In the classical domain, optimization problems are defined as mathematical problems that involve finding the best solution from a set of possible solutions. The goal of optimization is to find the solution that maximizes or minimizes an objective function—a measure of success—subject to a set of constraints limiting the allowable solutions.

Aerospace engineering is a field that grapples with immense complexity. The design of efficient aircraft and planning intricate space missions involve many variables and potential solutions, making optimization a complex process.  The numerous variables involved, such as wing shape and materials, create a high-dimensional optimization space. Classical algorithms, with their limited exploration capabilities, often get stuck in "good but not best" solutions.

Typically, classical optimization algorithms rely on searching through the space of all possible solutions, evaluating each solution until the best one is found. This process can be time-consuming and inefficient, especially for problems with many variables or constraints, resulting in limited exploration of the design space and getting trapped in local minima.    

As a result, engineers in aerospace engineering face the need to make educated guesses to effectively optimize design.

Quantum Computing Principles and QIEO Algorithms

Quantum computing principles are inherently well suited to the task of solving optimization problems thanks to the key phenomena of superposition and entanglement. Superposition refers to the ability of qubits—the basic building blocks of quantum computers—to exist in multiple states of 0 & 1 simultaneously, enabling parallelism and faster data processing.   

The QIEO (Quantum Inspired Evolutionary Optimization) Algorithms developed by BQP leverages the principles of Quantum Computing to offer exponentially faster exploration and more efficient solutions to specific industrial challenges that classical computers struggle to address.  

• Enhanced Exploration: QIEO explores a much wider range of possibilities, significantly increasing the chance of finding the global optimum (the absolute best solution) or high-quality local minima.  
  
• Breaking Free from Local Minima: QIEO explores a broader search space, and does not get trapped in local minima enabling solutions that classical methods might miss.  
  
• Faster Design Exploration: Quantum algorithms require fewer iterations for simulations, accelerating development cycles.  
  
• Multidimensional Design Space: Qubits enable efficient exploration of complex design possibilities, leading to more innovative solutions. 

Real World Applications with QIEO for the Aerospace Industry  

QIEO has demonstrated remarkable potential in optimizing diverse aspects of aerospace engineering, from aircraft design to space mission planning. By harnessing the power of quantum-inspired computing principles, QIEO can effectively navigate the complex design optimization required in aerospace applications.

Design Optimization

In general, we build what we can compute. The inability to numerically measure or compute turbulence at flight conditions correctly has contributed to transport aircraft being largely derivatives of 707s since the late 50s.  

Quantum-Inspired Evolutionary Optimization (QIEO) serves as a robust solution for design optimization in aerospace engineering. QIEO can optimize aircraft designs for fuel efficiency, identifying configurations that minimize fuel consumption to promote greener aviation practices and reduce operational costs.

In addition to fuel efficiency, QIEO excels in optimizing aerodynamic design and material selection to enhance performance metrics such as range, payload capacity, and maneuverability.

Multi-objective optimization, effectively balancing various design goals like fuel efficiency, performance, and cost-effectiveness, is possible with QIEO, providing a holistic approach to aircraft design optimization. 

Optimizing Flight paths

Coordinating multiple aircraft to efficiently transport passengers to various destinations while minimizing the number of flights and time required presents a significant challenge in the airline industry. Similarly, creating flight plans that reduce fuel consumption is essential in an industry with tight profit margins where a 5% improvement in routing could lead to exponential savings of fuel costs 

This is where Quantum Machine Learning Algorithms using QIEO come in. QIEO identifies the most fuel-efficient flight paths, minimizing fuel costs and environmental impact. Dynamic route adjustments based on weather or air traffic control changes can additionally be integrated. For passengers, QIEO can potentially optimize travel routes that combine air travel with other transportation modes for seamless journeys.  

Modelling Fuel Cells for SAF (Sustainable Aviation Fuel)

Fuel cells are very difficult to model with simulations using classical algorithms and every material and geometry cannot be physically tested for structural integrity. QIEO could enable the shape and size for fuel cells enabling hydrogen-powered aviation (or alternative SAF) with completely different capabilities, not possible today.  

A breakthrough solution could lead to the development of aircraft with extended range, increased payload capacity, and reduced environmental impact, transforming the future of aviation and space exploration 

Optimization applications for Space Missions

QIEO's optimization capabilities significantly enhance satellite operations by facilitating efficient orbit planning, streamlining downlink scheduling, and enabling the coordination of multiple satellites for enhanced data collection and observation capabilities. This results in more effective and productive satellite missions that can unlock valuable insights and drive advancements in various fields, including earth observation, climate monitoring, disaster management, and scientific research. 

No more Trade-offs with QIEO

In practice, the algorithm choice depends on the size and structure of the problem instance and the desired trade-off between solution quality and computation time. For small problem instances, exact methods may be preferred, as they can guarantee optimality. However, for large problem instances, exact methods may become computationally infeasible, and approximate methods are what engineers rely on to find a good-quality solution within a reasonable amount of time. With the QIEO approach, this could soon be a thing of the past.