Source: The Quantum Computer Dream is Falling Apart Channel: Sabine Hossenfelder Published: February 17, 2026 | Archived: May 29, 2026

Video: The Quantum Computer Dream is Falling Apart
Channel: Sabine Hossenfelder
Published: February 17, 2026
Duration: 7:08
Views: 612,972
Category: Science & Technology
Video ID: N-9muK0mv5w

Description

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As we continue to research quantum computing, quantum advantage – the supposed advantage that quantum computers theoretically have over regular computers – continues to dry up. Today we’re covering how more quantum computing use cases are disappearing, and an unexpected problem with quantum computing in general.

Paper 1: https://arxiv.org/abs/2601.04621 Paper 2: https://iopscience.iop.org/article/10.1088/2058-9565/add61d

Olivier Ezratty’s Slides: https://www.oezratty.net/Files/Conferences/Olivier%20Ezratty%20Q2B%20SV%20FTQC%20Energetics%20Dec2025.pdf

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#science #sciencenews #quantum #physics

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Transcript — YouTube panel (English (United Kingdom)

) (human-authored)

0:00 The story of quantum computing is becoming increasingly clear, I think. As research progresses, use cases evaporate. It’s a magic show: first the rabbit disappears, then the hat disappears, and eventually the audience disappears. Also, it turns out that quantum computers have a problem that no one thought of before. Let’s have a look. Quantum computers are great, they say, because these machines can solve problems too tough for regular computers. “They say” is my favourite source. Quantum computers get their advantage from the power of entanglement, a type of correlation that only exists in quantum physics. It’s a correlation so strong that if you mention it at a party, two philosophers immediately appear and start arguing about free will.

0:47 Quantum computers could vastly speed up certain calculations and that isn’t just academically interesting. It could be useful for material science or chemistry, for logistics, code-cracking or finance. At least that’s what they say. But things aren’t going as planned. Take for example the idea that quantum computers might one day simulate complex molecules. One big example has been the “FeMo-cofactor.” Sounds like a startup that sells protein powder to venture capitalists, but no, it’s a molecule that plays a major role in bacteria which enrich soil with nitrogen. This process feeds plants and, ultimately, us. Understanding this molecule could lead to better fertilizers and make a major impact on feeding the world. And while we have the equations that describe the properties of the molecule, we can’t solve them on a conventional computer, they are just too difficult — a quantum computer must do it, so they say.

1:48 But well, in a new preprint that just appeared on the arXiv, a team from Caltech managed to calculate the ground state energy of the molecule to stunning precision with a conventional computer cluster. This in itself isn’t going to change the world, but it illustrates that finding a practical advantage for quantum computers is tough. The future just got outperformed by a large room full of fans and cables, and one person who refused to go home.

2:16 This comes just a few weeks after another group of physicists argued that one of the most-often used examples for the use of quantum computers, solving the travelling-salesman problem, is a futile attempt. The traveling salesman problem is the task of finding the shortest route among a number of given locations. Closely related problems need to be solved in many every-day routines, from logistics to finance to 3d printing. For this reason, the traveling salesman problem has become the traveling sales pitch of quantum computers.

2:49 In the new paper, the authors reviewed two decades of failed attempts to force the problem into a form where a quantum computer would have an advantage, and suggest it is better to stick with classical or hybrid classical-quantum systems. Concretely, they write “In terms of the question of whether there is evidence that purely quantum approaches (as opposed to hybrid quantum-classical approaches) can solve even small-sized TSPs, this article has reviewed two decades of literature to conclude there is little cause for optimism.” “There is little cause for optimism” is such a beautiful academic phrase.

3:30 It’s not pessimism. It’s optimistic, but error-corrected. Even a recent, optimistic review by two pioneers of the field, acknowledges the “fraught road to quantum advantage”, that is the difficulty to find any meaningful task at which quantum computers might come in useful. Now let me be clear, on the research-side everything is going well. Error correction has been tested successfully, albeit on small circuits, and the precision and quality of qubits has steadily increased. A recent headline even claimed that quantum computers have reached their “Transistor moment.” But really?

4:10 What made microchips so dramatically successful was that transistors became dramatically smaller and cheaper to produce. But the cost-driving factors with quantum computers are at the moment the dramatic requirements for cryogenic cooling and noise buffering. These costs are not going to go down if they need to cool more qubits, that’s the entire problem. I see no indication whatsoever that the costs of quantum computers are going to go down.

4:34 And quantum computers have another problem that no one has thought about much: they would eat up a lot of energy. According to a recent estimate from Olivier Ezratty, error corrected quantum computers, once large enough to do actually useful calculations, would require as much power as entire supercomputer clusters, some designs even more. That means, it’s not only expensive to build them, running them won’t be cheap either.

5:01 Now you might say, ok, but wasn’t the entire point that they would be much faster. Yes, well, they would be faster for certain tasks, on the same task. That doesn’t mean that in total the calculation would be short. On a quantum computer calculations become long because you have to repeat them many times to reach a desired accuracy. And even if that is even peak power, you still need to provide that peak power, which drives cost.

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