Agile Earth Observation Satellite Scheduling With a Quantum Annealer

Abstract

We present a comparison study of state-of-the-art classical optimisation methods to a D-Wave 2000Q quantum annealer for the planning of Earth observation missions. The problem is to acquire high value images while obeying the attitude manoeuvring constraint of the satellite. In order to investigate close to real-world problems, we created benchmark problems by simulating realistic scenarios. Our results show that a tuned quantum annealing approach can run faster than a classical exact solver for some of the problem instances. Moreover, we find that the solution quality of the quantum annealer is comparable to the heuristic method used operationally for small problem instances, but degrades rapidly due to the limited precision of the quantum annealer.

Quantum-inspired optimization for wavelength assignment

“…Among existing methods to achieve such dynamics, quantum annealing offers physical implementations of a non-trivial size [8]. Quantum annealing is by now explored for analysis of various areas, such as chemistry calculations [9,10], lattice protein folding [11,12], genome assembly [13,14], solving polynomial systems of equations for engineering applications [15] and linear equations for regression [15], portfolio optimization [16][17][18][19], forecasting crashes [20], finding optimal trading trajectories [21], optimal arbitrage opportunities [22], optimal feature selection in credit scoring [23], foreign exchange reserves management [24], traffic optimization [25][26][27], scheduling [28][29][30][31][32][33], railway conflict management [32,33], and many others [5]. Advances also include the recent experimental demonstration of a super-linear quantum speedup in finding exact solutions for the hardest maximum independent set graphs [34].…”

Towards Finding an Optimal Flight Gate Assignment on a Digital Quantum Computer

“…The FGA problem is a quadratic assignment problem [14] with additional constraints, as typical for real-world applica-tions. Previous works mainly investigated the solution of the FGA problem [13] and related problems [15][16][17] with quantum annealers. Here, the constraints are incorporated into an unconstrained cost function by penalty terms.…”

“…Here, the constraints are incorporated into an unconstrained cost function by penalty terms. These approaches have a number of disadvantages, one of which is the typically exponentially small subspace of valid solutions in the entire Hilbert space [17]. One method for mitigating this issue is to constrain the algorithm to only search the feasible subspace.…”

Quantum-inspired optimization for wavelength assignment

“…Among existing methods to achieve such dynamics, quantum annealing offers physical implementations of a non-trivial size [8]. Quantum annealing is by now explored for analysis of various areas, such as chemistry calculations [9,10], lattice protein folding [11,12], genome assembly [13,14], solving polynomial systems of equations for engineering applications [15] and linear equations for regression [15], portfolio optimization [16][17][18][19], forecasting crashes [20], finding optimal trading trajectories [21], optimal arbitrage opportunities [22], optimal feature selection in credit scoring [23], foreign exchange reserves management [24], traffic optimization [25][26][27], scheduling [28][29][30][31][32][33], railway conflict management [32,33], and many others [5]. Advances also include the recent experimental demonstration of a superlinear quantum speedup in finding exact solutions for the hardest maximum independent set graphs [34].…”