[Blog] Tackling complex industrial problems with quantum computers

EQUALITY’s objective is to solidify the link between strategic European industries and the emerging quantum ecosystem, while also contributing to technologies which are critical to the green transition.

For that, the consortium brings together leading research groups, SMEs, and prominent industrial players towards the goal of developing quantum algorithms for real problems and testing them on real quantum hardware.

These problems have enormous computational requirements, forcing engineers either to use simplistic models or to rely on expensive build-and-test cycles. This is exemplified in aerodynamics, where it is more feasible to test models in a wind tunnel than solving the difficult equations involved in simulations.

Hence, the opportunity provided by quantum computers to solve such questions computationally promises a competitive edge for European industry.

Battery design and fuel cell design

Mobile and stationary energy storage systems are fundamental in our modern society, being found everywhere, from mobile phones to electric vehicles and homes. There is increasing demand for cheaper, higher-capacity, longer-lasting, and faster-charging energy storage systems made from more environmentally friendly materials.

The challenge in improving these systems lies mainly in understanding phenomena happening on multiple length scales, ranging from the microscopic scale (nm scale) up to the mesoscopic scale (µm scale) and up to the macroscopic scale (mm scale). Hence, their complete description requires a holistic theoretical framework, which consistently couples the atomistic description of the nanoscale with the continuum description of the meso- and macroscopic scales.

Such theoretical models usually consist of coupled (nonlinear) partial differential equations, which must be solved using numerical simulations. However, even for small systems with little complexity, such simulations consume large amounts of computational resources, often beyond the capabilities of classical computers.

Quantum computers offer a path forward for solving differential equations efficiently and adequately simulating these processes. EQUALITY will develop quantum mechanical solvers for differential equations to model whole batteries and fuel cells at the continuum level, providing a multiscale picture of their dynamics.

Battery materials discovery

Energy storage is crucial in the transition towards renewable energy sources and sustainable development. It is essential for increasing the use of renewable energy, improving grid stability, enabling the adoption of electric vehicles, providing backup power, and expanding access to energy in off-grid communities.

Although traditional battery materials, such as lithium-ion and lead-acid, have been widely used for many years, they have limitations, including low energy density, limited lifespan, prohibitive cost, and environmental concerns. And while the battery industry in Europe is expected to experience significant growth in the coming years, the raw materials necessary for their manufacturing are not sourced in the continent, putting it in a vulnerable position.

As such, there is a growing need to investigate and develop new battery materials to address these concerns. This complex and challenging process involves identifying new materials with the desired properties, optimising their properties for battery applications, and testing their performance in real-world conditions.

Therefore, by simulating battery behaviour at a microscopic level, it is possible to understand the underlying mechanisms that govern its performance. However, current simulation techniques impose computational trade-offs between precision and scale.

EQUALITY will investigate the use of quantum algorithms to simulate battery materials on an atomistic level, providing a significant speed-up in the calculations, higher accuracy due to more precise methods, and more accurate descriptions of processes between atomic species.

Solid Oxide Fuel Cell Optimisation

By 2050, as the aviation industry grows, it could consume up to a quarter of the global carbon budget necessary for limiting temperature rise to 1.5 degrees. To mitigate these emissions, hydrogen is considered a highly promising alternative fuel for powering aircraft, especially when produced from renewable energy sources.

Its use requires however the development of very efficient fuel cells, devices which transform the chemical energy of a fuel and an oxidising agent into electrical energy. Hydrogen-fed fuel cells are greener and easier to operate than combustion engines, yet scalable and powerful enough to replace the latter in different applications of autonomous power sources, especially in mobility.

Being efficient and fuel-versatile, the most promising candidate in this domain are the solid oxide fuel cells (SOFC). Despite that, there still is a need for improvement before they reach their full potential, particularly regarding the use of more cost-competitive materials, which require lower operating temperatures (currently at the 600-800°C range). This can be achieved through a deeper understanding of physical and chemical mechanisms driving the fuel cells to optimise the chemical compositions of the electrodes and electrolytes, as well as their microstructure.

