Abstract

The execution of a quantum algorithm typically requires various classical pre- and post-processing tasks. Hence, workflows are a promising means to orchestrate these tasks, benefiting from their reliability, robustness, and features, such as transactional processing. However, the implementations of the tasks may be very heterogeneous and they depend on the quantum hardware used to execute the quantum circuits of the algorithm. Additionally, today’s quantum computers are still restricted, which limits the size of the quantum circuits that can be executed. As the circuit size often depends on the input data of the algorithm, the selection of quantum hardware to execute a quantum circuit must be done at workflow runtime. However, modeling all possible alternative tasks would clutter the workflow model and require its adaptation whenever a new quantum computer or software tool is released. To overcome this problem, we introduce an approach to automatically select suitable quantum hardware for the execution of quantum circuits in workflows. Furthermore, it enables the dynamic adaptation of the workflows, depending on the selection at runtime based on reusable workflow fragments. We validate our approach with a prototypical implementation and a case study demonstrating the hardware selection for Simon’s algorithm.

Highlights

  • In recent years, various advances have been made in the development of quantum computers, quantum algorithms, and corresponding software tools [1,2,3]

  • To ease the modeling of such quantum workflows and increase the reuse of implementations for the various tasks, we introduced the Quantum Modeling Extension (QuantME) [21] for imperative workflow languages, such as BPMN [22] or BPEL [23]

  • We introduced an approach for the automated quantum hardware selection for quantum workflows

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Summary

Introduction

Various advances have been made in the development of quantum computers, quantum algorithms, and corresponding software tools [1,2,3]. We introduced the NISQ Analyzer [10], which automatically selects suitable quantum hardware for the execution of a quantum algorithm based on given input data For this purpose, the corresponding quantum circuit is compiled for the different available quantum computers and simulators to retrieve its hardware-dependent depth and width. For the decoherence times of the qubits and the maximum gate time, the NISQ Analyzer uses up-to-date provenance data [33] about the available quantum hardware These data are, e.g., retrieved from the API of the corresponding quantum hardware provider if available or otherwise determined by executing respective calibration circuits [8,16]. Thereby, the selection of a suitable workflow fragment for the replacement depends on the configuration attributes of the modeling construct, such as the quantum hardware to use These workflow fragments can be heterogeneous and use various SDKs, compilers, optimizers, or APIs of different quantum hardware providers

Problem Statement
Related Work
Automated Quantum Hardware Selection Approach
Quantum Hardware Selection Subprocess
Automated Hardware Selection and Dynamic Workflow Adaptation
Transformation into Native Workflow Models
Result
System Architecture
Prototypical Implementation
Case Study
Results?
Conclusions and Future Work
Full Text
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