Computer Science

We introduce dependently typed Proto-Quipper, or Proto-Quipper-D for short, an experimental quantum circuit programming language with linear dependent types. We give several examples to illustrate how linear dependent types can help in the construction of correct quantum circuits. Specifically, we show how dependent types enable programming families of circuits, and how dependent types solve the problem of type-safe uncomputation of garbage qubits. We also discuss other language features along the way.

Component-based development is challenging in a distributed setting, for starters considering programming a task may involve the assembly of loosely-coupled remote components. In order for the task to be fulfilled, the supporting interaction among components should follow a well-defined protocol. In this paper we address a model for message passing component-based systems where components are assembled together with the protocol itself. Components can therefore be independent from the protocol, and reactive to messages in a flexible way. Our contribution is at the level of the type language that allows to capture component behaviour so as to check its compatibility with a protocol. We show the correspondence of component and type behaviours, which entails a progress property for components.

We present a High-Level Python-based Hardware Description Language (ARGG-HDL), It uses Python as its source language and converts it to standard VHDL. Compared to other approaches of building converters from a high-level programming language into a hardware description language, this new approach aims to maintain an object-oriented paradigm throughout the entire process. Instead of removing all the high-level features from Python to make it into an HDL, this approach goes the opposite way. It tries to show how certain features from a high-level language can be implemented in an HDL, providing the corresponding benefits of high-level programming for the user.

Abstracting Gradual Typing (AGT) is a systematic approach to designing gradually-typed languages. Languages developed using AGT automatically satisfy the formal semantic criteria for gradual languages identified by Siek et al. [2015]. Nonetheless, vanilla AGT semantics can still have important shortcomings. First, a gradual language's runtime checks should preserve the space-efficiency guarantees inherent to the underlying static and dynamic languages. To the contrary, the default operational semantics of AGT break proper tail calls. Second, a gradual language's runtime checks should enforce basic modular type-based invariants expected from the static type discipline. To the contrary, the default operational semantics of AGT may fail to enforce some invariants in surprising ways. We demonstrate this in the GTFL ≲ language of Garcia et al. [2016]. This paper addresses both problems at once by refining the theory underlying AGT's dynamic checks. Garcia et al. [2016] observe that AGT involves two abstractions of static types: one for the static semantics and one for the dynamic semantics. We recast the latter as an abstract interpretation of subtyping itself, while gradual types still abstract static types. Then we show how forward-completeness [Giacobazzi and Quintarelli 2001] is key to supporting both space-efficient execution and reliable runtime type enforcement.

Many programming problems call for turning geometrical thoughts into code: tables, hierarchical structures, nests of objects, trees, forests, graphs, and so on. Linear text does not do justice to such thoughts. But, it has been the dominant programming medium for the past and will remain so for the foreseeable future. This paper proposes a novel mechanism for conveniently extending textual programming languages with problem-specific visual syntax. It argues the necessity of this language feature, demonstrates the feasibility with a robust prototype, and sketches a design plan for adapting the idea to other languages.

Most interaction with a computer is done via a graphical user interface. Traditionally, these are implemented in an imperative fashion using shared mutable state and callbacks. This is efficient, but is also difficult to reason about and error prone. Functional Reactive Programming (FRP) provides an elegant alternative which allows GUIs to be designed in a declarative fashion. However, most FRP languages are synchronous and continually check for new data. This means that an FRP-style GUI will "wake up" on each program cycle. This is problematic for applications like text editors and browsers, where often nothing happens for extended periods of time, and we want the implementation to sleep until new data arrives. In this paper, we present an asynchronous FRP language for designing GUIs called λ Widget . Our language provides a novel semantics for widgets, the building block of GUIs, which offers both a natural Curry--Howard logical interpretation and an efficient implementation strategy.

