OpenQL: A Portable Quantum Programming Framework for Quantum Accelerators

With the potential of quantum algorithms to solve intractable classical problems, quantum computing is rapidly evolving, and more algorithms are being developed and optimized. Expressing these quantum algorithms using a high-level language and making them executable on a quantum processor while abstracting away hardware details is a challenging task. First, a quantum programming language should provide an intuitive programming interface to describe those algorithms. Then a compiler has to transform the program into a quantum circuit, optimize it, and map it to the target quantum processor respecting the hardware constraints such as the supported quantum operations, the qubit connectivity, and the control electronics limitations. In this article, we propose a quantum programming framework named OpenQL, which includes a high-level quantum programming language and its associated quantum compiler. We present the programming interface of OpenQL, we describe the different layers of the compiler and how we can provide portability over different qubit technologies. Our experiments show that OpenQL allows the execution of the same high-level algorithm on two different qubit technologies, namely superconducting qubits and Si-Spin qubits. Besides the executable code, OpenQL also produces an intermediate quantum assembly code, which is technology independent and can be simulated using the QX simulator.

Playback Robot Programming Framework for Fiber Spraying Processes

Robot-based automation is still not widespread in small and medium-sized enterprises, since programming industrial robots is usually costly and only feasible by experts. This disadvantages can be resolved by using intuitive robot programming approaches like playback programming. At the same time, there are currently not automatized automatized, like fiber spraying. We present a novel approach in programming a robot system for fiber spraying processes, which extends a playback programming framework inspired by video editing concepts. The resulting framework allows the programming of also the periphery devices needed for the fiber spraying process. We evaluated the resulting programming framework to measure the intuitiveness in the use and show that the framework is not only able to program fiber spraying tasks but is also rather intuitive to use for domain experts.

Learning Distributional Programs for Relational Autocompletion

Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DCs), which supports both discrete and continuous probability distributions. Within this framework, we introduce DiceML – an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data). To realize this, DiceML integrates statistical modeling and DCs with rule learning. The distinguishing features of DiceML are that it (1) tackles autocompletion in relational data, (2) learns DCs extended with statistical models, (3) deals with both discrete and continuous distributions, (4) can exploit background knowledge, and (5) uses an expectation–maximization-based (EM) algorithm to cope with missing data. The empirical results show the promise of the approach, even when there is missing data.

Bounds for the Lonely Runner Problems Via Linear Programming

In this note we develop a linear programming framework to produce upper and lower bounds for the lonely runner problem.