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Astrodynamics

Transcription and optimization of an interplanetary trajectory through quantum annealing

Federico De Grossi, Andrea Carbone, Dario Spiller, Daniele Ottaviani, Riccardo Mengoni, Christian Circi

Published Apr 1, 2025• 5 citations
Publisher

Abstract

Abstract This study employed a quantum-annealing framework to solve spacecraft trajectory optimization problems. Quantum annealing belongs to the field of quantum computing and is a promising technique for tackling hard binary optimization problems by employing quantum annealers. To address the optimal control of a trajectory using quantum annealing, a transcription procedure was introduced to express the problem in the binary optimization form required. The proposed procedure leverages the pseudospectral method to discretize the trajectory and represents the dynamical constraints as algebraic equality constraints at specific nodes. Subsequently, both a linearization procedure and binary representation strategy for the real-valued variables of the problem were presented, leading to the quadratic binary unconstrained optimization form. The quantum-annealing-based method was tested in the context of an interplanetary low-thrust transfer from the Earth to Mars. First, we discussed which instances of the problem, especially in terms of their dimensions, are implementable on currently available quantum annealers; then, a solution was sought by employing annealers from D-Wave systems. Solutions from hybrid solvers that combine classical and quantum resources, and fully quantum solvers were explored. The results demonstrate the validity of the transcription approach, demonstrate the ability of the hybrid solver to tackle the case-study problem, and highlight the promising features and current limitations of practical trajectory optimization with quantum annealing.

