The business potential of quantum computing … “if it hasn’t profoundly shocked you, you haven’t understood it yet”

October 8, 2022

“If quantum mechanics hasn’t profoundly shocked you, you haven’t understood it yet.” said Niels Bohr. Some years ago as a physics student I came across that phrase, and dismissed it. Some years later, when I had grasped the real ideas and implications of quantum mechanics, compared to classical atomic theory, I realised how true it was.

Quantum computing will profoundly shock you.

If you think of a classical computer as a bunch of coins on a table, heads and tails, ones and zeros, a quantum computer is what happens when you toss all the coins up in the air at once and watch them spin.

A new report The Business Potential of Quantum Computing, by ADL, shows how the technology promises to transform business through its ability to tackle so-called intractable problems with exponentially increasing complexity that currently are beyond the reach of conventional high-performance computers

These types of problems can be grouped broadly into four categories: simulation, optimization, machine learning, and cryptography. The use cases for business across the first three categories are almost endless, from fully digitalized drug discovery and new materials development through to logistics, supply chain management, and portfolio optimization.

Given the current state of play, companies that have not already done so take steps now to ensure they are not left behind. The Quantum Computing learning slope is steep. The ADL report sets out four steps companies should take to prepare for the future, including exploring applicability, monitoring technical developments, engaging with the ecosystem, and building knowledge and capability.

Quantum computing in action

  • Aerospace: By considering an exponential number of variables, quantum computing could help determine the optimal alternative for each route. It can also help find the best way to allocate resources so that the crew and passengers are impacted as little as possible.
  • Healthcare: Harnessing the power of quantum computing can significantly accelerate the timelines of various stages of the pharmaceutical research and development process. It can help life science companies by accelerating the speed of the pre-clinical phase and reducing the cost of drug development.
  • Finance: Quantum computing will be able to help solve the problems of customers in finance institutes. It can optimize investment portfolios and financial derivatives. It can also enable the institutions to accurately characterize anomalous transactions and rapidly detect fraud.
  • Chemistry: It is likely that quantum computing can be applied to simulate the properties and behavior of new molecular structures in chemistry. It can address the probabilistic challenges of quantum mechanics. In the future, quantum computing is expected to predict molecular properties for new molecules.

Here are 3 real examples – from BMW in automotive, Speedel in logistics, Qubit in pharma.

Physical prototyping is an expensive area of R&D. Automakers spend up to 30% of the total R&D expenditure to pre-test hardware components, assemble them, and test the final product. This motivated BMW  to contract a quantum computing technology developer, Pasqal. BMW wanted to learn how to apply the new technology to metal forming. With quantum computing in place, they now perform more virtual testing and have reduced the number of tests required. This has led to a 50% cost reduction in prototyping and testing.

Speedel is a UK B2B courier firm that works with aerospace and manufacturing companies. It uses hundreds of vehicles every day on multiple routes. This means billions of possible variants in route creation. A conventional computer can’t process so many shipment simulations, so the company decided to implement quantum computing. They developed a practical app running on quantum algorithms. The app calculates all the possible options in planning and routing (yes, those billions mentioned earlier), does traffic simulations, and, finally, identifies the best option. For a fleet of hundreds of vehicles, this means huge time and money savings. Now, Speedel gets more shipments delivered in a shorter time frame.

The French company, Qubit Pharmaceuticals, uses quantum computing to model how molecules behave and interact. It’s a small research team without the resources of big pharma. However, in March 2022, they launched an ambitious program for designing novel COVID-19 treatments. What’s more, their algorithms helped discover the most suitable compounds in just six months. Qubit Pharmaceuticals tested huge data libraries to find compounds that could help fight the virus. Their solution carried out simulations with drug candidates and modeled possible chemical reactions. In the end, they discovered two novel drug candidates and brought them to a further drug development stage. The research is ongoing. Over six months, the team has shown astonishing results, given the time and cost constraints.

Neuromorphic computing

However there is more.

The neuromorphic chip is a silicon chip designed to mimic the form of the human brain. This game-changing approach transforms the way our devices process information, moving them closer to the way our brains process information.

Neural networks can now be etched into silicon not unlike the billions of neurons and synapses that form our nervous system and brain. This means software and data no longer have to be processed on different chips, they can be stored and processed on the same chip—saving significant time, energy and space. In many ways, it is a great example of how our modern world, already powered by software, is about to change in a really big way.

Neuromorphic computation and quantum computing always seemed that they were years away. The fact is commercial neuromorphic chips and quantum computers are in use today. These two new technologies are going to change what looked like a straight path to Artificial Intelligence.

  • Aerospace and defense: Neuromorphic computing architecture can help in pattern recognition, event reasoning, and robust decision-making. It can also aid in adaptive learning and autonomous tasking for energy-efficient agile Air Force platforms.
  • Self-driving cars: Similar to space communications, neuromorphic computing enhances self-driving. In imitation of the human brain, neuromorphic chips attempt to think and learn on their own and then adapt their learning to unexpected scenarios on the road.
    While conventional computers run commands sequentially, neuromorphic computers process and store data almost at the same time. This makes self-driving cars more energy efficient. It can also help autonomous vehicles learn skills and execute tasks more efficiently.
  • Healthcare: Neuromorphic platforms can be used for the hardware-based implementation of ML methods in treating Chronic Obstructive Pulmonary Disease (COPD) in home-care environments. Real-time analysis of data can be obtained by bringing data from the backend onto a neuromorphic chip. Furthermore, securing sensitive medical data on a single chip complies better with patient privacy regulations. Since neuromorphic platforms process data near a patient, it offers a large fault tolerance for medical applications. Moreover, hardware-based neuromorphic systems require less computational power making them perfect for PoC medical devices.

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