From Prototype to Production: How Volkswagen Is Using Quantum Computing
- Although the technology is still emerging, Volkswagen is investigating quantum computing because of its incredible potential to turbocharge business processes.
- In the past five years, Volkswagen has tested and successfully prototyped quantum computing solutions across different parts of the company.
- Volkswagen’s lead data scientist David Von Dollen believes areas such as logistics, the mobility space, materials design, and optimization are ripe to be optimized by quantum computing.
Automobile companies by their very nature have to be fast-moving, so it’s little wonder global automotive giant Volkswagen has long been committed to exploring new technologies.
Recognizing that mastering quantum computing takes time, in 2016 Volkswagen America tasked lead data scientist David Von Dollen with prototyping and testing different quantum computing solutions for various parts of their business.
The goal of the investigation was to apply the new technology in ways that brought value to the company. “We thought (quantum computing) had enormous potential to unlock value in logistics, and the mobility space, materials design, and optimization,” David explains.
In an episode of The Qubit Guy podcast, David discusses Volkswagen’s research into quantum computing, the lessons learned (so far), the critical synergies companies need to create value, and the essential talents required for a good quantum team.
Volkswagen’s first quantum computing project tackled traffic
The first project that the Volkswagen team looked at was traffic flow optimization with a quantum annealer.”Essentially, we were looking at taxi routes over the city of Beijing,” David explains. “We wanted to find a global configuration of routes for taxis that maximized the flow through the road network.”
In 2019, Volkswagen launched its first quantum-enabled production application. Based on the classic bus optimization problem, Volkswagen tackled route optimization in Lisbon during the annual WebSummit,which attracted tens of thousands of people to the Portuguese capital. The project demonstrated a practical application possibility for quantum computers.
The following year, David’s group continued its investigation of quantum computing by co-authoring a paper with Google on theTensorFlow Quantum, an open source library for quantum machine learning.
Possibilities and pitfalls: Using a quantum annealer for a paint shop encoding project
The team also investigated a binary paint shop encoding, leveraging QAOA (Quantum Approximate Optimization Algorithm) on gate model computers. For the project, David’s group was able to iterate with the paint shop to define their success criteria, refine the problem and the business requirements, and quickly get to the solution. Through this refinement, David explains, the problem was generalized to a “multi-car optimization for the paint shop using a quantum annealer.”
That application is currently being pushed into production. While the paint shop project was fairly seamless, David warns that the move from experimentation and prototyping into production is not always smooth.
“The central challenge is, how do you get a team that’s primarily focused on research and development push something into production?” David says. “Should that team own those production deployments? Should there be another team? Those are open questions I think organizations can answer on an individual basis.” That said, he believes it’s important to have architecture considerations, authentication, and a good testing strategy in place.
Lessons learned: The importance of synergy between business and IT teams
For companies thinking about rolling quantum into their enterprise, David says a central challenge to the project’s success is the need for “real synergy between business and IT teams to understand the business problem that’s being solved.”
Companies that crack the code on collaboration, David says, will be a couple of steps ahead in the game. “At the end of the day, the problem is going to determine whether or not a quantum computer is applicable, as well as the value that you can create with the quantum computer.”
What makes a great quantum team?
In addition to having learned about the importance of synergy between business and IT teams, David also points to the ingredients necessary for a good quantum team. An excellent quantum team will include:
1) Hardware technologies experts.
It is critical to have somebody who has an understanding of all the hardware technologies and can understand the strengths and limitations. “For example, different types of QPUs and the embeddings of a gate model,” David says. “ How long are the coherence times? What are the maximum qubit values that we can use? Those types of things that will allow the team to kind of understand the strengths and weaknesses of different hardware.”
2) Software engineers.
Software engineers are key “because as you’re developing solutions you may need to develop a UI or figure out how to push the quantum service into production.”
3) Assorted experts.
Describing his team, David says “we have people who are focusing on optimization, and quantum machine learning … and other people are looking at quantum chemistry and material simulation. So I think, building up experts in those areas, as well as some generalists that you can train up and can grow within the team is important.”
4) One business-facing role.
Finally, David believes having a business analyst or team lead “can help the business understand how to augment their process and derive value with the technology.”
Quantum Computing Support: Next Steps For Your Company
To support a business’s move into quantum computing, David believes middleware could be a powerful solution. “Developing a good middleware that can allow an engineer the ability to design, test and evaluate different algorithms quickly while switching out backends” will be important, he says.
Training is also key. “It’s gotten easier and easier for people to get into quantum computing. There are more and more resources available for people to learn about the canonical algorithms, how to compose circuits from gates, (and to learn) what the strengths and limitations are with the hardware,” David says, adding that organizations need to allow for upskilling.
He believes we will continue to see innovations in quantum machine learning and new applications of quantum computing across different business domains in the next year.
He expects new hardware and software platforms to emerge. “I’m hopeful that as we move through this hype cycle, we (will) create lasting things we can learn from and bring up a generation of scientists that will eventually contribute to solutions that we may not even know exist today.”