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An Efficient Methodology for Mapping Quantum Circuits to the IBM QX Architectures

arXiv
Authors: Alwin Zulehner, Alexandru Paler, Robert Wille

Year

2017

Paper ID

24534

Status

Preprint

Abstract Read

~2 min

Abstract Words

291

Citations

N/A

Abstract

In the past years, quantum computers more and more have evolved from an academic idea to an upcoming reality. IBM's project IBM Q can be seen as evidence of this progress. Launched in March 2017 with the goal to provide access to quantum computers for a broad audience, this allowed users to conduct quantum experiments on a 5-qubit and, since June 2017, also on a 16-qubit quantum computer (called IBM QX2 and IBM QX3, respectively). Revised versions of these 5-qubit and 16-qubit quantum computers (named IBM QX4 and IBM QX5, respectively) are available since September 2017. In order to use these, the desired quantum functionality (e.g. provided in terms of a quantum circuit) has to be properly mapped so that the underlying physical constraints are satisfied - a complex task. This demands solutions to automatically and efficiently conduct this mapping process. In this paper, we propose a methodology which addresses this problem, i.e. maps the given quantum functionality to a realization which satisfies all constraints given by the architecture and, at the same time, keeps the overhead in terms of additionally required quantum gates minimal. The proposed methodology is generic, can easily be configured for similar future architectures, and is fully integrated into IBM's SDK. Experimental evaluations show that the proposed approach clearly outperforms IBM's own mapping solution. In fact, for many quantum circuits, the proposed approach determines a mapping to the IBM architecture within minutes, while IBM's solution suffers from long runtimes and runs into a timeout of 1 hour in several cases. As an additional benefit, the proposed approach yields mapped circuits with smaller costs (i.e. fewer additional gates are required). All implementations of the proposed methodology is publicly available at http://iic.jku.at/eda/research/ibm_qx_mapping.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2017 reference point for readers tracking recent quantum research.
  • In the past years, quantum computers more and more have evolved from an academic idea to an upcoming reality.

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