Why Ford Is Taking a Quantum Leap into the Future of Computing

By Dr. Ken Washington, Vice President, Ford Research and Advanced Engineering, and Chief Technology Officer

We live in an era where technology breakthroughs are paving the way for major changes in the way we move. Electrification is changing how we power our cars and self-driving vehicles will change how we get around, but there are other game-changing technologies that — if we can properly wrap our minds around them — hold great potential to transform our transportation systems as well.

One of these is quantum computing, which enables us to process great amounts of information much faster than we can with traditional computers today. We’re always striving at Ford to explore the potential of new technology, and this is just one of several areas in advanced computing that’s creating new opportunities in how we process, manage and store information.

So while we’re still in the discovery phase when it comes to quantum computing, we know enough to believe that its potential can help us solve real problems that affect the people and businesses using our vehicles. That’s why we’ve hired our own quantum specialists and are beginning to collaborate with experts in the field.

One of our first collaborations in this space is with NASA. We’ll be working with experts there to better understand how we can frame problems in a way that yields benefits from quantum computing.

Over the next year, we’ll be working with NASA’s Quantum Artificial Intelligence Laboratory at its Ames Research Center in Silicon Valley. We’ll be using the quantum annealer hosted at Ames — which is shared between NASA, Google and the Universities Space Research Association — to see how we can apply the technology to complex problems that cannot be solved by today’s computers.

In the scenario we’re testing with NASA, we’re exploring quantum computing’s ability to help commercial fleet owners more efficiently manage the total energy consumption of their large number of vehicles. The scenario involves designing criteria-based vehicle-to-route mappings for diesel delivery vehicles.

Diesel engines have particulate filters that must be managed for overall efficient operation of the vehicle and environmental compliance, which is achieved when the vehicles are operating in the most optimal drive cycles. In some delivery situations, it’s difficult for vehicles to achieve the right drive cycles due to traffic flow patterns and speed changes, which impacts the efficiency of the filter management process and ultimately, engine performance. So our scenario entails finding the optimal route for a single delivery vehicle making stops at multiple locations carrying out a specific task, then applying that to all vehicles in the fleet.

As you would expect, a large number of complex variables come into play. When working to design a scheduling system with multiple pick-up and drop-off spots, any location you choose will have an impact on congestion, time management and people’s experiences.

Right now, using traditional computing methods, you can model these options for a limited number of vehicles and locations. But as soon as you increase the number of vehicles, relevant locations and potential routes, it quickly becomes a very large and expensive problem, and the number of different scenarios needing to be analyzed becomes intractable.

So this is where quantum computing comes in. Unlike traditional computing methods, which only translate information into a 1 or a 0, a quantum computer can translate information into multiple states. It can even understand something as being in multiple states at the same time, so instead of a computer bit that is either a 1 or a 0, a quantum bit can be both at once.

If this is all rather mind-blowing, think of it like the difference between a light bulb that’s either on or off, versus a light bulb that can be dimmed. It all boils down to the fact that we can store more information in a quantum bit, or qubit, than we can a traditional computer bit, and process all of it simultaneously.

Our work with NASA entails encoding all these options into qubits to simulate the efficiency of each to determine the best fit. So in our scenario, all available options in terms of number of locations, density of locations and route timing can be thought of as different states, or measurements. Because qubits can process so much information all at once, we believe such complex problems as fleet route planning can be solved faster relative to computing methods that rely on traditional bits.

Beyond route planning, we believe quantum computing can make an impact in a number of other areas as the technology evolves, including materials development, manufacturing and battery chemistry optimization.

Albert Einstein told us curiosity has its own reason for existing. “The important thing is not to stop questioning,” he said. For us, working with NASA creates an opportunity to learn more about the potential of quantum computing to identify quantum-level problems to ensure we know how to design the proper scenarios to put to the test. Once we understand the right way to ask questions in a quantum framework, there’s no telling the power we’ll have to solve potential problems in the future as we work to transform our transportation systems.