Quantum Computer Aided Engineering

Classical computation, while powerful, faces limitations in tackling the breadth and complexity of design problems. These limitations are primarily the processing speed and storage capacity of classical devices. In response, quantum computation emerges as a potential solution, promising to revolutionize engineering design through phenomena such as superposition and entanglement that can potentially resolve significantly larger design spaces and/or existing design spaces in a fraction of the time. The challenge that exists is the need to refactor Engineering Design problems to take advantage of quantum computing as well as understand what scale of quantum hardware is necessary for useful scales of Engineering Design problems to be considered.

Computation in Engineering Design

Computational methods have become indispensable tools and revolutionised the process of designing and optimizing solutions. 50 years ago, Engineering Design relied heavily on manual calculations, physical prototypes, and intuitive decision-making. Engineers meticulously performed calculations by hand, often spending countless hours computing and evaluating design alternatives. The design process was time-consuming, limited in scope, and heavily dependent on the expertise and intuition of the designers.

This reliance on analytical hand-calculated solutions has significantly diminished as tools like Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) software have become standard practices. These digital tools allow engineers to visualize, manipulate, and iterate on designs in a virtual environment, saving time, resources, and reducing the need for extensive physical testing. Moreover, engineers now have access to powerful algorithms, simulations, and modeling techniques that enable them to explore a vast design space and optimize solutions with unprecedented precision.

The design process today can be characterized as a data-driven decision-making process, where engineers are supported by comprehensive analysis, optimization algorithms, and machine learning techniques to guide their decisions. Designers now have a suite of computational methods that have become integral to Engineering Design. These methods enable designers to discover key information about their problem and its potential solutions. This information ranges from how sensitive a design is to changes in design and/or scenario parameters, what are the number of theoretical solutions, and how optimal is the current solution? The role of the designer is therefore shifting from the generation of solutions to the definition/constraint of design spaces that can then be explored computationally more quickly and to a much fuller extent.

Problems with the Classical Approach

No matter how well our computational methods perform, designers are always looking to increase the fidelity and expanse of the design space they are exploring. Never satisfied, the primary objective remains to reduce uncertainty and increase confidence in the design taken to production.

As problem complexity increases the advantage classical computation methods provide plateaus. This limitation primarily stems from the inherent complexity in computationally representing, resolving, and exploring design spaces. This increasing complexity becomes a limitation as we reach the upper end of our manufacturing process limits for classical processors. Despite increasing numbers of transistors the clock speed of classical computers is capped. Further, our ability to store the information returned by our computational tools for further analysis is limited in classical computation. With modern hard disk drives
storage capacity in the 10s of TBs we are quickly diverging from what is capable. We are limited by the vastness and complexity of problems, as shown in Fig. 1

Quantum Computation as the Solution?

These scaling challenges are well known and often encountered in Engineering Design. As a result, research has developed a variety of classical methods to help optimise the exploration of design spaces. Some examples are gradient descent methods, evolutionary algorithms, particle swarm optimisation, and generative approaches. However, these each face their own issues as design spaces become increasingly complex. For example gradient descent struggles with convergence to local optima and traversing discontinuous design spaces.

This begs the question, “Are there a fundamentally different approaches to representing and resolving Engineering Design design spaces such that they can be explored more effectively?”. Quantum Computer Aided Engineering is exploring how Quantum Computing can provide novel methods of modelling and navigating design spaces to identify optimal and robust designs to our increasingly complex design problems.

Quantum computers are computers that use quantum phenomena to represent information. The smallest unit of information represented in a quantum computer is a quantum bit – often referred to as a qubit. While classical computers process information in binary states (0 or 1), qubits can be in a state of 0, 1, or a superposition of both. This superposition (represented symbolically below) enables quantum computers to perform computations in parallel and explore multiple solutions simultaneously. Taking advantage of superposition, as well as other QC features such as entanglement, has resulted in a number of quantum algorithms for tackling computational problems. However, a gap must be bridged between the available techniques and their implementation.

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James Gopsill

Lead

Oliver Schiffmann

Research Engineer

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