001043252 001__ 1043252
001043252 005__ 20250624202315.0
001043252 0247_ $$2doi$$a10.48550/ARXIV.2505.17944
001043252 037__ $$aFZJ-2025-02802
001043252 041__ $$aEnglish
001043252 1001_ $$0P:(DE-Juel1)194305$$aMontanez-Barrera, J. A.$$b0$$ufzj
001043252 245__ $$aOptimizing QAOA circuit transpilation with parity twine and SWAP network encodings
001043252 260__ $$barXiv$$c2025
001043252 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1750750241_10824
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001043252 3367_ $$2BibTeX$$aARTICLE
001043252 3367_ $$2DataCite$$aOutput Types/Working Paper
001043252 520__ $$aMapping quantum approximate optimization algorithm (QAOA) circuits with non-trivial connectivity in fixed-layout quantum platforms such as superconducting-based quantum processing units (QPUs) requires a process of transpilation to match the quantum circuit on the given layout. This step is critical for reducing error rates when running on noisy QPUs. Two methodologies that improve the resource required to do such transpilation are the SWAP network and parity twine chains (PTC). These approaches reduce the two-qubit gate count and depth needed to represent fully connected circuits. In this work, a simulated annealing-based method is introduced that reduces the PTC and SWAP network encoding requirements in QAOA circuits with non-fully connected two-qubit gates. This method is benchmarked against various transpilers and demonstrates that, beyond specific connectivity thresholds, it achieves significant reductions in both two-qubit gate count and circuit depth, surpassing the performance of Qiskit transpiler at its highest optimization level. For example, for a 120-qubit QAOA instance with 25% connectivity, our method achieves an 85% reduction in depth and a 28% reduction in two-qubit gates. Finally, the practical impact of PTC encoding is validated by benchmarking QAOA on the ibm_fez device, showing improved performance up to 20 qubits, compared to a 15-qubit limit when using SWAP networks.
001043252 536__ $$0G:(DE-HGF)POF4-5221$$a5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522)$$cPOF4-522$$fPOF IV$$x0
001043252 536__ $$0G:(DE-Juel1)BMBF-13N16149$$aBMBF 13N16149 - QSolid - Quantencomputer im Festkörper (BMBF-13N16149)$$cBMBF-13N16149$$x1
001043252 536__ $$0G:(MKW-NRW)QuGrids20231101$$aQuGrids - Quantum-based Energy Grids (QuGrids20231101)$$cQuGrids20231101$$x2
001043252 588__ $$aDataset connected to DataCite
001043252 650_7 $$2Other$$aQuantum Physics (quant-ph)
001043252 650_7 $$2Other$$aFOS: Physical sciences
001043252 7001_ $$0P:(DE-Juel1)204481$$aJi, Yanjun$$b1$$eCorresponding author$$ufzj
001043252 7001_ $$0P:(DE-HGF)0$$avon Spakovsky, Michael R.$$b2
001043252 7001_ $$0P:(DE-HGF)0$$aNeira, David E. Bernal$$b3
001043252 7001_ $$0P:(DE-Juel1)138295$$aMichielsen, Kristel$$b4$$ufzj
001043252 773__ $$a10.48550/ARXIV.2505.17944
001043252 909CO $$ooai:juser.fz-juelich.de:1043252$$pVDB
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001043252 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)204481$$aForschungszentrum Jülich$$b1$$kFZJ
001043252 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138295$$aForschungszentrum Jülich$$b4$$kFZJ
001043252 9131_ $$0G:(DE-HGF)POF4-522$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5221$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vQuantum Computing$$x0
001043252 9141_ $$y2025
001043252 920__ $$lyes
001043252 9201_ $$0I:(DE-Juel1)PGI-12-20200716$$kPGI-12$$lQuantum Computing Analytics$$x0
001043252 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x1
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