December 6th,Airbus and the BMW Group have jointly launched a global quantum computing challenge called "Quantum Transportation Exploration".to address the most pressing challenges in aviation and automotive—challenges that remain insurmountable for traditional computers.
This first-of-its-kind challenge brings together two global industry leaders to harness quantum technology for real-world industrial applications, unlocking the potential to deliver more efficient, sustainable and safer solutions for the future of transportation.
There's never been a better time to focus on quantum technology and its potential impact on our society. Partnering with an industry leader like the BMW Group allows us to mature this technology because we need to bridge the gap between scientific exploration and its potential applications. Isabell Gradert, vice president of research and technology at Airbus, said: ".We are looking for the best students, PhDs, scholars, researchers, start-ups, companies or professionals in the field across the globe to join us in the challenge and bring about a huge paradigm shift in the way aircraft are built and flew.
Dr. Peter Leinert, Vice President Research Technology of BMW GroupPeter Lehnert said: "We are gearing up for a new wave of innovation and exploring technical capabilities for sustainability and operational excellence. The BMW Group's goal is clear: 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 improving the overall user experience of BMW Group products
Quantum computing has the potential to dramatically increase computing power and enable the most complex operations that challenge even today's best computers. Especially for data-driven industries such as transportation, this emerging technology can play a key role in simulating a variety of industrial and operational processes, providing opportunities to shape the future of transportation products and services.
Challenge candidates may select one or more problem statements:
Improving aerodynamic design with quantum solvers - Leveraging quantum machine Xi for the future of autonomous transportation – Leveraging quantum optimization for more sustainable chains – Enhancing corrosion inhibition with quantum simulation.
In addition,Candidates can also come up with their own quantum technologies and potentially develop untapped local applications in the transportation sector
The list of challenges includes:
1) Smart Coatings: Research on quantum computing for corrosion inhibition
Slowing down the surface degradation process is essential to extend the service life 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 after damage that prevents the aluminum from degrading.
The goal of this challenge is to simulate the process of adsorption of inhibitors on aluminum surfaces and to understand their binding properties using quantum methods.
Schematic diagrams describing the various widely used computational chemistry techniques, categorized according to their computational cost (n roughly represents the size of the system) and precision (measured in arbitrary units).
General characteristics of commonly used quantum chemistry methods.
2) Quantum-driven logistics: Achieving an efficient and sustainable ** chain
Transport and logistics between production sites significantly increase CO2 emissions and reduce industrial efficiency and costs. In particular, the complexity of transportation products such as cars and airplanes leads to a high level of complexity in the ** chain. Both Airbus and the BMW Group are committed to reducing CO2 emissions and ensuring a reliable and efficient ** chain for their manufacturing processes.
The goal of this use case is to develop a quantum solution to the manufacturing chain problem, taking into account the constraints that drive the application.
3) Quantum-enhanced autonomy: generative AI that augments images of key test scenarios
The future of autonomous transportation will rely heavily on reliable, safe AI vision systems, which are an essential component not only for autonomous vehicle driving, but also for aircraft to land automatically. In order to achieve the highest level of security, it is necessary to obtain a representative image dataset dedicated to critical test scenarios. These scenarios include poor visibility at night as well as bad weather, intricate traffic patterns, and obstacles on the runway. Quantum computers have potential advantages over classical computers in addressing such challenges.
The focus of this problem statement is to utilize quantum generative modeling techniques to generate images that contain key scenarios.
4) Quantum solver :* aeroacoustic and aerodynamic modeling
The ability to accurately** aerodynamic flow and sound wave propagation is a critical capability in the transportation industry and is highly relevant to both the automotive and aerospace sectors. With this capability, it is possible to develop high-quality products with excellent performance, such as reducing noise pollution and carbon emissions. In this case, the most important thing is to solve partial differential equations that describe multiscale problems with millions of degrees of freedom, and the available computing power of high-performance computing systems is limited.
The focus of this problem statement is to find the most suitable quantum-based method to solve the relevant aerodynamic and acoustic equations.
Generic features of the most widely used numerical methods.
5) The Golden App: Advancing the use of quantum technology in transportation
The typical approach is to push quantum technology to the most driving challenges, but these challenges are often not quantum-native!Airbus AG and BMW Group are leaders in the automotive and aerospace sectors, respectively, and they are committed to using quantum technology as an early adopter of their innovation strategy. While quantum computing seems to have gained a foothold in various fields, the companies said, "We're still wondering if the 'best applications' in the mobile space are still unavailable." ”
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 to be proven. ”
Hosted by The Quantum Insider (TQI), the challenge is divided into two parts: the first phase is four months long, in which participants will develop a theoretical framework for one of the given statements;In the second phase, finalists will implement and benchmark their solutions.
Amazon Web Services (AWS) offers candidates the opportunity to run algorithms on their Amazon Braket quantum computing service.
A judging panel of world-leading quantum experts will work with experts from Airbus, the BMW Group, and AWS to evaluate the submitted proposalsAnd by the end of 2024, a prize of €30,000 will be awarded to the winning team in each of the five challenges
Registration is now open and can be submitted from mid-January to April 30, 2024
Ever since Oliver Wright and Orville Wright first flew airplanes in 1903, humans have made it their mission to take to the skies to conquer what many previously thought was impossible.
Airplanes have come a long way since the days in the air in Kitty Hawk, North Carolina. As of 2018, the aerospace and defense industry (A&D) has revenues of a record $760 billion. This is precisely why quantum computing (QC) is now aligned with the industry.
Quantum research in this area is already underway, looking at how 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 race that challenges experts in the field to usher in the quantum era in aerospace.
In fact, Airbus, one of the world's two largest aviation giants, has been in the field of quantum computing for many years.