Dynamically typed languages such as JavaScript and Python have emerged as the most popular programming languages in use. Important benefits can accrue from including type annotations in dynamically typed programs. This approach to gradual typing is exemplified by the TypeScript programming system which allows programmers to specify partially typed programs, and then uses static analysis to infer the remaining types. However, in general, the effectiveness of static type inference is limited and depends on the complexity of the program's structure and the initial type annotations. As a result, there is a strong motivation for new approaches that can advance the state of the art in statically predicting types in dynamically typed programs, and that do so with acceptable performance for use in interactive programming environments. Previous work has demonstrated the promise of probabilistic type inference using deep learning. In this paper, we advance past work by introducing a range of graph neural network (GNN) models that operate on a novel type flow graph (TFG) representation. The TFG represents an input program's elements as graph nodes connected with syntax edges and data flow edges, and our GNN models are trained to predict the type labels in the TFG for a given input program. We study different design choices for our GNN models for the 100 most common types in our evaluation dataset, and show that our best two GNN configurations for accuracy achieve a top-1 accuracy of 87.76% and 86.89% respectively, outperforming the two most closely related deep learning type inference approaches from past work -- DeepTyper with a top-1 accuracy of 84.62% and LambdaNet with a top-1 accuracy of 79.45%. Further, the average inference throughputs of those two configurations are 353.8 and 1,303.9 files/second, compared to 186.7 files/second for DeepTyper and 1,050.3 files/second for LambdaNet.

Context: Actor-based programming languages offer many essential features for developing modern distributed reactive systems. These systems exploit the actor model's isolation property to fulfill their performance and scalability demands. Unfortunately, the reliance of the model on isolation as its most fundamental property requires programmers to express complex interaction patterns between their actors to be expressed manually in terms of complex combinations of messages sent between the isolated actors. Inquiry: In the last three decades, several language design proposals have been introduced to reduce the complexity that emerges from describing said interaction and coordination of actors. We argue that none of these proposals is satisfactory in order to express the many complex interaction patterns between actors found in modern reactive distributed systems. Approach: We describe seven smart home automation scenarios (in which an actor represents every smart home appliance) to motivate the support by actor languages for five radically different types of message synchronization patterns, which are lacking in modern distributed actor-based languages. Fortunately, these five types of synchronisation patterns have been studied extensively by the Complex Event Processing (CEP) community. Our paper describes how such CEP patterns are elegantly added to an actor-based programming language. Knowledge: Based on our findings, we propose an extension of the single-message matching paradigm of contemporary actor-based languages in order to support a multiple-message matching way of thinking in the same way as proposed by CEP languages. Our proposal thus enriches the actor-model by ways of declaratively describing complex message combinations to which an actor can respond. Grounding: We base the problem-statement of the paper on an online poll in the home automation community that has motivated the real need for the CEP-based synchronisation operators between actors proposed in the paper. Furthermore, we implemented a DSL -- called Sparrow -- that supports said operators and we argue quantitatively (in terms of LOC and in terms of a reduction of the concerns that have to be handled by programmers) that the DSL outperforms existing approaches. Importance: This work aims to provide a set of synchronization operators that help actor-based languages to handle the complex interaction required by modern reactive distributed systems. To the best of our knowledge, our proposal is the first one to add advanced CEP synchronization operators to the -- relatively simplistic single-message based matching -- mechanisms of most actor-based languages.

Tezos is a smart-contract blockchain. Tezos smart contracts are written in a low-level stack-based language called Michelson. In this article we present Albert, an intermediate language for Tezos smart contracts which abstracts Michelson stacks as linearly typed records. We also describe its compiler to Michelson, written in Coq, that targets Mi-Cho-Coq, a formal specification of Michelson implemented in Coq.

We present an algorithm for synthesizing program loops satisfying a given polynomial loop invariant. The class of loops we consider can be modeled by a system of algebraic recurrence equations with constant coefficients. We turn the task of loop synthesis into a polynomial constraint problem by precisely characterizing the set of all loops satisfying the given invariant. We prove soundness of our approach, as well as its completeness with respect to an a priori fixed upper bound on the number of program variables. Our work has applications towards program verification, as well as generating number sequences from algebraic relations. We implemented our work in the Absynth tool and report on our initial experiments with loop synthesis.