arXiv subjects

Adrian Asmund Fessler

Publications and source records attributed to Adrian Asmund Fessler.

3 recordsLinked to original sources

arXiv2026

The fixed boundary plasma equilibrium basis for a one Gigawatt electric stellarator power plant

A fixed boundary stellarator equilibrium capable of producing 3 GW of fusion power (1 GW-electric) is presented as the design basis for the GIGA fusion power plant being developed by Gauss Fusion GmbH. The stellarator concept provides a steady-state, transient free, low recirculating power approach to a fusion power plant, which builds on 50 years of progress in plasma physics. A set of requirements for a fixed boundary equilibrium were determined through application of 0.5 D modeling. Optimization of a modified Wendelstein 7-X (W7-X) equilibrium was performed to achieve these requirements including alpha power confinement greater than $85\%$, neoclassical effective ripple below 0.01, bootstrap current below 50 kA, and reduced turbulent heat fluxes. In order to fix the plasma volume of $1500~m^3$ during optimization, the VMEC code was modified to renormalize the boundary coefficient to the desired plasma volume. The STELLOPT stellarator optimization code was modified as well to include new bootstrap current targets, a new target for the radial electric field, and the capability to hold the magnetic field on axis at a fixed value. An intermediary conceptual design plasma and final evolved fixed boundary equilibria are compared to the original modified W7-X equilibrium. The final evolved equilibrium is shown to achieve all the necessary requirements for the GIGA fusion power plant through more detailed modeling of stability, fast ion confinement, and transport.

physics.plasm-ph↗

arXiv2026

From Cables to Qubits: A Decomposed Variational Quantum Optimization Pipeline

The Cable Routing Optimization Problem (CROP) is a Multi-Commodity Flow Problem (MCFP) central to industrial layouts and smart manufacturing. Historically, quantum optimization has modeled MCFPs as Quadratic Unconstrained Binary Optimization problems (QUBOs). Recent studies suggest that mapping routing problems to Polynomial Unconstrained Binary Optimization problems (PUBOs) can improve efficiency. However, solving full-scale MCFPs with quantum optimization remains computationally challenging. To bridge this gap, we introduce a Decomposed Variational Quantum Pipeline that exploits the block-diagonal structure of CROP, breaking the multi-cable routing task into modular, single-commodity subproblems. We explicitly derive both the QUBO and PUBO representations for CROP and demonstrate that our pipeline can evaluate both formulations within the same pipeline. Our empirical study highlights a trade-off: PUBO eliminates auxiliary qubits at the cost of circuit depth. In our experiments, the decomposed pipeline accelerates time-to-solution, reliably generating feasible cable layouts while trading strict optimality for computational scalability. PUBO formulations achieved full routing feasibility across all tested seeds, while global QUBO formulations showed substantially lower robustness.

quant-ph↗

arXiv2026

SPHERE: Spherical partitioning for large-scale routing optimization

We study shortest-path routing in large weighted, undirected graphs, where expanding search frontiers raise time and memory costs for exact solvers. We propose \emph{SPHERE}, a query-aware partitioning heuristic that adaptively splits the problem by identifying \emph{source-target} ($s$--$t$) overlaps of hop-distance spheres. Selecting an anchor node $a$ within this overlap partitions the task into independent induced subgraphs for $s\to a$ and $a\to t$, each restricted to its own induced subgraph. If resulting subgraphs remain large, the procedure recurses on that specific subgraph. We provide a formal guarantee that by using the partition cut within the shared overlap, the resulting subpaths preserve feasibility, thereby avoiding the need for boundary repair. Furthermore, \emph{SPHERE} acts as a solver-agnostic framework that naturally exposes parallelism across subproblems. On million-scale road networks, \emph{SPHERE} achieves faster runtimes and smaller optimality gaps than contemporary state-of-the-art partitioning and community-based routing pipelines. Crucially, it also substantially mitigates heavy-tail runtime outliers suffered by standard exact methods, yielding highly stable and predictable execution times across varying queries.

cs.DC↗