In 2015, Airbus established a quantum computing R&D team. In 2016, Airbus Ventures, the venture capital arm of Airbus, made its first investment in quantum computing startup QC Ware.
Since 2019, Airbus has 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 major problems in flight physics.
Flight physics is a broad name for all the scientific and engineering aspects related to aircraft flight and encompasses many computationally difficult problems, such as those governed by complex differential equations. As traditional computers approach their limits, quantum computers are expected to take aeronautical computing power to the next level.
Not to be outdone, the US aerospace giant Boeing has set up a new organization called Disruptive Computing and Networks (DC&N) to begin its own Airbus challenge. The organization, which will be based in California, will share Airbus' 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 article in the journal Nature NPJ Quantum Information, developing a new quantum computing method for studying the chemistry of metal corrosion in aviation — an early step toward creating new corrosion-resistant materials.
Results of quantum algorithms and hardware experiments.
*Link: Whatever happens, these two aviation giants clearly see a niche in the market, and they are using their money and influence to bring aerospace to the quantum age.
Quantum computing in the automotive industry is closer to our daily lives.
Every driver has the experience that when a traffic jam occurs, the first thing most drivers do is to use a satellite navigation system to identify alternative routes to avoid traffic jams. However, many drivers see the same alternative route, and soon the road becomes congested as well, and in the end no one is able to avoid the traffic jam.
In an ideal world, if there were multiple alternate routes, there would be the same number of cars on each route, rather than all cars taking the same alternate route, to ensure that at least twice as many cars would arrive at their destination on time.
The problem is that a typical satellite navigation system relies on classical computing techniques to recalculate the route. Choosing 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 impossible to accomplish in a valid time, whereas it can be exponentially accelerated by quantum computers
This is what Denso, a well-known Japanese auto parts manufacturer, has done. 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, to minimize congestion.
In December 2019, Ford partnered with Microsoft to test a traffic routing algorithm using "quantum inspire" technology, though not a true quantum computer, to reduce traffic in Seattle by 73 percent and commute times by 8 percent in a simulation of 5,000 cars
In fact, DENSO is not the only company involved in quantum computing in the automotive industry, as the world's two largest automotive groups, Volkswagen and Toyota, as well as BMW, Ford, Daimler, and Bosch, also have many years of experience in quantum computing. For automakers, route optimization is not the only reason they are involved in quantum computing, they can also use quantum chemistry simulations to make better car batteries, and combine quantum computing, artificial intelligence, and big data to study autonomous driving technology.
According to a McKinsey report, the total value of the quantum computing market will be between $32 billion and $52 billion by 2035. 1 10 of the value comes from the automotive industry, and the economic impact of related technologies on the automotive industry will reach $2 billion to $3 billion by 2030.
Quantum computing can facilitate improvements across the automotive value chain.
Now, the automotive industry is in a once-in-a-century period of change, and four major trends will change the global automotive industry, namely CASE.
The four letters "CASE" stand for Connected, Autonomous, Share & Service, and Electric. CASE 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. The Volkswagen Group has announced that all its models will be electrified by 2030Toyota has proposed that by 2025, all models will be electrically driven (at least hybrid).Daimler has set out that by 2030, electric vehicles (including all-electric vehicles and plug-in hybrids) will account for more than half of the group's total sales. In this process, quantum computing will play to its advantages in chemical simulation, and as mentioned earlier, several automakers are working on using quantum computing to develop better performing batteries.
Autonomous driving requires the joint efforts of more technical fields, where networking is a prerequisite for autonomous driving, and in the future, all autonomous vehicles will be connected to each other and share relevant data and information, including their respective driving experiences. In addition, autonomous driving relies on the development of sensor technology and the improvement of computing power.
The development of advanced driver assistance systems (ADAS) and autonomous driving technology generates a large amount of data. OEMs and technology partners need to capture and analyze large amounts of unstructured data from test vehicles equipped with lidar, radar,** and other sensors.
There are 6 levels of autonomous driving
As the volume of data continues to increase, so does the need for capacity and speed. For example, a test vehicle equipped with 3-4 cameras, 3-4 lidar sensors, and other sensors will generate 10-20TB of data per hour. Even a small test fleet of 10 vehicles, driving 8 hours a day can generate more than 1EB (exabytes) of data over 3-5 years.
Autonomous driving requires enormous computing power, hence the intervention of quantum computing. Quantum computing can greatly reduce the time to learn Xi at depth, while helping autonomous driving to optimize routes.
According to a McKinsey report, quantum computing will permeate every aspect of the automotive industry. Specific industry applications can be divided into three stages:
From 2020 to 2025, near-term opportunities for QC are likely to be in product development and R&D. The use cases will primarily involve solving simple optimization problems, or parallel data processing involving simple quantum artificial intelligence machine Xi (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. Possible optimization use cases include logistics portfolio optimization, traffic route optimization. Due to the increase in parallel processing of large amounts of data, quantum AI ML may involve efficient training of autonomous driving algorithms.
From 2025 to 2030, medium-term opportunities are likely to focus on the following:
- Quantum simulation. Areas of focus will include modeling complex partial differential problems, and simulating material properties at the atomic level will also become important, for example, to improve the selection and development of materials for batteries and fuel cells.
- More complex optimization problems. For example, minimizing the possibility of chain defaults, optimizing city-wide traffic flows, and solving large-scale multimodal fleet routing problems.
- Complex Quantum AI ML. These applications will be able to handle larger amounts of data.
In the long term, (if all goes well) quantum computing applications will be built on mass access to general-purpose quantum computers from 2030 onwards. At that time, the shor algorithm for cracking public key cryptography will be universally available.
As players try to stop quantum hacking attacks on autonomous driving, in-vehicle electronics, and IIoT communications, the focus is likely to shift to digital security and risk mitigation.