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Variational quantum algorithms (VQAs) represent a promising approach toutilizing current quantum computing infrastructures. VQAs are based on aparameterized quantum circuit optimized in a closed loop via a classicalalgorithm. This hybrid approach reduces the quantum processing unit load butcomes at the cost of a classical optimization that can feature a flat energylandscape. Existing optimization techniques, including either imaginarytime-propagation, natural gradient, or momentum-based approaches, are promisingcandidates but place either a significant burden on the quantum device orsuffer frequently from slow convergence. In this work, we propose the quantumBroyden adaptive natural gradient (qBang) approach, a novel optimizer that aimsto distill the best aspects of existing approaches. By employing the Broydenapproach to approximate updates in the Fisher information matrix and combiningit with a momentum-based algorithm, qBang reduces quantum-resource requirementswhile performing better than more resource-demanding alternatives. Benchmarksfor the barren plateau, quantum chemistry, and the max-cut problem demonstratean overall stable performance with a clear improvement over existing techniquesin the case of flat optimization landscapes. qBang introduces a new developmentstrategy for gradient-based VQAs with a plethora of possible improvements.

Optimizing Variational Quantum Algorithms with qBang: Efficiently Interweaving Metric and Momentum to Tackle Flat Energy Landscapes