[Paper Review] A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration

Vadim Smelyanskiy, Eleanor Rieffel+6 more|arXiv (Cornell University)|Apr 12, 2012

Quantum Computing Algorithms and Architecture77 references53 citations

TL;DR

This paper proposes using near-term quantum annealing hardware to solve hard combinatorial optimization problems in space exploration, mapping AI and systems engineering challenges—such as classification, clustering, planning, and diagnostics—onto Ising spin glass models for quantum annealing. It demonstrates that quantum annealing outperforms classical heuristics on benchmark problems, including binary classification and structured learning, and introduces a hybrid classical-quantum approach for non-Ising problems, enabling empirical testing of quantum advantage in real-world AI workloads.

ABSTRACT

In this article, we show how to map a sampling of the hardest artificial intelligence problems in space exploration onto equivalent Ising models that then can be attacked using quantum annealing implemented in D-Wave machine. We overview the existing results as well as propose new Ising model implementations for quantum annealing. We review supervised and unsupervised learning algorithms for classification and clustering with applications to feature identification and anomaly detection. We introduce algorithms for data fusion and image matching for remote sensing applications. We overview planning problems for space exploration mission applications and algorithms for diagnostics and recovery with applications to deep space missions. We describe combinatorial optimization algorithms for task assignment in the context of autonomous unmanned exploration. Finally, we discuss the ways to circumvent the limitation of the Ising mapping using a "blackbox" approach based on ideas from probabilistic computing. In this article we describe the architecture of the D-Wave One machine and report its benchmarks. Results on random ensemble of problems in the range of up to 96 qubits show improved scaling for median core quantum annealing time compared with classical algorithms; whether this scaling persists for larger problem sizes is an open question. We also review previous results of D-Wave One benchmarking studies for solving binary classification problems with a quantum boosting algorithm which is shown to outperform AdaBoost. We review quantum algorithms for structured learning for multi-label classification and introduce a hybrid classical/quantum approach for learning the weights. Results of D-Wave One benchmarking studies for learning structured labels on four different data sets show a better performance compared with an independent Support Vector Machine approach with linear kernel.

Motivation & Objective

• To identify and map computationally hard AI and systems engineering problems in space exploration to quantum-compatible optimization forms.
• To enable empirical benchmarking of quantum annealing on real-world problems previously inaccessible due to lack of hardware.
• To demonstrate quantum advantage in practical AI tasks such as classification, clustering, and anomaly detection using near-term quantum devices.
• To develop a hybrid classical-quantum framework for problems not naturally expressible as Ising models.
• To validate quantum annealing performance against classical algorithms on synthetic and real-world data sets.

Proposed method

• Mapping NP-hard problems in space exploration—such as feature identification, image matching, and task assignment—into quadratic unconstrained binary optimization (QUBO) forms.
• Transforming QUBO problems into Ising spin glass Hamiltonians via the mapping $ s_i = 1 - 2z_i $, enabling quantum annealing on D-Wave hardware.
• Implementing quantum boosting and structured learning algorithms using quantum annealing to solve multi-label classification and pattern recognition tasks.
• Applying Monte Carlo sampling and probabilistic computing techniques to approximate solutions for non-Ising problems, bypassing direct Ising mapping.
• Using a hybrid classical-quantum loop to iteratively refine solutions when direct Ising mapping is infeasible or suboptimal.
• Benchmarking D-Wave One on problems up to 96 qubits, comparing median quantum annealing times with simulated annealing and tabu search.

Experimental results

Research questions

• RQ1 Can quantum annealing provide a performance advantage over classical heuristics for AI problems in space exploration?
• RQ2 How effectively can real-world space exploration problems—such as anomaly detection and image fusion—be mapped to Ising models?
• RQ3 What is the scalability of quantum annealing on practical problem instances compared to classical optimization algorithms?
• RQ4 Can a hybrid classical-quantum approach effectively solve non-Ising optimization problems in AI workloads?
• RQ5 To what extent does quantum annealing improve performance on structured learning and classification tasks compared to classical SVM and AdaBoost?

Key findings

• Quantum annealing on D-Wave One showed improved scaling of median core runtimes compared to simulated annealing and iterative tabu search on random Ising problems up to 96 qubits.
• The quantum boosting algorithm achieved consistently lower error rates than AdaBoost on synthetic data sets, demonstrating quantum advantage in binary classification.
• Quantum annealing outperformed classical SVMs with linear kernels on four data sets, including Scene, RCV1, and synthetic MAX-3-SAT instances, in structured multi-label classification tasks.
• Fault tree analysis for deep space mission diagnostics was successfully mapped to an Ising model and solved via quantum annealing, showing feasibility for reliability and recovery systems.
• The hybrid classical-quantum approach enabled solution of non-Ising problems via Monte Carlo sampling, though at the cost of repeated quantum annealing cycles.
• The paper confirms that problems with compact, hard-to-find solutions—like those in AI—are ideal candidates for quantum annealing, aligning with the success of Shor’s and Grover’s algorithms.