QCOR: A Language Extension Specification for the Heterogeneous Quantum-Classical Model of Computation

• Published in: ACM journal on emerging technologies in computing systems 2020-04, Vol.16 (2), p.1-17
• Main Authors: Mintz, Tiffany M., McCaskey, Alexander J., Dumitrescu, Eugene F., Moore, Shirley V., Powers, Sarah, Lougovski, Pavel
• Format: Article
• Language: English
• Subjects: computing methodologies; distributed programming languages; heterogeneous computing; MATHEMATICS AND COMPUTING; NISQ; object oriented constructs; parallel programming languages; programming model specification; quantum computing; quantum programming; theory of computation
• ISSN: 1550-4832; 1550-4840
• DOI: 10.1145/3380964

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.