Solving real-world problems with quantum computing

Moore’s Law is dead. Enter quantum computing. In the maze of emerging technologies — be it artificial intelligence (AI), blockchain, internet of things (IoT), machine learning, and so forth — quantum computing is considered to be the worthy heir of Moore’s Law, finding ways for breakthrough advances in computing beyond transistor doubling. Quantum computing is taking off… literally right now. It will become a technological game-changer, that one technology that will exponentially boost and accelerate the development and deployment of artificial intelligence systems and machine learning algorithms, just to mention a few.

Without digging too deep into the theory and mechanical phenomena of quantum computing one big question exists: what is it that quantum computers will actually be better at than classic binary computers? To put it bluntly, quantum computers can run (some) calculations exponentially faster than today’s conventional binary computers. The reason for this is that while today’s conventional computers compute using binary digits (bits) in ONLY one of two states, either 0 or 1, quantum computers, in contrast, use quantum bits (qubits) that can exist in the states 0 or 1 or any superposition state between 0 and 1. These qubits have a property called entanglement, which means that all the qubits in the system are connected with each other in a fundamental way. When we change the conditions of one qubit in our computer, it could possibly affect all other qubits in our computer without any additional work. This makes quantum computers extremely powerful, provided that we can write algorithms that exploit these quantum properties. We can solve a million computations in parallel whereas as a classic binary computer can only solve one at a time. For complex, computationally-intensive problems that need to be solved in real-time, in many cases, it’s only quantum computers that are up to the task. This is obviously deeply technical. Suffice to say, it’s also incredibly powerful stuff!

So far, only a handful of companies have entered the race to explore the power of quantum computing. Within the automotive industry, the Volkswagen (VW) Group spearheads the quantum computing technology. Their experts design intelligent, quantum-assisted solutions for the future of (urban) mobility, amongst others. The solutions that they are developing are intrinsically tied to a time-critical world, a world where people require to commute in a digitized city from ‘A’ to ‘B’ during rush hour in a minimum amount of time.

But why is this problem predestined to be solved with quantum computing? In simple terms, it’s time criticality. Within fractions of a second — in real-time — we demand (close-to) optimum solutions for the shortest-time commuting routes. Urban traffic itself adds to the complexity of the problem, i.e., the continuous redistribution of position data for all the vehicles that are present in the dense urban road network.

To find the optimum solutions for this time-critical optimization task, the VW experts broke the problem down into three distinct steps. First, the demand spots need to be predicted, i.e., how many people will demand a vehicle to commute from/to another place per time interval. Demand spot prediction can still be solved using a conventional computer running a predictive machine learning algorithm. Ideally, the prediction happens 15 to 30 minutes before the demand occurs. Second, all available vehicles must be assigned to the demand spots in such a way that the waiting time to start the commute from ‘A’ to ‘B’ will be minimal for each person. Third, the travel time itself must be minimized for each person. This involves proactive traffic flow optimization to prevent traffic jams to form. Real-time vehicle assignment to different routes and speed limit assignment are key to quantum-assisted traffic flow optimization.

On the whole, the complexity of the commute optimization task is mainly due to the three-time components processing, waiting and commuting time. Quantum-assistance, however, helps to cope with time criticality and to find solutions for real-time mobility demand prediction and traffic flow optimization. These optimum solutions can be used “[…] to improve the life of people and their mobility — in any city in the world,” said Dr. Martin Hofmann, Chief Information Officer of the Volkswagen Group. In March 2017, Volkswagen’s first successful research project was completed on a quantum computer to optimize traffic flow for 10,000 taxis in the Chinese megacity Beijing.

And Volkswagen’s next quantum solutions are just off the starting blocks, too, such as air resistance minimization of car exterior mirrors, simulations of new materials for high-performance batteries in electric vehicles, reinforcement learning for self-driving cars, or — maybe more of an unconventional one — the real-time prediction of ideal escape routes for large scale emergency evacuation such as in the event of a tsunami.

