Exploring the Future of Transportation! Airbus, BMW kick off new round of "Quantum Computing Challenge"

The challenge is the first of its kind, bringing together two global industry leaders to use quantum technology to enable real-world industrial applications, unlocking the potential for more efficient, sustainable and safer solutions for the future of transportation.

"There has never been a better time to focus on quantum technology and its potential impact on our society. Working with industry leaders like the BMW Group allows us to mature this technology as we need to bridge the gap between scientific exploration and its potential applications." Isabell Gradert, Airbus Vice President Central Research and Technology, said, "We are looking for the best students, PhDs, academics, researchers, start-ups, companies or professionals in the field worldwide to join us in our challenge to bring about a huge paradigm shift in the way airplanes are built and flown ."

Dr. Peter Lehnert, Vice President Research Technology at the BMW Group, said, "We are preparing for a new wave of innovation and exploring technological capabilities for sustainability and operational excellence. The BMW Group has a clear goal to position itself at the intersection of quantum technologies, global ecosystems and cutting-edge solutions."

"By doing so, we are convinced that significant progress will be made in areas such as sustainable materials for batteries and fuel cells, unique and efficient designs or enhancing the overall user experience of BMW Group products."

Quantum computing has the potential to significantly increase computing power and enable the most complex operations that challenge even the best computers available today. Particularly for data-driven industries such as transportation, this emerging technology could play a key role in simulating a wide range of industrial and operational processes, providing opportunities to shape the transportation products and services of the future.

Challenge candidates may choose one or more problem statements:

- Improving aerodynamic design using quantum solvers
- Enabling the future of automated transportation using quantum machine learning
- Enabling a more sustainable supply chain using quantum optimization
- Enhancing corrosion mitigation using quantum simulation

In addition, candidates can propose their own quantum technologies and potentially develop native applications that have not yet been developed in the transportation field.

The list of challenges this time includes:

1) Smart coatings: investigating quantum computing for corrosion inhibition

Mitigating surface degradation processes is essential to extend the lifetime of Airbus and BMW Group products, improve operational efficiency, optimize maintenance schedules and ultimately reduce costs. The surface degradation process begins when mechanical damage in the coating exposes the underlying aluminum to the surrounding environment. In smart coating materials, an inhibitor is embedded in the coating matrix and forms a protective layer that stops aluminum degradation after damage.

The goal of this challenge is to simulate the process of adsorption of the inhibitor on the aluminum surface and to understand its binding properties using quantum methods.

2) Quantum-driven logistics: Towards an efficient and sustainable supply chain

Transportation and logistics between production sites significantly increases CO2 emissions and reduces industrial efficiency and costs. In particular, the complexity of transportation products such as automobiles and airplanes leads to a high degree of supply chain complexity. Both Airbus and the BMW Group are committed to reducing CO2 emissions and ensuring a reliable and efficient supply chain for their manufacturing processes.

The goal of this use case is to develop quantum solutions to manufacturing supply chain problems, taking into account the constraints driving the application.

3) Quantum-enhanced autonomy: generative AI for enhancing images of critical test scenarios

The future of automated transportation will rely heavily on reliable and safe AI vision systems, which are an essential component of not only automated vehicle driving, but also automated aircraft landing. In order to achieve the highest level of safety, it is necessary to obtain representative image datasets dedicated to key test scenarios. These scenarios include poor visibility at night as well as inclement weather, intricate traffic patterns, and obstacles on runways. Quantum computers have potential advantages over classical computers in addressing such challenges.

The focus of this problem statement is to generate images containing critical scenarios using quantum generative modeling techniques.

4) Quantum Solver: predictive aeroacoustic and aerodynamic modeling

The ability to accurately predict aerodynamic flow and acoustic wave propagation is a key capability for the transportation industry and is highly relevant to both the automotive and aviation sectors. With this capability, it is possible to develop high quality products with superior performance, e.g. reducing noise pollution and carbon emissions. In this context, it is of utmost importance to solve partial differential equations describing multiscale problems with millions of degrees of freedom, for which the available computational power of high-performance computing systems is limited.

The focus of this problem statement is to find the most suitable quantum-based methods to solve the relevant aerodynamic and acoustic equations.

