A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits
Variational quantum optimization algorithms, such as the variational quantum eigensolver (VQE) or the quantum approximate optimization algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an algorithm based on the VQE, which uses exponentially fewer q...
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Quantum Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10506971/ |
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| author | David Winderl Nicola Franco Jeanette Miriam Lorenz |
| author_facet | David Winderl Nicola Franco Jeanette Miriam Lorenz |
| author_sort | David Winderl |
| collection | DOAJ |
| description | Variational quantum optimization algorithms, such as the variational quantum eigensolver (VQE) or the quantum approximate optimization algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an algorithm based on the VQE, which uses exponentially fewer qubits compared to the QAOA. We highlight the numerical instabilities generated by encoding the problem into the variational ansatz and propose a classical optimization procedure to find the ground state of the ansatz in fewer iterations with a better or similar objective. In addition, we propose a method to embed the linear interpolation of the MaxCut problem on a quantum device. Furthermore, we compare classical optimizers for this variational ansatz on quadratic unconstrained binary optimization and graph partitioning problems. |
| format | Article |
| id | doaj-art-2dca029b847a40d4b838823c1a22ecd5 |
| institution | Kabale University |
| issn | 2689-1808 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Quantum Engineering |
| spelling | doaj-art-2dca029b847a40d4b838823c1a22ecd52025-01-25T00:03:37ZengIEEEIEEE Transactions on Quantum Engineering2689-18082024-01-01511010.1109/TQE.2024.339283410506971A Comparative Study on Solving Optimization Problems With Exponentially Fewer QubitsDavid Winderl0https://orcid.org/0009-0001-9822-5536Nicola Franco1https://orcid.org/0000-0002-7320-5627Jeanette Miriam Lorenz2https://orcid.org/0000-0001-6530-1873Fraunhofer Institute for Cognitive Systems IKS, Munich, GermanyFraunhofer Institute for Cognitive Systems IKS, Munich, GermanyFraunhofer Institute for Cognitive Systems IKS, Munich, GermanyVariational quantum optimization algorithms, such as the variational quantum eigensolver (VQE) or the quantum approximate optimization algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an algorithm based on the VQE, which uses exponentially fewer qubits compared to the QAOA. We highlight the numerical instabilities generated by encoding the problem into the variational ansatz and propose a classical optimization procedure to find the ground state of the ansatz in fewer iterations with a better or similar objective. In addition, we propose a method to embed the linear interpolation of the MaxCut problem on a quantum device. Furthermore, we compare classical optimizers for this variational ansatz on quadratic unconstrained binary optimization and graph partitioning problems.https://ieeexplore.ieee.org/document/10506971/Hybrid algorithmsoptimizationquantum computing |
| spellingShingle | David Winderl Nicola Franco Jeanette Miriam Lorenz A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits IEEE Transactions on Quantum Engineering Hybrid algorithms optimization quantum computing |
| title | A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits |
| title_full | A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits |
| title_fullStr | A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits |
| title_full_unstemmed | A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits |
| title_short | A Comparative Study on Solving Optimization Problems With Exponentially Fewer Qubits |
| title_sort | comparative study on solving optimization problems with exponentially fewer qubits |
| topic | Hybrid algorithms optimization quantum computing |
| url | https://ieeexplore.ieee.org/document/10506971/ |
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