Context-Sensitive and Duration-Aware Qubit Mapping for Various NISQ Devices

• URL: http://arxiv.org/abs/2001.06887v1
• Date: Sun, 19 Jan 2020 19:35:43 GMT
• Title: Context-Sensitive and Duration-Aware Qubit Mapping for Various NISQ Devices
• Authors: Yu Zhang and Haowei Deng and Quanxi Li
• Abstract summary: We propose a COntext-sensitive and Duration-Aware Remapping algorithm (Codar) based on the Quantum Abstract Machine (QAM) By introducing lock for each qubit, Codar is aware of gate duration difference and program context. Compared to the best-known algorithm, Codar halves the total execution time of several quantum algorithms and cut down 17.5% - 19.4% total execution time on average in different architectures.
• Score: 4.866886176084101
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Quantum computing (QC) technologies have reached a second renaissance in the last decade. Some fully programmable QC devices have been built based on superconducting or ion trap technologies. Although different quantum technologies have their own parameter indicators, QC devices in the NISQ era share common features and challenges such as limited qubits and connectivity, short coherence time and high gate error rates. Quantum programs written by programmers could hardly run on real hardware directly since two-qubit gates are usually allowed on few pairs of qubits. Therefore, quantum computing compilers must resolve the mapping problem and transform original programs to fit the hardware limitation. To address the issues mentioned above, we summarize different quantum technologies and abstractly define Quantum Abstract Machine (QAM); then propose a COntext-sensitive and Duration-Aware Remapping algorithm (Codar) based on the QAM. By introducing lock for each qubit, Codar is aware of gate duration difference and program context, which bring it abilities to extract more program's parallelism and reduce program execution time. Compared to the best-known algorithm, Codar halves the total execution time of several quantum algorithms and cut down 17.5% - 19.4% total execution time on average in different architectures.

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