Do quantum computers offer a way to vastly improve agriculture? As quantum computers continue to advance, identifying problems they can solve faster than the world's best conventional computers is becoming increasingly important - but it turns out that a key task held up as a future goal by quantum proponents may not need a quantum computer at all. The task in question involves a molecule called FeMoco, which plays a vital role in making life on Earth possible. That is because it is part of the process of nitrogen fixation, in which microbes convert atmospheric nitrogen into ammonia, making it biologically accessible to most other living organisms. How exactly FeMoco works during this process is complicated and not fully understood, but if we could crack it and replicate it on an industrial scale, it could drastically cut the energy involved in producing fertilisers, potentially leading to a boost in crop yields.

For the past year, I kept bringing the same story to my editor: quantum computers are on the edge of becoming useful for scientific discovery. Of course, that has always been the goal. The idea of using quantum computers to better understand our universe is part of their origin story, and it even featured in a 1981 speech by Richard Feynman. Contemplating the best way to simulate nature, he wrote: "We can give up on our rule about what the computer was, we can say: Let the computer itself be built of quantum mechanical elements which obey quantum mechanical laws." Today, Feynman's vision has been realised by Google, IBM and dozens more companies and academic teams. Their devices are now being used to simulate reality at the quantum level - and here are some highlights.

Researchers at the quantum computing firm Quantinuum used a new Helios-1 quantum computer to simulate a mathematical model that has long been used to study superconductivity. These simulations are not out of reach for conventional computers, but this advance sets the stage for quantum computers to become useful tools for materials science . Superconductors conduct electricity with perfect efficiency, but they currently only work at temperatures too low to be practical. For decades, physicists have been trying to understand how to tweak their structure to make them work at room temperature, and many believe answers will come from a mathematical framework called the Fermi-Hubbard model. This potential makes it one of the most important models in all condensed matter physics, says Quantinuum's Henrik Dreyer . Conventional computers can run exceptional simulations of the Fermi-Hubbard model but struggle with very large samples or cases where the materials it describes change over time.

What makes a quantum computer good? Claims that one quantum computer is better than another rest on terms like quantum advantage or quantum supremacy, fault-tolerance or qubits with better coherence - what does it all mean? Eleven years ago, I was just getting a start on my PhD in theoretical physics, and to be honest with you I never thought about quantum computers, or writing about them, at all. Meanwhile, staff were hard at work putting together the world's first " Quantum computer buyer's guide " (we've always been ahead of the curve). Looking through it reveals what a different time it was - John Martinis at University of California, Santa Barbara got a shout out for working on an array of only nine qubits, and just last week he was awarded the Nobel Prize in Physics .

Google has unveiled a new quantum computer and is once more claiming to have pulled ahead in the race to show that these exotic machines can beat even the world's best conventional supercomputers – so does that mean useful quantum computers are finally here? Researchers at the tech giant were the first in the world to demonstrate this feat, known as quantum supremacy, with the announcement of the Sycamore quantum computing chip in 2019. But since then, supercomputers have caught up, leaving Sycamore behind. Now, Google has produced a new quantum chip, called Willow, which Julian Kelly at Google Quantum AI says is the firm's best yet. "You can think of this as having all the advantages of Sycamore, but if you were to look under the hood, we changed the geometry… we reimagined the processor," he says.

The huge amount of energy required to train artificial intelligence (AI) is becoming a concern. To train the large language model (LLM) powering Chat GPT-3, for example, almost 1,300 megawatt hours of energy was used, according to an estimate by researchers from Google and the University of California, Berkeley, a similar quantity of energy to what is used by 130 American homes in one year. Furthermore, an analysis by OpenAI suggests that the amount of power needed to train AI models has been growing exponentially since 2012, doubling roughly every 3.4 months as the models become bigger and more sophisticated. However, our energy production capacity is not increasing as steeply, and doing so is likely to further contribute to global warming: generating electricity is the single biggest contributor to climate change given that coal, oil, and gas are still widely used to generate electricity, compared to cleaner energy sources. "At this rate, we are running into a brick wall in terms of the ability to scale up machine learning networks," said Menachem Stern, a theoretical physicist at the AMOLF research institute in the Netherlands.

A first-of-its-kind computer can perform calculations using the random thermal "noise" that is inherent in our world. It is built using standard commercial components and could eventually run artificial intelligence (AI) programs more efficiently than conventional computers. In conventional computers, all calculations are reduced to sequences of 1s and 0s, represented as the switching on and off of many tiny switches.

A first-of-its-kind computer can perform calculations using the random "noise" that is inherent in our world. It is built using standard commercial components and could eventually run artificial intelligence programs more efficiently than conventional computers. In conventional computers, all calculations are reduced to sequences of 1s and 0s, represented as the switching on and off of many tiny switches. However, these computers must contend with random thermodynamic noise, like a piece of a circuit warming up and unexpectedly turning a 0 into a 1.…

It is shown that conventional computers can be exponentiallx faster than planar Hopfield networks: although there are planar Hopfield networks that take exponential time to converge, a stable state of an arbitrary planar Hopfield network can be found by a conventional computer in polynomial time.

Physicists prove that classical machine learning models can improve predictions about quantum materials.