Whereas the first three quantum-assisted solutions strictly tie to Volkswagen’s core business of developing and manufacturing cars, it might be solutions like traffic flow or escape route optimization which is even more interesting to explore further: how will a company like Volkswagen monetize their knowledge and capabilities for emerging technology such as quantum computing? What quantum computing business models exist for Volkswagen to capture or exchange value?

Let’s get back to the example of intelligent, quantum-assisted traffic flow optimization by having a look into an imaginary business model of Volkswagen.

First things first, who might the customer segments for this solution be? Some of the paying customers, most presumably, will be cities, municipalities, public venues such as stadiums, exhibitions, conferences, public transport, and — in the future — operators with fleets of fully-autonomous vehicles. They will pay for predictive and truly real-time, cloud-based solutions for traffic flow optimization that will come either in the form of quantum software as a service (qSaaS), or a quantum platform as a service (qPaaS).

With qSaaS, the user will gain instant access to off-the-shelf, ready-to-use mobile and web-based applications for specific services. The quantum software and user data are provided online — “on the cloud” — and hosted on a server. The most favorable pricing option for qSaaS might be a tiered subscription with payment plans that are based on the number of available services. As an example, three payment plans might be established with a platinum tier as the full-blown version with advanced control features and capabilities, comprising of hotspot demand prediction as well as vehicle, route and speed limit assignment. The two lower plans, namely a silver and base tier, might include a limited set of services and features. In addition, pay-per-update or -upgrade options can be offered. Of course, updates and upgrades will be complimentary for the platinum tier alongside with a 24/7 customer support.

The qPaaS offering will provide a cloud-based platform with scalable, highly available applications, or middleware (APIs), that are built into the platform with special software components. This platform would include an operating system, a programming language execution environment, a database, web servers, etc. Developers can customize the pre-coded quantum algorithms and quantum computing models and test, manage, and deploy them on the platform over the cloud. The qPaaS offering might be priced based upon data consumption either in the form of a tiered subscription with pre-defined tiers for compute instances, data storage, and outbound network traffic or simply as a pay-per-use option.

Of course, the entire business model for quantum-assisted traffic flow optimization is only an imaginary one and, hence, full of assumptions such as the revenue streams for qSaaS and qPaaS, which are only options (of many) at this point. All the assumptions around the value propositions and pricing must be validated. This can only be accomplished through an iterative process in close collaboration with the prospective customers to test assumptions and hypotheses for the identification of the customers’ willingness to pay for the quantum-enhanced, cloud-based service offerings. For the qSaaS tiered subscription model, for instance, the number of tiers must be identified and also the content and price per individual tier. User testing and pricing validation also needs to address the extent to which the end users, namely drivers and riders, are willing to pay for quantum-assisted traffic solutions since in the current business model all the services are provided to the end users for free. Further questions might arise in terms of capturing real-time traffic data with a high level of accuracy: will a collaboration with a 5G network provider solve the problem? And, of course, who’s the go-to-partner to provide access to quantum computers where the computations will be run on?

From a technological standpoint, a variety of challenges are still to overcome as well, e.g. building the programming languages for quantum computers or, more on the hardware side, the refrigeration of quantum chips. However, looking back into the 1950s, the early days of today’s conventional computers, a huge bucket of challenges was to overcome, too, and in the course of time, computer developers and experts were able to cope with any of these. We should therefore not dare to look too far into the future, imagining all the opportunities that quantum computing will open up for us. And who knows better than one of the leading experts in the field, Asst. Prof. Dr. Florian Neukart, Principal Scientist at the Volkswagen Group of America (VWGoA).