DOI
10.1007/s42064-024-0216-6

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Abstract This study employed a quantum-annealing framework to solve spacecraft trajectory optimization problems. Quantum annealing belongs to the field of quantum computing and is a promising technique for tackling hard binary optimization problems by employing quantum annealers. To address the optimal control of a trajectory using quantum annealing, a transcription procedure was introduced to express the problem in the binary optimization form required. The proposed procedure leverages the pseudospectral method to discretize the trajectory and represents the dynamical constraints as algebraic equality constraints at specific nodes. Subsequently, both a linearization procedure and binary representation strategy for the real-valued variables of the problem were presented, leading to the quadratic binary unconstrained optimization form. The quantum-annealing-based method was tested in the context of an interplanetary low-thrust transfer from the Earth to Mars. First, we discussed which instances of the problem, especially in terms of their dimensions, are implementable on currently available quantum annealers; then, a solution was sought by employing annealers from D-Wave systems. Solutions from hybrid solvers that combine classical and quantum resources, and fully quantum solvers were explored. The results demonstrate the validity of the transcription approach, demonstrate the ability of the hybrid solver to tackle the case-study problem, and highlight the promising features and current limitations of practical trajectory optimization with quantum annealing.
Low-thrust trajectory optimization is a central task in interplanetary mission design, but its nonlinear dynamics and operational constraints often lead to challenging non-convex optimal-control problems. Sequential convex programming has emerged as an effective approach to address these problems, while quantum annealing offers a complementary paradigm for solving quadratic unconstrained binary optimization problems. This paper introduces quSCP, a quantum-based sequential convex programming framework that reformulates each convex subproblem as a quadratic unconstrained binary optimization problem suitable for quantum and hybrid quantum--classical solvers. Equality, inequality, and trust-region constraints are embedded through quadratic penalty terms, while an iterative refinement strategy is used to reduce the accuracy loss introduced by binary discretization. The method is assessed on a fuel-optimal Earth--Mars low-thrust transfer by comparing standard sequential convex programming, a continuous quadratic unconstrained formulation, direct quantum processing unit sampling, and D-Wave hybrid solvers. Results show that quSCP produces physically consistent trajectories with propellant consumption and nonlinear constraint violations close to classical benchmarks. Direct quantum annealing is feasible only for small instances because of embedding overhead and hardware connectivity limits, whereas hybrid solvers scale to larger discretizations. Although no computational quantum advantage is demonstrated with current hardware, the results show that quantum and hybrid quantum--classical optimization can already provide competitive solutions for demanding trajectory design problems.
This paper introduces an innovative approach to explore the capabilities of Quantum Annealing (QA) for trajectory optimization in dynamic systems. The proposed method involves transforming trajectory optimization problems into equivalent binary optimization problems using Quadratic Unconstrained Binary Optimization (QUBO) representation. The procedure is general and adaptable, making it applicable to a wide range of optimal control problems that entail the satisfaction of dynamic, boundary, and path constraints. Specifically, the trajectory is discretized and approximated using polynomials. In contrast to the conventional approach of determining the polynomial degree (n) solely based on the number of boundary conditions, a specific factor is introduced in our method to augment the polynomial degree. As a result, the ultimate polynomial degree is calculated as a composite of two components: n = l + (m−1), where m denotes the count of boundary conditions and l signifies the number of independent variables. By leveraging inverse dynamics, the control required to follow the approximated trajectory can be determined as a linear function of independent variables l. As a result, the optimization function, which is represented by the integral of the square of the control, can be formulated as a QUBO problem and the QA is employed to find the optimal binary solutions.
This paper explores the application of Quantum Annealing (QA) to the field of combinatorial optimization. The core idea is to leverage the principles of quantum mechanics, specifically quantum tunneling, to efficiently solve complex optimization problems. We present a framework for utilizing QA to tackle problems represented as quadratic unconstrained binary optimization (QUBO) models. The methodology focuses on mapping the problem's constraints and objective function into a form suitable for QA hardware. We discuss the challenges involved in this mapping process, including the limitations of current QA devices and the need for careful problem formulation. Furthermore, we analyze the potential advantages of QA over classical optimization techniques, particularly concerning the exploration of a wider solution space. The research highlights the promise of QACO as a viable approach for tackling computationally demanding combinatorial optimization tasks, offering a novel method to enhance optimization efficiency.
Quantum annealing emerges as a promising approach for tackling complex scheduling problems such as the resource-constrained project scheduling problem (RCPSP). This study represents the first application of quantum annealing to solve the RCPSP, analyzing 12 well-known mixed integer linear programming (MILP) formulations and converting the most qubit-efficient one into a quadratic unconstrained binary optimization (QUBO) model. We then solve this model using the D-wave advantage 6.3 quantum annealer, comparing its performance against classical computer solvers. Our results indicate significant potential, particularly for small to medium-sized instances. Further, we introduce time-to-target and Atos Q-score metrics to evaluate the effectiveness of quantum annealing and reverse quantum annealing. The paper also explores advanced quantum optimization techniques, such as customized anneal schedules, enhancing our understanding and application of quantum computing in operations research.
Cybersecurity in telecommunication networks often leads to hard combinatorial optimization problems that are challenging to solve with classical methods. This work investigates the practical feasibility of using quantum annealing to address the Restricted Vertex Minimum Multicut Problem. The problem is formulated as a Quadratic Unconstrained Binary Optimization model and implemented on D-Wave s quantum annealer. Rather than focusing on solution quality alone, we analyze key aspects of the quantum workflow including minor embedding techniques, chain length, topology constraints, chain strength selection, unembedding procedures, and postprocessing. Our results show that quantum annealing faces substantial hardware-level constraints limitations in embedding and scalability, especially for large instances, while hybrid quantum-classical solvers provide improved feasibility. This study offers a realistic assessment of the D-Wave system s current capabilities and identifies crucial parameters that govern the success of quantum optimization in cybersecurity-related network problems.
In this study, we explore Quantum Annealing (QA) as a new method for determining the optimal observation schedule for the radar MARSIS onboard the Mars Express mission, with the aim of obtaining a high-quality coverage of the South Pole of Mars. The MARSIS observation plan is greatly influenced by space weather and it is constrained by the limited hardware of the Mars Express spacecraft. In this work, we build upon previous research on classical optimization algorithms and present a novel Quadratic Unconstrained Binary Optimization (QUBO) formulation, tailored for execution on the D-Wave quantum annealer. We test the solution of the QUBO formulation through QA on small-scale instances of the problem, derived from real data provided by the MARSIS operation center. We also study the effects of the penalties introduced in the QUBO and compare QA with classical algorithms, drawing some conclusion on the current applicability of this new technology.