Quantum-Inspired Optimization for Engineering Systems

From Simulation-Driven Problems to Structured Search in Aerospace, Defense, and Space

Many real-world optimization problems in aerospace, defense, and space are black boxes. Their objective functions are evaluated through high-fidelity simulations that capture complex, non-linear system behavior. These simulations are accurate, but computationally expensive.

As a result, only a limited number of evaluations are feasible. Under such constraints, the challenge is not defining the problem but searching the decision space efficiently. Quantum-Inspired Optimization (QIO) addresses this challenge by introducing structure into otherwise opaque optimization processes.

Why Black-Box Optimization Becomes Intractable at Scale ?

Discrete Decisions, Constraints, and Hidden Correlations

In black-box settings, gradients are unavailable, and smoothness cannot be assumed. Decision variables are discrete, constrained, and often tightly coupled across subsystems. Classical optimization methods must therefore rely on heuristics, evolutionary strategies, or random sampling.

As dimensionality increases, these approaches struggle to maintain performance. Sampling becomes sparse, correlations remain undiscovered, and improvements in solution quality diminish rapidly. This creates an apparent plateau, even though better solutions may still exist.

Preprocessing as a Necessary First Step

Reducing Scale Without Improving Structure

Preprocessing reduces the effective search space before optimization begins. It filters configurations using feasibility checks, coarse performance estimates, or domain-specific thresholds.

This step is essential when simulations are expensive. By eliminating clearly suboptimal regions, preprocessing allows available evaluations to be used more effectively.

However, preprocessing alone does not address the core challenge of black-box optimization. It reduces scale, but it does not improve how the remaining space is explored or how variable dependencies are handled.

Parameterization and the Role of Correlation

Why Variable Independence Is a Poor Assumption

Correlation between decision variables is a defining characteristic of engineered systems. Choices made in one subsystem propagate through others in non-linear and often unintuitive ways.

Parameterizations that ignore these dependencies force the optimizer to rediscover correlations through sampling. This increases evaluation requirements and reduces reliability under tight budgets.

Problem-inspired parameterizations encode partial system knowledge. They reduce dimensionality and expose correlations while preserving sufficient flexibility to explore alternative configurations.

Quantum-Inspired Models

Learning Structure With and without Explicit System Models

Quantum-Inspired Optimization introduces generative models that learn probability distributions over the vast solution space. These models capture dependencies between variables that are explicitly defined in the problem formulation. However, the use of quantum machine learning models allows for the evaluation without an explicit system model.

Quantum-inspired approaches are one example. They provide a compact representation of complex correlations without requiring full system observability.  When embedded within generator-enhanced optimization frameworks, these models guide classical solvers toward more promising configurations rather than relying on uniform or random sampling.

From Random Sampling to meta-heuristic Search

How Quantum-Inspired Optimization Improves Exploration Efficiency

In quantum-inspired optimization workflows, meta-heuristic search strategies are enhanced using mathematical principles motivated by quantum mechanics. Concepts such as superposition-like representations and correlation modeling are used to express and explore large design spaces more efficiently on classical hardware.

Rather than sampling candidate solutions independently, the optimization process learns structural relationships between decision variables. New candidate solutions are generated based on this learned structure, allowing the search to focus on promising regions of the solution space.

This shifts optimization from blind or uniform exploration toward structured, guided search. As a result, evaluations are concentrated where higher-quality solutions are more likely to exist, improving outcomes under fixed computational and simulation budgets.

Importantly, quantum-inspired methods do not replace classical solvers. They act as a search enhancement layer, improving how candidate solutions are explored and proposed while leaving objective evaluation and simulation models unchanged.

Encoding and Locality in Quantum-Inspired Optimization

Why Representation Shapes Optimization Outcomes

Most quantum-inspired methods operate on binary or discrete representations. The choice of encoding determines how distances between solutions are perceived by the optimizer.

Poor encodings distort locality, making similar configurations appear unrelated. This reduces the effectiveness of both classical and quantum-inspired search strategies.

Quantum encodings preserve locality by ensuring that small changes in system parameters correspond to small changes in representation. This improves convergence behavior and reduces wasted evaluations.

Evidence from Industrial-Scale Optimization Problems

Structure Matters as Much as the Algorithm

In large-scale production planning studies, quantum-inspired optimization methods consistently matched or exceeded the performance of classical optimizers. Gains were most pronounced when intermediate levels of problem knowledge were incorporated.

These results demonstrate that optimization success depends as much on representation and structure as on algorithm choice. Methods that exploit correlations outperform those that ignore them.

Implications for Aerospace, Defense, and Space Systems

Why Structured Search Is No Longer Optional

Aerospace and defense systems are modular, hierarchical, and constraint heavy. Decisions propagate across subsystems, creating tightly coupled optimization landscapes.

As system complexity increases, structured search becomes a necessity rather than an optimization preference. Quantum-Inspired Optimization aligns naturally with these characteristics by learning structure without overfitting or hard-coding assumptions.

From Black-Box Simulation to Quantum-Inspired Decision Quality

Classical optimization challenges cannot be solved by faster solvers alone. Performance is governed by how effectively structure is revealed and exploited through preprocessing, parameterization, encoding, and extensive search.

Quantum-Inspired Optimization synthesizes these elements into a practical framework. By learning correlations and guiding exploration, it improves solution quality under realistic evaluation constraints.

Actions to Take Forward

Organizations looking to apply quantum-inspired optimization should focus on disciplined experimentation:

• Select representative optimization problems where classical optimization has plateaued
• Benchmark existing methods under fixed evaluation budgets
• Introduce structured parameterization and encoding before applying QIO
• Measure gains in solution quality, not theoretical speedups

This approach enables objective evaluation while minimizing risk. Quantum-Inspired Optimization is not a replacement for domain expertise or simulation fidelity. It is a structured way to make better use of limited computational resources.

For aerospace, defense, and space organizations facing growing system complexity, the next step is not full-scale adoption, but informed proof-of-value studies grounded in real engineering problems.