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Optimization of Local Ordering Technique for Nearest Neighbour Circuits

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Machine Learning, Image Processing, Network Security and Data Sciences (MIND 2020)

Abstract

Most quantum architectures restrict qubit interactions. They allow a qubit to interact with another qubit only if they are directly connected, and this constraint is the nearest neighbour (NN) constraint. If the interacting qubits are not adjacent, we need to insert swap gates appropriately to make them adjacent. Since the insertion of swap gates increases the circuit cost, a minimal number of swaps has to be performed. This paper illustrates the possibility of swap gate reduction for 2D NN circuits by better re-ordering of qubits using a multi-window look-ahead approach. Using this technique, near optimal solutions for NN circuit conversion are obtained. Experimental evaluation shows the effectiveness of our proposed local re-ordering algorithm for the reduction of swap requirements. We have compared our results with the most recent results, and a significant improvement is observed, with a maximum of 37.5% swap gate reduction.

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Correspondence to Lalengmawia Chhangte .

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Chhangte, L., Chakrabarty, A. (2020). Optimization of Local Ordering Technique for Nearest Neighbour Circuits. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_16

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  • DOI: https://doi.org/10.1007/978-981-15-6318-8_16

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  • Online ISBN: 978-981-15-6318-8

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