Quantum computing is the start of possibilities without end

Quantum chemistry is used across science and engineering fields to accurately predict properties of molecules and materials. But for it to tackle ‘real world’ problems a paradigm shift in computing is needed.

Trying to explain something difficult to grasp

When asked how he would describe his research into quantum computing to family or friends, the response of Philipp Harbach, our Head of In Silico Research is short: “I just don’t,” he says, laughing.

This is perhaps not surprising. Physics, let alone quantum mechanics, is often seen by those outside the field as the most intimidating of the science subjects. Once you move into the realm of the quantum, non-experts are often baffled by the contradictions involved.

“The most well-known phenomenological explanation in quantum mechanics is Schroedinger’s cat,” says Harbach. “But this explanation is very limiting.”

The simple version of ‘Schroedinger’s cat’ states that if you place a cat and something that could kill the cat (a radioactive atom) in a box and sealed it, you would not know if the cat was dead or alive until you opened the box. This, he suggested, meant that until the box was opened, the cat was (in a sense) both dead and alive at the same time.

This thought experiment is used as a way to describe a phenomenon known as quantum superpositions – in which a quantum system, such as a fundamental particle like an atom or photon, can exist as a combination of multiple states corresponding to different possible outcomes.

“Schroedinger’s cat was originally used by Schroedinger to demonstrate the pointlessness of trying to describe quantum mechanics in a classical way,” continues Harbach. “The problem is that there just isn’t any classical theory or ‘real-life’ example which can explain the paradoxes found in quantum mechanics. So people tend to believe that it’s like magic. No – it’s pure math.”

However, even though we can’t experience this quantum ‘strangeness’, making it difficult to grasp, we can still see its very real effects in action. Quantum superpositions are incredibly important in quantum computing, for example – which has the potential to radically change approaches in areas like chemistry, material science, drug development, artificial intelligence, and security.

Did you know?

  • 1.5

    trillion times faster is Google's quantum computer compared to a classical computer. [2]

  • 53

    is the number of qubits in the biggest gate-based quantum computers to date. [2]

  • -273

    degrees Celsius is the temperature many quantum computers operate at – almost Absolute Zero. [3]

For quantum computers it’s not a question of either or

Quantum physics describes the behavior of atoms and subatomic particles, like electrons and photons. A quantum computer operates by controlling the behavior of these particles in a way that is completely different from regular computers.

But a quantum computer is not just a more powerful version of our current computers, in the same way a light bulb is not just a more powerful candle. You can’t build a light bulb by building better and better candles.

A quantum computer is an entirely new kind of device, based on quantum physics and, in particular, the concept of quantum superpositions.

In a normal computer, data is stored in bits. A bit is a single piece of information that can exist in two states – 0 or 1. Quantum computing uses quantum bits, or 'qubits' instead. These are quantum systems with two states. However, unlike a usual bit, they can store much more information than just 0 or 1, because they can exist in any superposition of these values – 0 and 1 at the same time.

To help picture this, imagine a sphere. A classical bit can be in two states – for example at either of the two poles of the sphere. A qubit, on the other hand, can be any point on the sphere. You can see an illustration of this here.

Thanks to this counterintuitive phenomenon, a quantum computer with several qubits in superposition can crunch through a vast number of potential outcomes simultaneously. The final result of a calculation emerges only once the qubits are measured, which immediately causes their quantum state to ‘collapse’ to either 0 or 1.

This means a computer using qubits can store and process a much larger amount of quantum information, using less energy, fewer bits, and a smaller amount of space than a classical computer.

AI and drug discovery could make a quantum leap

Quantum computing has a number of exciting potential applications. A primary example is artificial intelligence (AI). AI is based on the principle of learning from experience, becoming more accurate as feedback is given until the computer program appears to exhibit ‘intelligence’.

This feedback is based on calculating the probabilities for many possible choices, and so AI is an ideal candidate for quantum computation, which will allow it to perform these calculations much faster.

