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OpenQL: A Portable Quantum Programming Framework for Quantum Accelerators

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Authors: N. Khammassi, I. Ashraf, J. V. Someren, R. Nane, A. M. Krol, M. A. Rol, L. Lao, K. Bertels, C. G. Almudever

Year

2021

Paper ID

13650

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

200

Citations

53

Abstract

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.

Why This Paper Matters

  • This paper contributes to the Superconducting Qubits research area in the Quantum Articles archive.
  • It adds a 2021 reference point for readers tracking recent quantum research.
  • With the potential of quantum algorithms to solve intractable classical problems, quantum computing is rapidly evolving, and more algorithms are being developed and optimized.

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