Plug-in quantum software

Taballione

Large-scale and programmable quantum photonic processors are needed to control photonic quantum computations that lead to a quantum advantage. Here, we present a universal 12-mode quantum photonic processor which is the largest of its kind to date. The processor is remotely controllable and seamlessly integrated with a dedicated control software, making the device fully plug-and-play.

Leonie Mueck

Damian S. Steiger, Thomas Häner, Matthias Troyer

We introduce ProjectQ, an open source software effort for quantum computing. The first release features a compiler framework capable of targeting various types of hardware, a high-performance simulator with emulation capabilities, and compiler plug-ins for circuit drawing and resource estimation. We introduce our Python-embedded domain-specific language, present the features, and provide example implementations for quantum algorithms. The framework allows testing of quantum algorithms through simulation and enables running them on actual quantum hardware using a back-end connecting to the IBM Quantum Experience cloud service. Through extension mechanisms, users can provide back-ends to further quantum hardware, and scientists working on quantum compilation can provide plug-ins for additional compilation, optimization, gate synthesis, and layout strategies.

José A. Cruz-Lemus, Manuel Serrano

Harry Buhrman, Floor van de Pavert

Otso Kinanen, Andrés D. Muñoz-Moller, Vlad Stirbu +2 more

Abstract Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, these new capabilities need software solutions that are able to effectively harness its power. However, developers may face significant challenges when developing and executing quantum software due to the limited availability of quantum computer hardware, high computational demands of simulating quantum computers on classical systems, and complicated technology stack to enable currently available accelerators into development environments. These limitations make it difficult for the developer to create an efficient workflow for quantum software development. In this paper, we investigate the potential of using remote computational capabilities in an efficient manner to improve the workflow of quantum software developers, by lowering the barrier of moving between local execution and computationally more efficient remote hardware and offering speedup in execution with simulator surroundings. The goal is to allow the development of more complex circuits and to support an iterative software development approach. In our experiment, with the solution presented in this paper, we have obtained up to 5 times faster circuit execution runtime, and enabled qubit ranges from 21 to 29 qubits with a simple plug-and-play kernel for the Jupyter notebook.