Florian, your work with Volkswagen in the field of quantum computing gets more and more traction. Can you tell us why a traditional auto manufacturer takes the leap, stepping into quantum computing and exploring its vast opportunities?
[Florian]: “Our efforts with quantum computing demonstrate that Volkswagen is much larger than just an auto manufacturer, we go far beyond putting cars on the road only. We have a multitude of challenges to overcome, and our services span the entire technological spectrum. Physically building a car is an engineering problem. Scheduling deliveries of cars from factories to dealerships is a logistical problem. Designing better batteries for electric cars is a chemistry problem. Organizing warehouses for part storage is an optimization problem. The list goes on endlessly, and the IT and R&D infrastructure within Volkswagen need to support all these different areas. This is exactly where quantum computing fits in Volkswagen, helping to solve all these complex problems with cutting edge technology. We aren’t simply interested in theoretical questions about algorithms and complexity. We need to know how these computers work on a fundamental level, and we also look into the future to see what specific areas of our business they can impact. When researchers talk about a new quantum algorithm, we look at our business and ask ourselves: where can this help us? Can we use this to organize our factories better? Can we now simulate new materials we weren’t able to before? Those are the kind of questions that drive us. And we are convinced we can.”

That’s awesome. Can you tell us a bit more about the vision of Volkswagen in terms of the capabilities you are building for quantum computing?
[Florian]: “At Volkswagen, we have invested deeply in quantum computing to position ourselves for the future at the very forefront of the industry. We have to be ready today for the technology of tomorrow. Over time, it has become clear that quantum technology holds enormous potential. However, the truth about quantum computing is that it’s too early to exactly know what the first “killer application” will be. No matter what field this “killer application” of quantum computing will come from, at Volkswagen we must find a way to use it so that everyone will benefit. We can use our optimization solutions for smart mobility platforms that will reduce traffic congestion in large cities. Quantum machine learning algorithms can revolutionize autonomous cars, making them safer and more reliable at a lower cost. With new material simulations, we can research better components for our products, reducing our waste production, creating environmental impact and fostering sustainability. After decades of research, we are finally reaching the point where quantum computers leave the research labs and become accessible to industrial early adopters. For us at Volkswagen it’s not enough to be only the users of this technology, we need to become experts in it to help ourselves and others accelerate the growth of the quantum computing community. At Volkswagen, we are heading towards thought leadership in quantum computing. We create long-term partnerships with industry leaders such as Google and D-Wave Systems. Together we work towards developing the tools and strategies needed to incorporate quantum computing into our existing infrastructure.

Internally, we are constantly learning and developing. We work closely with experts in related fields to fully understand their needs. We connect quantum computing to different branches within Volkswagen which helps our company to grow, and we assess where the technology can be most effective. We know that these quantum devices, although small today, are capable of doing extraordinary things in a fundamentally different way. We will continue to showcase their use in industrial applications like we have done with mobility optimization and materials research, while we already prepare for the quantum computers of the future. Whatever path leads in the end to the quantum computing revolution, Volkswagen is committed to leading the way.”

So for the moment, where does the quantum computing technology stand and where will the technology be heading towards?
[Florian]: “The state of quantum computing now is similar to that of computers in the 1950s. There are no standard operating systems, no programming languages or compilers. The work being done now by quantum computing researchers, both on the hardware and software side, is laying the groundwork for future users of these devices. The industry of quantum computing is ripe for developers at the moment, people who are eager to understand the technology at low levels, and help build the stack on which future applications will sit on. The main challenge for the coming years is to come up with techniques that intelligently leverage the quantum resource that’s provided by these devices. At Volkswagen, we are contributing to this in many different aspects.
With our collaborators at Google, we’ve been working on solving complex optimization problems with a significantly different approach. We are researching methods to compile quantum circuits in a way that exploits the inner structure of these optimization problems to compress the circuits. This will allow us to make even more efficient use of quantum computers when solving these problems, much like compilers work in regular computers today. We can then train these quantum circuits to solve problems like traffic flow, quantum machine learning, and high-dimensional optimization of car parts. While many of these projects are small relative to the size of our company, the knowledge and experience they provide help us prepare Volkswagen for the arrival of bigger quantum computers in the future.”

If you are interested in hearing more about the quantum computing technology, its use cases and connected quantum business models with tons of insights from the expert and from Volkswagen, watch out for the video interview that Florian and I will publish soon… Stay tuned!

And, of course, if you are ready to take the quantum leap with your own business models, we, at BMI, are here to help you!

read original article at https://medium.com/@bmi_blog/solving-real-world-problems-with-quantum-computing-94bcf94c2006?source=rss——artificial_intelligence-5