We present a hybrid quantum-classical algorithm for distribution network reconfiguration, a combinatorial optimization problem on power distribution networks, that combines a radiality preserving branch-exchange encoding with iterative warm-start quantum imaginary-time evolution to minimize active line losses. The enco...
Noah A. Crum, Pete Pritchard, T. Rase et al.· 0 citations
Distribution network reconfiguration (DNR) is a combinatorial optimization problem that seeks a low-loss network topology subject to topological and electrical operating constraints. In this work, we propose a two-stage quantum-classical method for DNR. First, an iterative linear-ramp Quantum Alternating Operator Ansat...
Nowar Alashkar, C. Kennedy, Phillip C. Lotshaw et al.· 0 citations
The wing module is introduced, a self-contained few-qubit circuit that feeds additional information into the core circuit through a sparse, one-way coupling, and reframe scaling for quantum fine-tuning: added qubits help when they carry added inputs, not merely more parameters.
Sang Hyub Kim, Julien Baglio, R. Krishnakumar et al.· 0 citations
Topological data analysis (TDA) provides a powerful framework for extracting information about the shape of complex, unstructured data, but the classical cost of computing high dimensional topological features limits its application. Quantum algorithms for TDA offer a route around this bottleneck, yet existing approach...
Jason Iaconis, Sayonee Ray, Samwel Sekwao et al.· 0 citations
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