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.
Original language | English |
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Article number | 8382253 |
Number of pages | 11 |
Journal | IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems (TCAD) |
DOIs | |
Publication status | Published - 2018 |
Fields of science
- 102 Computer Sciences
- 202 Electrical Engineering, Electronics, Information Engineering
JKU Focus areas
- Computation in Informatics and Mathematics
- Mechatronics and Information Processing
- Nano-, Bio- and Polymer-Systems: From Structure to Function