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VQAs are based on a parameterized quantum circuit optimized in a closed loop via a classical algorithm. This hybrid approach reduces the quantum processing unit load but comes at the cost of a classical optimization that can feature a flat energy landscape. Existing optimization techniques, including either imaginary time-propagation, natural gradient, or momentum-based approaches, are promising candidates but place either a significant burden on the quantum device or suffer frequently from slow convergence. In this work, we propose the quantum Broyden adaptive natural gradient (qBang) approach, a novel optimizer that aims to distill the best aspects of existing approaches. By employing the Broyden approach to approximate updates in the Fisher information matrix and combining it with a momentum-based algorithm, qBang reduces quantum-resource requirements while performing better than more resource-demanding alternatives. Benchmarks for the barren plateau, quantum chemistry, and the max-cut problem demonstrate an overall stable performance with a clear improvement over existing techniques in the case of flat (but not exponentially flat) optimization landscapes. qBang introduces a new development strategy for gradient-based VQAs with a plethora of possible improvements.<\/jats:p>","DOI":"10.22331\/q-2024-04-09-1313","type":"journal-article","created":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T11:38:37Z","timestamp":1712662717000},"page":"1313","update-policy":"https:\/\/doi.org\/10.22331\/q-crossmark-policy-page","source":"Crossref","is-referenced-by-count":10,"title":["Optimizing Variational Quantum Algorithms with qBang: Efficiently Interweaving Metric and Momentum to Navigate Flat Energy Landscapes"],"prefix":"10.22331","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4268-5485","authenticated-orcid":false,"given":"David","family":"Fitzek","sequence":"first","affiliation":[{"name":"Department of Microtechnology and Nanoscience, MC2, Chalmers University of Technology, 412 96 Gothenburg, Sweden"},{"name":"Volvo Group Trucks Technology, 405 08 Gothenburg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8235-3058","authenticated-orcid":false,"given":"Robert S.","family":"Jonsson","sequence":"additional","affiliation":[{"name":"Department of Microtechnology and Nanoscience, MC2, Chalmers University of Technology, 412 96 Gothenburg, Sweden"},{"name":"Future Technologies, Saab Surveillance, 412 76 Gothenburg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6479-1874","authenticated-orcid":false,"given":"Werner","family":"Dobrautz","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Chemical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8557-733X","authenticated-orcid":false,"given":"Christian","family":"Sch\u00e4fer","sequence":"additional","affiliation":[{"name":"Department of Microtechnology and Nanoscience, MC2, Chalmers University of Technology, 412 96 Gothenburg, Sweden"},{"name":"Department of Physics, Chalmers University of Technology, 412 96 Gothenburg, Sweden"}]}],"member":"9598","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"0","doi-asserted-by":"publisher","unstructured":"M. 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