the two major challenges of quantum computation

The advent of artificial intelligence (AI) raises major ethical issues for society. The quantity is likely to do the same.

When applying algorithms to sensitive topics – eg HR data or fairness – one of the requirements is that you must be able to explain how to get the result. No question of having black boxes recommending this or that option without further arguments.

If we still have to wait several years before we really see operational quantum computation, the prospect of its “superiority” raises the same type of question.

In fact, it will quickly become impossible to simulate, on a classical computer, the algorithms performed on a quantum material worthy of the name. And knowing the astronomical amount of parameters a quantum computer can consume to search for an answer, how could a human being solder its depths to determine if a calculation makes sense or not?

“If a quantum computer can effectively solve a problem, can it convince an observer that its solution is correct?” Asks Marc Carrel-Billiard, Head of Global Technology Innovation at Accenture.

Already relevant use cases intended for quantum calculation

Quantum computing does not exist yet, but we know how it will behave, and researchers understand better and better in which areas it can be used in a relevant way.

Heike Riel, Head of Science and Technology at IBM and Head of IBM Research Quantum Europe, explains that “it’s not just a matter of technological beauty. We seek to create value. It’s a journey: Develop technology, find the most suitable phase-advance applications, demonstrate and prove that value, and then develop the hardware and software. ”

The journey has already begun, for example, with Eon Energy, which has joined the IBM Quantum Network. The transition to greener sources, such as solar and wind, has multiplied the types of energy to be managed by an electricity grid. Quantum computers can help optimize these networks if companies and many households in the future become producers of electricity through their own photovoltaic systems or electric vehicles, thanks to initiatives such as the Vehicle to Grid (V2G) project from Aeon.

“It’s a journey: developing technology, finding the most suitable applications at an early stage, presenting and proving value, and then developing hardware and software. »

Heike RielHead of Science and Technology at IBM

In this project, the batteries in electric vehicles are connected to the network as flexible storage media. This will make it possible to offset fluctuations in the production of renewable energy sources. Quantum computing would drive these processes more efficiently and effectively.

“All these sources have different characteristics; forecasts are becoming more complex, ”remembers Heike Riel. “You have to optimize the system in real time. However, the complexity grows exponentially with the number of parameters, to end up being a problem that is difficult to solve using conventional data processing. »

Another example of application in science, the theoretical physicist and chief scientist at Cambridge Quantum Computing, Bob Coecke assures that the behavior of atoms and molecules – which are governed by the laws of quantum mechanics – must be modeled on a quantum computer controlled by the same laws.

“In view of the complex function of quantum mechanics, a physical matter simulates [au niveau moléculaire] are more and more expensive, ”he explains. In fact, just in terms of storage, he explains, it would be impossible to adapt a traditional computer to that kind of problem.

Simulation of new materials and modeling of particle behavior are two of the largest applications envisaged for quantum computers. In August 2021, Nicholas Rubin and Charles Neill, two researchers at Google AI Quantum, wrote a blog post about an experiment aimed at creating a complex chemical simulation using a Hartree-Fock model from computational physics.

“An accurate numerical prediction [sur] chemical processes, based on the laws of quantum mechanics that govern them, can open new avenues in chemistry and help to improve a wide range of industries, ”the researchers write.

Reliability of results and quantum noise

However, these promises come with their share of challenges. For example, the two Google researchers find that their algorithms are still hampered by the high error rate of the first quantum computers.

“If a quantum computer can effectively solve a problem, can it then convince an observer that its solution is correct?”

Marc Carrel-BilliardHead of Global Technology Innovation at Accenture.

Like the ability of classical neural networks to tolerate imperfections in the data, the pair explains that in their experiment, the VQE algorithm (for Variational Quantum Eigensolver) attempts to optimize the parameters of a quantum circuit to reduce the “noise” that interferes with algorithm.

IBM is working on the same issue. With few qubits, it is still possible to simply verify the result of a quantum algorithm on a quantum computer by comparing it with the result of this same algorithm on a classical machine, which simulates the behavior of the quantum computer.

But the method is only possible as long as the number of qubits remains low enough, says IBM’s Heike Riel. The key point here is to understand how the “noise” from many qubits affects the system by producing erroneous results.

Today, IBM continues its roadmap with a 128-qubit system and wants to “show evidence that error correction can work”, says Heike Riel, “we are working to verify the results”.


Mark Mattingley-Scott, CEO of Europe at producer Quantum Brilliance, raises another challenge: Explanation.

“This is one of the paradoxes of quantum computers. When we reach the stage where it is useful – when a quantum algorithm can perform calculations at a speed and with an accuracy that is impossible with a classical computer – it becomes impossible to directly verify the accuracy of The results achieved ”, he sums up.

“We can check the correctness of the process on reduced versions of the same problem that we do every day with classical algorithms, but there will be no way to ‘check’ as such. »

But quantum computation is fundamentally non-deterministic. Mark Mattingley-Scott therefore insists that the results obtained are based on probabilities. “A quantum algorithm works by using a quantum mechanism that constructively amplifies the ‘right’ response and destructively suppresses the ‘wrong’ response,” he explains. But this construction remains the fruit of probability. “There is therefore always some uncertainty. And using a classical computer to validate a quantum computer is only possible at the methodological level, not at the data level itself. »

“It’s a paradox in quantum computation. When it becomes useful, it also becomes impossible to directly verify the accuracy of its results.

Mark Mattingley-ScottManaging Director Europe at Quantum Brilliance

Bob Coecke from the specialist company Cambridge Quantum Computing, for his part, believes that the composition principle and the theory of categories can help to understand what happens in a quantum computer.

The Belgian researcher explained this idea in a book written with Aleks Kissinger (“Picture quantum processes”). From a general point of view, the book looks at how to divide large quantum problems into smaller components. According to Bob Coecke, these “little blocks” are more understandable and verifiable.

Similarly, the team of Mark Carrel-Billiard from Accenture is working on how to map certain problems in subsets of math problems. These “sub-problems” are then coded with SDKs and libraries from multiple quantum platforms. By testing the programs on different quantum hardware architectures, it becomes so theoretically possible to determine whether they give consistent results.

In some cases, validation can also be performed “in vivo”. In chemistry, Michael Biercuk, CEO and founder of Q-CTRL, explains, for example, that “for a molecular structure or for chemical dynamics calculated on a quantum computer, it may not be possible to validate the calculation of the simulation itself. On the other side we can do a real chemical experiment [ou une analyse comparative avec des molécules connues] to validate the results. »

To understand or not to understand, that is the question

Quantum calculation will also remain an approach among others. “If you have a complex optimization problem to solve, it does not matter how or what type of computer you use, as long as you get a result in the fastest and most efficient way,” assures Heike Riel of IBM.

From IBM’s perspective, a complex computational problem often has several different parts. Some will be processed with quantum data processing, others with classical data processing.

And even in the first case, an understanding of quantum mechanics will not necessarily be necessary. Once the basics are laid out, “you need a model developer who does not need to understand quantum computation in detail, but who needs to know how to describe the problem and use the best solution to solve it,” predicts Heike IBM Riel. “The model developer should not bother with advanced quantum knowledge. “, she insists.

But clarity and reliability will remain two imperatives that are strongly intertwined with these technologies.

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