For example, Lockheed Martin plans to use its D-Wave quantum computer to test autopilot software that is currently too complex for classical computers, and Google is using a quantum computer to design software that can distinguish cars from landmarks. [1]

Importantly for us, quantum computing also has the potential to transform scientific research by enabling quantum chemistry to tackle real-world systems.

The design and analysis of molecules is a challenging problem, and that's because exactly describing and calculating all the quantum properties of all the atoms in a molecule is a computationally difficult task, even for the most advanced supercomputers.

“Quantum chemistry enables us to mimic complicated experiments on a computer to understand underlying natural phenomena,” says Harbach. “This could help us do a whole range of things, from speeding up the identification of new potential drugs to making materials like solar cells more efficient. The applications are incredibly varied. But so far, although these algorithms are productive, we’re having to run them on limiting classical computing hardware.”

“The problem is that most quantum chemical problems scale exponentially with system size. And classical computers struggle to cope with this exponential scaling. Realistically, they will never enable quantum chemistry to tackle real-world systems. This intrinsic limitation can only be overcome with a technological paradigm shift, which is why quantum computing is so promising.”

And when will I have a quantum computer on my desk?

The race is on between some of the world’s biggest tech giants – Google, IBM, Microsoft – to reach ‘quantum supremacy’. A state heralded as the point at which a quantum computer can complete a mathematical calculation that is demonstrably beyond the reach of even today’s most powerful supercomputers.

In fact, a paper published by Google’s researchers in the journal Nature in October 2019 claimed they had reached quantum supremacy already, saying “A computation that would take 10,000 years on a classical supercomputer took 200 seconds on our quantum computer.”  However, IBM’s researchers were quick to argue it wasn’t actually supremacy at all. [2]

Whether or not supremacy has been reached, it doesn’t change the fact that today’s quantum machines have at best a few dozen qubits and they are often beset by computation-destroying noise. Realistically, researchers are still many years away from general-purpose, ‘fault-tolerant’ quantum computers.

However, scientists are now looking for ways to put the quantum systems already available to good use. These quantum systems – known as near-term quantum computers or noisy intermediate-scale quantum (NISQ) machines – still have great potential to start tackling problems like those faced by Harbach’s team.

“It’s obvious that the development of productive quantum computers will continue for the next few decades,” explains Harbach. “This development will be comparable to that of classical computers. But although that means we’re a little way off applying these technologies to today’s problems, we can already shape the direction.

“We’ve agreed to a three-year cooperation with start-up HQS Quantum Simulations,” he continues. “We’re working with them to develop software for quantum chemical applications which will run on near-term quantum computers.

“The partnership enables us to understand the completely unknown possibilities of quantum programming paired with quantum chemistry problems.”

HQS was the winner of our Anniversary Research Grant in Digitalization and Computing, one of the research grants we initiated in 2018 as part of our 350th anniversary. They have special expertise in enabling quantum chemistry on NISQ devices.

“The result of the partnership won’t necessarily be software which we can immediately use,” says Harbach. “Instead, it’ll be a proof of concept for the application to basic research in academia.”

In addition to the partnership with HQS, we’re also investing time and resources into quantum computing in a number of other ways, such as supplying the state-of-the-art materials that are
needed for the production of more advanced Quantum Computers.

“Our M Ventures division is investing in a number of startups,” says Harbach. “We’re also taking part in several initiatives focusing on quantum computing hardware. Meanwhile, we’ve set up scouting projects in our Innovation Center which tackle new hardware approaches that fit with our Electronics business unit and we organized an ‘Applied Quantum Conference’ in February 2020, together with the European Space Agency and GSI – a leading particle accelerator facility.”

As for the longer-term future, Harbach is confident quantum computing is here to stay.

“It will be one of the widest research field of the future,” he says. “This is just the beginning.”

Learn more about Quantum Computing and the role we’re playing on it in our Blog: https://www.emdgroup.com/en/the-future-transformation/quantum_computing.html

Hear more about Quantum Computing

Superconduction Quantum Computing: from chips to full systems. Quantum computing is poised to disrupt traditional computing methods and bring the power to tackle large optimization problems. Learn how John Langan, Daniel Franke and John Levy are addressing these challenges.

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