ProjectQ: Compiling quantum programs for various backends

Luke Southall, Joshua Ammermann, Rinor Kelmendi +2 more

In the NISQ-era, there is a wide variety of hardware manufacturers building quantum computers. Each of these companies may choose different approaches and hardware architectures for their machines. This poses a problem for quantum software engineering, as the retargetability of quantum programs across different hardware platforms becomes a non-trivial challenge. In response to this problem, various retargetable quantum compilers have been presented in the scientific literature. These promise the ability to compile software for different hardware platforms, enabling retargetability for quantum software. In this paper, we develop and apply a metric by which the retargetability of the quantum compilers can be assessed. We develop and run a study to analyze key aspects regarding the retargetability of the compilers Tket, Qiskit, and ProjectQ. Our findings indicate that Tket demonstrates the highest level of retargetability, closely followed by Qiskit, while ProjectQ lags behind. These results provide insights for quantum software developers in selecting appropriate compilers for their use-cases, and highlight areas for improvement in quantum compilers.

Paolo Zuliani

José A. Cruz-Lemus, Manuel Serrano

Luke Southall, Joshua Ammermann, Rinor Kelmendi +2 more

In the NISQ-era, there is a wide variety of hardware manufacturers building quantum computers. Each of these companies may choose different approaches and hardware architectures for their machines. This poses a problem for quantum software engineering, as the retargetability of quantum programs across different hardware platforms becomes a non-trivial challenge. In response to this problem, various retargetable quantum compilers have been presented in the scientific literature. These promise the ability to compile software for different hardware platforms, enabling retargetability for quantum software. In this paper, we develop and apply a metric by which the retargetability of the quantum compilers can be assessed. We develop and run a study to analyze key aspects regarding the retargetability of the compilers Tket, Qiskit, and ProjectQ. Our findings indicate that Tket demonstrates the highest level of retargetability, closely followed by Qiskit, while ProjectQ lags behind. These results provide insights for quantum software developers in selecting appropriate compilers for their use-cases, and highlight areas for improvement in quantum compilers.

Damien Nguyen, Dmitry Mikushin, Yung Man-Hong

In this work, we present some of the latest efforts made at Huawei Research in order to improve the overall performance of ProjectQ, the quantum computing framework used as the foundation of our quantum research. Since a few years, performance assessment of the framework using profiling tools has shown that a significant portion of the compilation time is spent in doing memory management linked to the lifetime and access of Python objects. The main purpose of this work is therefore aimed at addressing some of these limitations by introducing a new C++ processing backend for ProjectQ, as well as starting a complete rewrite of the simulator code already written in C++. The core of this work is centered around on a new C++ API for ProjectQ that moves most of the compiler processing into natively compiled code. We achieve this by providing a new compiler engine to perform the conversion from Python to C++, which ensures that we retain maximum compatibility with existing user code while providing significant speed-ups. We also introduce some of our work aimed at porting the existing C++ code to offload the more demanding calculations onto GPUs for better performance. We then investigate the performance of this new API by comparing it with the original ProjectQ implementation, as well as some other existing quantum computing frameworks. The preliminary results show that the C++ API is able to considerably reduce the cost of compilation, to the point that the compilation process becomes mostly limited by the performance of the simulator. Unfortunately, due to the some last minutes developments and the time frame required for the submission of the present manuscript, we are unable to provide conclusive benchmark results for the GPU-capable implementation of the simulator. These will most likely be presented during the keynote presentation at the DATE conference in 2021.

T. Loke, Jingbo B. Wang, Y.H. Chen