5) The Golden App (The Golden App): pushing quantum technology into transportation applications

The typical approach is to push quantum technologies towards the most driving challenges, but these challenges are often not quantum-native! Airbus and the BMW Group, leaders in the automotive and aerospace sectors, respectively, are committed to quantum technologies as early adopters of their innovation strategies. While quantum computing seems to have gained a foothold in various fields, the companies say, "We are still wondering if the 'golden app' for mobility is still yet to come."

"In this challenge, we invite you to propose novel hardware and software solutions that you believe have great potential, but whose relevance to the mobile space remains unproven."

Organized by The Quantum Insider (TQI), the challenge is divided into two parts: a four-month first phase in which participants will develop a theoretical framework for one of the given statements; and a second phase in which finalists will implement and benchmark their solutions.

Amazon Web Services (AWS) is offering candidates the opportunity to run algorithms on its Amazon Braket quantum computing service.

A jury of the world's leading quantum experts, in collaboration with experts from Airbus, BMW Group and AWS, will evaluate the submitted proposals and award a prize of €30,000 to the winning team by the end of 2024 in each of the five challenges.
 

Ever since Oliver Wright and Orville Wright first flew an airplane in 1903, mankind has seen it as their mission to take to the skies and conquer what many previously thought was impossible.

Airplanes have come a long way since the airborne days of Kitty Hawk, North Carolina. As of 2018, the aerospace and defense (A&D) industry has generated a record $760 billion in revenue. This explains precisely why quantum computing (QC) is now aligning itself with that industry.

Quantum research is already underway in the field, looking at ways to improve the physics of flight. As an active user of advanced computing solutions, Airbus is at the forefront of a paradigm shift in computing, exploring how quantum computing (QC) can help solve key problems in the aerospace industry. Airbus is taking a step forward by launching a global quantum computing competition, challenging experts in the field and working together to usher in the quantum era in aerospace.

In fact, Airbus, one of the world's two aviation giants, has been working on quantum computing for years.

In 2015, Airbus established a quantum computing research and development team.In 2016, Airbus Ventures, the venture capital arm of Airbus, made its first investment in quantum computing startup QC Ware.

Starting in 2019, Airbus launched the Global Quantum Computing Challenge. The inaugural Challenge invited more than 800 researchers from 36 quantum computing teams around the world to solve several challenges in the physics of flight.

The physics of flight is a broad name for all scientific and engineering aspects related to airplane flight, which encompasses many computationally difficult problems, such as those controlled by complex differential equations. Quantum computers are expected to take aeronautical computing power to a new level as conventional computers inch closer to their limits.

Not to be outdone, U.S. aerospace giant Boeing has started its own Airbus Challenge with a new organization called Disruptive Computing and Networks (DC&N). The organization will be based in California and, like Airbus, will focus on how disruptive technologies such as quantum computing can improve its industry.

In October, researchers from Boeing and IBM Quantum teamed up to publish a new paper in Nature's npj Quantum Information that develops new quantum computing methods for studying the chemical reactions that corrode metals in aviation - which could be an early step toward creating new corrosion-resistant materials.

Quantum algorithm results and hardware experiments

Link to paper:

Quantum computing in the automotive industry is much closer to our daily lives.

Every driver has had the experience that when a traffic jam occurs, the first thing most drivers do is look through their satellite navigation system to identify alternative routes to avoid the traffic jam. However, many drivers see the same alternative route, and before long that road also becomes congested, and eventually no one is able to avoid the traffic jam. 

In an ideal world, if there were multiple alternative routes, there would be the same number of cars on each route, rather than all cars taking the same alternative route, to ensure that at least twice as many cars arrive at their destination on time. 

The problem is that typical satellite navigation systems rely on classical computing techniques to recalculate routes. Selecting the best route out of 3 routes for 1 car is simple because there are only 9 route combinations. However, there will be 60,000 combinations for 10 cars, and hundreds of millions of route combinations for 20 and 30 cars, 3.5 billion and 20 trillion respectively. 

If the optimal route is calculated for each vehicle, classical computing is unlikely to accomplish the task in effective time, whereas the speedup can be exponential with a quantum computer. 