However, commonly used computational methods lack the required level of detail for the problems at hand, while more precise methods are too computationally intensive. To gain a deeper understanding of SOFCs, novel computational chemistry techniques are necessary.

EQUALITY will develop quantum mechanical solvers for differential equations to model and optimise various aspects of SOFC fuel cells at the continuum level, providing a multiscale picture of their dynamics.

Aerodynamics simulations

The ambition of lowering aircraft emissions or even decarbonising air transport has become the key driver for the next generation of aircraft, triggering the study of innovative and disruptive concepts.

One way to propel the aviation industry towards zero emissions is optimising airplane frames to make them more energy efficient. It all depends on simulating in detail how air flows around the aircraft and the aerodynamic forces acting on its surfaces – including wings, fuselages, engines, landing gears, and many others – while optimising for frame weight, integrity, and performance.

These are called Computational Fluid Dynamic (CFD) simulations, and they allow for the numerical resolution of the complex flow physics, governed by the Navier-Stokes equations. However, the computational requirements of these simulations are enormous, since improving their results imply in increasing the level of detail and, consequently, the size of the problem to be solved. In turn, time, cost, and energy consumption increase exponentially. For that reason, these simulations are performed on supercomputer clusters, which have been pushed to their limits in complex simulation problems, such as the optimisation of a wing console shape.

Hence, to achieve notably better results, there is a need for more efficient ways to simulate fluid dynamics. EQUALITY will take advantage of the opportunity provided by quantum computers to solve critical aerospace problems by developing algorithms to increase the speed and precision of CFD simulations. On top of that, quantum computer algorithms can also be leveraged in multi-disciplinary design optimisation problems, such as that of load-bearing structures.

Mission optimisation for space

Over 900 Earth observation satellites are currently in orbit, grouped in constellations from different providers, and with different capabilities and aims. They are used, for example, for environmental monitoring, providing information on agricultural lands, forest cover, and weather events, which are essential to evaluate the present and future impact of climate change.

When a company operating one of these constellations receives a request to acquire an image of a given region on Earth at a given time, it faces a tough resource allocation problem. Which satellite should be assigned to take the job? When will it be capable of executing the request? Which manoeuvres need to be performed for successful acquisition?

This kind of mission planning is one of the critical procedures in operating such systems. It involves customer preferences and priorities, maximizing the number and quality of acquisitions, while also satisfying tight constraints on the platform manoeuvrability, onboard memory usage as well power and thermal limits.

With the huge costs involved in launching and operating new satellites, optimising these missions is critical to ensure that resources are efficiently used, and customer demands are satisfied. The complexity of this problem grows dramatically with the number of satellites, and solving the complete problem optimally takes too much time.

However, there is evidence that quantum approaches could outperform classical approaches for such optimisation problems. EQUALITY plans on developing powerful optimisation methods for quantum computers, which will be tested on satellite mission optimisation.

Space data analysis and processing

Earth observation satellites are in high demand nowadays, and the images they take are essential for several industries such as urban planning, agriculture, forestry, energy, defence, and many others, with novel applications being constantly developed thanks to advancements in computational approaches to image data processing, such as Machine Learning or Big Data.

Radar are extremely powerful tools for space imagery, thanks to their ability to deliver high-quality images regardless of weather and daylight conditions. They work by emitting a radio wave towards a target and collecting back its echo, the wave reflected on the target’s surface. By knowing the exact wave pattern sent out and the position of the sensor, it is possible to generate an image out of the raw data by measuring the time it takes for the radio wave to go from the satellite to the surface and back again.

In satellites, multiple radio signals are sent during the flight along the satellite's ground track, and various echoes are received from the area of interest. This technique is called synthetic aperture radar (SAR), and it stands out for its high resolution, which comes, however, with a high computational cost.

The raw SAR data is an overlay of complex echo patterns that must be processed by sophisticated and computing-intensive algorithms responsible for generating an image. Adding to the computational cost are the various error corrections due to distortions in the data caused by atmospheric effects and the sensor's timing and position errors.

Quantum computers have the potential to significantly accelerate the resolution of these computational issues. EQUALITY will explore the use of quantum computers to speed up the post-processing of satellite-based image acquisition by applying machine learning techniques to SAR data processing.