Oliver Schiffmann, MEng

Oliver has begun his journey as a postgraduate researcher with a PhD focused on Quantum Computation for Engineering Design. Specifically, this involves an investigation into how established quantum techniques (such as Grover’s algorithm for unstructured search) might be utilised by engineering designers. 
Oliver was awarded a masters degree (Meng) in Mechanical Engineering from the University of Bristol. After a bachelor’s project studying the affordances of classical metaheuristics for combinatorial systems design, he completed a summer internship developing an alternative quantum computing methodology fostering his passion for this field.

Why Quantum Computers?

Computational methods have become indispensable tools and revolutionised the process of designing and optimising solutions. 50 years ago, Engineering Design relied heavily on manual calculations, physical prototypes, and intuitive decision-making. 
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. The design process today can be characterised as a data-driven decision-making process, where engineers are supported by comprehensive analysis, optimisation algorithms, and machine learning techniques to guide their decisions.

However, 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 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, having evaluated a set of options, it may be be useful to store the results for later analysis or selection. However, with modern hard disk drives storage capacity in the 10s of TBs we are quickly diverging from what is capable with classical computers.

These factors limit the vastness and complexity of the problems we can tackle as engineering designers. This begs the question “Are there a fundamentally different approaches to representing and resolving Engineering Design design spaces?”. The approach being examined in this PhD is quantum computing, which has emerged
as a promising method that overcomes some of the barriers faced by classical methods. The field of quantum computation remains an evolving field, within which there are a variety of techniques being developed.

Progress Made in the First Year

Interest in the potential for quantum computation in engineering design began with work conducted by the supervisors of this PhD – James Gopsill and Ben Hicks. This spawned a summer internship completed by Oliver and resulted in a conference publication “Research Questions in Applying Quantum Computing to Systems Design“. This publication provided a starting point from which Oliver’s PhD could begin. 

To answer these research questions, a stronger background in quantum information and computation was required. This was obtained through the completion of several taught courses at the University of Bristol during year 1 of the PhD. Further, self-guided exploration of two quantum SDKs, Qiskit and Ocean, was performed. This aimed to fill the practical knowledge gaps left by the academic courses.

With this fundamental knowledge in hand, an investigation into the existing literature covering quantum computing and its applications to engineering problems was conducted. This led to a categorisation of the field clarifying the avenues for further investigation, as well as highlighting the impact of the chosen quantum hardware can have on performance. It also demonstrated the relative infancy of the field of quantum computation for engineering. The combination of this field’s rapid development and the need to consider the affordances of different hardware architectures highlights the potential for drastic changes within this PhD’s timeframe.

Going Forwards

Moving into the second year of this PhD, Oliver will be looking at the following questions:

  1. What are the main (or most significant in terms of computational cost) classical computations/types of tasks we perform/attempt in the field of engineering design?
  2. Of those, what are their base problems. This means are they solving systems of linear equations, are they solving PDEs, are they combinatorial or classification problems?
  3. What quantum techniques could be applied to this problem? Are there gate-based, annealing, variational and QML methods available?
  4. Given the hardware constraints for modern NISQ machines, which of the possible quantum techniques is most appropriate for implementation now, and is this likely to change in the near future?

Answering these questions will begin with an investigation into the performance of quantum annealing for an 8×8 tiling problem. The performance will be compared against the Grover’s algorithm approach explored in previous work as well as a classical approach. The hope for this research is that it will provide an environment to learn more about how quantum algorithms can be implemented on real devices and how their performance can be benchmarked.

Academic Citizenship and Continued Professional Development

As well as the work examining the potential for quantum computers in engineering design, Oliver has led/been involved with several other projects during his first year. This can be seen most clearly in the following list of publications:


  • O. Schiffmann, B. Hicks, A. Nassehi, J. Gopsill, and M. Valero. “A Cost–Benefit Analysis Simulation for the Digitalisation of Cold Supply Chains”. In: Sensors 23.8 (2023). ıssn: 1424-8220. doı: 10.3390/s23084147


  • M. Valero, O. Schiffmann, A. Nassehi, and B. Hicks. “Digital Twin Design and Evaluation for Dynamically Optimised Distribution Strategy in Food Supply Chains: An Exploratory Case Study”. In: Proceedings of the 32nd annual Flexible Automation and Intelligent Manufacturing Conference. Porto – Portugal, June 2023
  • J. Gopsill, B. Hicks, O. Schiffmann, and A. McClenaghan. “A Sustainable Computational Design Concept Using Web Service Methods”. In: Proceedings of the Design Society 3 (2023), pp. 425–434
  • J. Gopsill, O. Schiffmann, and B. Hicks. “Research Questions in Applying Quantum Computing to Systems Design”. In: Design Computing and Cognition’22. Ed. by J. S. Gero. Cham: Springer International Publishing, 2022, pp. 735–745. ısbn: 978-3-031-20418-0. doı: 10.1007/978-3-031-20418-0_43

In Review

  • H. Felton, O. Schiffmann, M. Goudswaard, J. Gopsill, C. Snider, R. Real, A. McClenaghan, and B. Hicks. “Maker Communities and the COVID-19 Pandemic: A Longitudinal Analysis of Thingiverse’s Response to Supply Shortages”. In: Royal Society Open Science (2023). In Review

Working Drafts

  • O. Schiffmann, J. Gopsill, and B. Hicks. “A Performance Comparison Between Annealing and Gate-based Methods for Combinatorial Optimisation”. Working Draft. 2024

Last updated: 16/08/2023

If you’re interested in any of my work, please get in touch.

Email: Oliver.Schiffmann@bristol.ac.uk

ORCID: https://orcid.org/0000-0002-7103-190X

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