Satellite Mission Planning with Rydberg Atoms
- URL: http://arxiv.org/abs/2606.23045v1
- Date: Mon, 22 Jun 2026 08:54:29 GMT
- Title: Satellite Mission Planning with Rydberg Atoms
- Abstract summary: We investigate a problem that requires every-day scheduling of critical tasks involving a large number of actors.<n> Namely, fixing the planning for a Earth Observation satellite fleet composed of several units exposed to a high density of targets to be scanned.<n>We explore numerical schemes that convert the formulated problem into a cold-atoms friendly setup.
- Score: 0.46843765915430247
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum computers relying on cold atoms are being built and promise a high flexibility in the way information in encoded into the physical system. In particular, the analog mode is spiking interest in the field of optimization as a classically intractable number of configurations can be tackled. In this work, we investigate a problem that requires every-day scheduling of critical tasks involving a large number of actors. Namely, fixing the planning for a Earth Observation satellite fleet composed of several of units exposed to a high density of targets to be scanned. We explore numerical schemes that convert the formulated problem into a cold-atoms friendly setup. We begin by a naive formulation of the Satellite Mission Planning problem without taking account for the agility of the satellites. We then extend the problem to take it into account based on the literature. By formulating the planning problem as a Maximum Independent Set problem, we are able to solve the problem with a QPU based on Rydberg atoms. We explore two ways of solving the MIS problem on the QPU, one relying on the graphs and on the Quadratic Unconstrained Binary Optimization Framework (QUBO). We show that the QUBO methodology is the most relevant and explore it more deeply with numerical experiments. We conclude on the potential utility of using a QPU to solve the Satellite Mission Planning problem in an operational context.
Related papers
- Combinatorial optimization of connected UAV communication bridges for emergency response [1.5861469511290378]
We present a quadratic optimization problem for the strategic deployment of UAVs equipped with 5G antennas.<n>Our formulation aims to maximize signal coverage and minimize interference while ensuring network connectivity.<n>We show that the quantum adiabatic algorithm (QAA) can accurately solve small-scale instances.
arXiv Detail & Related papers (2026-09-02T13:14:12Z) - EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling [59.18180569653578]
This paper introduces EOS-Bench, a comprehensive framework for systematic and reproducible evaluation of scheduling methods.<n>By integrating high-fidelity orbital dynamics and platform constraints, EOS-Bench generates 1,390 scenarios and 13,900 benchmark instances.<n>Results show that EOS-Bench effectively distinguishes solver performance across scales and conditions.
arXiv Detail & Related papers (2026-04-28T15:49:46Z) - Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems [54.887871365121775]
Multi-robot control in cluttered environments is a challenging problem that involves complex physical constraints.<n>We propose a hybrid multi-robot control framework that jointly optimize task and motion planning.<n>Our approach consistently improves task success over motion-agnostic and VLA-based baselines.
arXiv Detail & Related papers (2026-04-22T22:58:47Z) - AstroReason-Bench: Evaluating Unified Agentic Planning across Heterogeneous Space Planning Problems [71.89040853616602]
We introduce AstroReason-Bench, a benchmark for evaluating agentic planning in Space Planning Problems (SPP)<n>AstroReason-Bench integrates multiple scheduling regimes, including ground station communication and agile Earth observation, and provides a unified agent-oriented interaction protocol.<n>We find that current agents substantially underperform specialized solvers, highlighting key limitations of generalist planning under realistic constraints.
arXiv Detail & Related papers (2026-01-16T15:02:41Z) - A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system.
We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
arXiv Detail & Related papers (2024-08-29T06:57:45Z) - Quantum Optimization Methods for Satellite Mission Planning [0.3252295747842729]
The ever-growing amount of satellites in orbit underscores the need to operate them efficiently.
Current classical algorithms often fail to find the global optimum or take too long to execute.
Here, we approach the problem from a quantum computing point of view, which offers a promising alternative.
arXiv Detail & Related papers (2024-04-08T13:36:29Z) - Quantum algorithms applied to satellite mission planning for Earth
observation [0.0]
This paper introduces a set of quantum algorithms to solve the satellite mission planning problem.
The problem is formulated as maximizing the number of high-priority tasks completed on real datasets.
A hybridized quantum-enhanced reinforcement learning agent can achieve a completion percentage of 98.5% over high-priority tasks.
arXiv Detail & Related papers (2023-02-14T16:49:25Z) - Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware.
We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
arXiv Detail & Related papers (2022-02-17T18:59:20Z) - GeoQA: A Geometric Question Answering Benchmark Towards Multimodal
Numerical Reasoning [172.36214872466707]
We focus on solving geometric problems, which requires a comprehensive understanding of textual descriptions, visual diagrams, and theorem knowledge.
We propose a Geometric Question Answering dataset GeoQA, containing 5,010 geometric problems with corresponding annotated programs.
arXiv Detail & Related papers (2021-05-30T12:34:17Z) - A Maximum Independent Set Method for Scheduling Earth Observing
Satellite Constellations [41.013477422930755]
This paper introduces a new approach for solving the satellite scheduling problem by generating an infeasibility-based graph representation of the problem.
It is tested on a scenarios of up to 10,000 requested imaging locations for the Skysat constellation of optical satellites as well as simulated constellations of up to 24 satellites.
arXiv Detail & Related papers (2020-08-15T19:32:21Z) - Image Acquisition Planning for Earth Observation Satellites with a
Quantum Annealer [0.35232085374661287]
We compare state-of-the-art classical optimisation methods to a D-Wave 2000Q quantum annealer for the planning of Earth observation missions.
We find that the solution quality of the quantum annealer is comparable to the method used operationally for small problem instances, but degrades rapidly due to the limited precision of the quantum annealer.
arXiv Detail & Related papers (2020-06-17T08:55:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.