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672
Heuristic Reordering Strategy for Quantum Circuit Mapping on LNN Architectures.
Jinfeng He1, Hai Xu1,2, Shiguang Feng1
1School of Information Science and Technology, Nantong University, Nantong 226019, China.
Computational Intelligence and Neuroscience
|May 16, 2022
Summary
This study introduces new heuristic reordering strategies for quantum circuit mapping on linear nearest neighbor architectures. These methods significantly reduce the number of SWAP gates needed, improving quantum circuit efficiency.
Area of Science:
- Quantum Computing
- Quantum Information Science
Background:
- Quantum gates have connectivity constraints, preventing direct operation on nonadjacent qubits.
- Quantum circuit mapping is essential to adapt logical circuits to physical hardware constraints using SWAP gates.
Purpose of the Study:
- To reduce the number of SWAP gates in quantum circuit mapping.
- To optimize quantum circuit mapping for linear nearest neighbor (LNN) architectures.
Main Methods:
- Proposed global heuristic qubit reordering optimization algorithm.
- Proposed local heuristic qubit reordering optimization algorithm.
Main Results:
- Achieved average improvements of 13.19% with the global method.
- Achieved average improvements of 15.46% with the local method.
- Demonstrated significant reduction in quantum cost compared to existing algorithms.
Conclusions:
- The proposed heuristic methods effectively reduce SWAP gates for LNN architectures.
- These strategies are applicable to one-dimensional quantum architectures and adaptable to others.
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