Denso (Denso), a leading Japanese automotive parts manufacturer, did just that. in 2020, Denso used quantum computing to optimize the routes of thousands of motorists in Bangkok, Thailand, one of the world's most congested cities, in order to minimize congestion.

In fact, Denso is not the only company in the automotive industry involved in quantum computing; the world's two largest automotive groups, Volkswagen and Toyota, as well as BMW, Ford, Daimler, and Bosch also have years of quantum computing experience. For automakers, route optimization is not the only reason they are involved in quantum computing; they can also use quantum chemistry simulations to create better car batteries, and combine quantum computing, artificial intelligence, and big data to study self-driving technology.

A McKinsey report suggests that by 2035, the total value of the quantum computing market will be between $32 billion and $52 billion. 1/10th of that value will come from the automotive industry, and by 2030, the economic impact of related technologies on the automotive industry will be between $2 billion and $3 billion.

The automotive industry is now in a once-in-a-century period of change, with four major trends set to transform the global automotive industry, known as CASE.

The four letters of CASE stand for Connected, Autonomous, Share & Service, and Electrification, which was first proposed by Daimler and is now a common goal for the development of the automotive industry. 

In recent years, major automakers have accelerated their electrification strategies. Volkswagen Group proposes that by 2030, all models will be fully electrified; Toyota proposes that by 2025, all models will be electrically driven (or at least hybridized); Daimler proposes that by 2030, sales of electric vehicles (including all-electric vehicles and plug-in hybrids) will account for more than half of the group's total sales. This process will see quantum computing play to its strengths in chemical simulation, which, as mentioned, several automakers are working to utilize to develop better-performing batteries. 

Autonomous driving requires the joint efforts of more technological fields, in which networking is a prerequisite for the realization of autonomous driving, in the future, all self-driving cars will be connected to each other, sharing relevant data and information, including their own driving experience. In addition, autonomous driving relies on advances in sensor technology and increased computing power. 

The development of Advanced Driver Assistance Systems (ADAS) and autonomous driving technologies generates large amounts of data. OEMs and technology partners need to capture and analyze large amounts of unstructured data from test vehicles equipped with LiDAR, radar, video and other sensors.

As the amount of data continues to grow, the need for capacity and speed is skyrocketing. For example a test vehicle equipped with 3-4 cameras, 3-4 LIDAR sensors and other sensors will generate 10-20 terabytes of data per hour. Even a small test fleet of 10 vehicles driving 8 hours a day could generate more than 1EB (ten billion billion bytes) of data over 3-5 years. 

Autonomous driving requires enormous computing power, hence the need for quantum computing intervention. Quantum computing can greatly reduce the time for deep learning while helping autonomous driving with route optimization.

According to McKinsey's report, quantum computing will permeate every aspect of the automotive industry. Specific industry applications can be divided into three phases:

From 2020 to 2025, near-term opportunities for QC are likely to emerge in product development and R&D. Relevant use cases will primarily involve solving simple optimization problems or parallel data processing involving simple quantum artificial intelligence/machine learning (AI/ML) algorithms. 

These quantum computing applications will be executed as part of a hybrid solution, simply outsourcing problems that are difficult for classical computers to solve to quantum computers. Possible optimization use cases include logistics portfolio optimization, and traffic route optimization. Quantum AI/ML may involve efficient training of self-driving algorithms due to increased parallel processing of large amounts of data. 

From 2025 to 2030, medium-term opportunities are likely to focus on the following:

- Quantum simulation. Areas of focus will include simulating complex partial differential problems, and it will also become important to simulate material properties at the atomic level, for example, to improve the selection and development of materials for batteries and fuel cells. 

- More complex optimization problems. Examples include minimizing the likelihood of supply chain defaults, optimizing citywide traffic flows, and solving large-scale intermodal fleet routing problems.

- Complex Quantum AI/ML. these applications will be able to process much larger amounts of data. 

In the long term (if all goes well) from 2030 onwards, quantum computing applications will be based on large-scale access to general-purpose quantum computers. By then, the Shor algorithm for cracking public-key ciphers will be universally available.

The focus may shift to digital security as well as risk mitigation as players try to stop quantum hacking of autonomous driving, in-vehicle electronics and industrial IoT communications.