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- Fitzek, David, 1993
(författare)
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Applying quantum approximate optimization to the heterogeneous vehicle routing problem
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Quantum computing offers new heuristics for combinatorial problems. With small- and intermediate-scale quantum devices becoming available, it is possible to implement and test these heuristics on small-size problems. A candidate for such combinatorial problems is the heterogeneous vehicle routing problem (HVRP): the problem of finding the optimal set of routes, given a heterogeneous fleet of vehicles with varying loading capacities, to deliver goods to a given set of customers. This licentiate thesis is an extended introduction to the accompanying paper, which consists of a study of a new formulation of the HVRP applicable to both quantum annealers and programmable noisy intermediate-scale quantum (NISQ) devices.
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| 3. |
- Fitzek, David, 1993, et al.
(författare)
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Optimizing Variational Quantum Algorithms with qBang: Efficiently Interweaving Metric and Momentum to Navigate Flat Energy Landscapes
- 2024
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Ingår i: Quantum. - 2521-327X. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Variational quantum algorithms (VQAs) represent a promising approach to utilizing current quantum computing infrastructures. 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 momentumbased algorithm, qBang reduces quantumresource requirements while performing better than more resource -demanding alternatives. Benchmarks for the barren plateau, quantum chemistry, and the maxcut 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.
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