexical decision and word naming latencies. However, on the basis of an interaction between linear arousal and linear valence, they claim that automatic vigilance does not occur among arousing stimuli and they purport to reject the Categorical Model. Here we show that (a) this interaction is logically irrelevant to whether automatic vigilance is Categorical; (b) the linear interaction is statistically consistent with the Categorical Model; (c) the interaction is not observed within the Categorical Model; and (d) despite having 5 fewer parameters, the Categorical Model predicts word recognition times as well as the interaction Model. Thus, automatic vigilance is Categorical and generalizes across levels of arousal.

  • Automatic vigilance for negative words is Categorical and general
    Emotion, 2008
    Co-Authors: Zachary Estes, James S. Adelman
    Abstract:

    With other factors controlled, negative words elicit slower lexical decisions and naming than positive words (Estes & Adelman, 2008; see record 2008-09984-001). Moreover, this marked difference in responding to negative words and to positive words (i.e., between-category discontinuity) was accompanied by relatively uniform responding among negative words (i.e., within-category equivalence), thus suggesting a Categorical Model of automatic vigilance. Larsen, Mercer, Balota, and Strube (this issue; see record 2008-09984-002) corroborated our observation that valence predicts lexical decision and word naming latencies. However, on the basis of an interaction between linear arousal and linear valence, they claim that automatic vigilance does not occur among arousing stimuli and they purport to reject the Categorical Model. Here we show that (a) this interaction is logically irrelevant to whether automatic vigilance is Categorical; (b) the linear interaction is statistically consistent with the Categorical Model; (c) the interaction is not observed within the Categorical Model; and (d) despite having 5 fewer parameters, the Categorical Model predicts word recognition times as well as the interaction Model. Thus, automatic vigilance is Categorical and generalizes across levels of arousal