Quantum memory is proving extremely powerful

It's not easy to study quantum systems — collections of particles that follow the non-intuitive rules of quantum mechanics. Heisenberg's uncertainty principle, a cornerstone of quantum theory, says it's impossible to measure a particle's exact position and speed simultaneously, information that's very important for understanding what's happening.

In order to study a particular set of electrons, for example, researchers have to get smart about it. They might take a box of electrons, stab it in different ways, and then take a snapshot of what it looks like in the end. In doing so, they hope to reconstruct the internal quantum dynamics at work.

But there's a problem: they can't measure all of the system's properties at the same time. So they repeat. They will start with their system, then store it, then measure it. Then they will do it again. At each iteration, they will measure a new set of properties. Build enough snapshots together, and machine learning algorithms can help reconstruct the full properties of the original system — or at least get really close to them.

This is a tedious process. But in theory, quantum computers could help. These machines, which operate according to quantum rules, have the potential to be much better than ordinary computers at modeling the workings of quantum systems. They can also store information not in classical binary memory, but in a more complex form called quantum memory. This allows for richer and more accurate descriptions of particles. This also means that a computer can hold multiple copies of a quantum state in its working memory.

A few years ago, a team based at Caltech Proven Some algorithms that use quantum memory require significantly fewer snapshots than algorithms that do not. Their method was a major advance, but it required a relatively large amount of quantum memory.

This is a deal breaker, because in practice, quantum memory is hard to come by. A quantum computer consists of interconnected quantum bits called qubits. Qubits can be used for computation or memory, but not both.

Now, two independent teams have found ways to deal with much smaller quantum memory. In the first paper, Seitan Chena computer scientist at Harvard University, and his co-authors have shown that just two versions of a quantum state can dramatically reduce the number of times you need to take a snapshot of your quantum system. In other words, quantum memory is almost always worth the investment.

“These two- or triplicate measurements are more powerful than one might think,” he said. Richard Koeniga computer scientist at Johannes Kepler University Linz in Austria.

To prove this, Chen and his colleagues combined information theory, an area of ​​mathematics that studies the transmission and processing of information, with specialized techniques used in quantum error correction and classical simulations of quantum computation.

The next day this work appeared on the scientific preprint arxiv.org, a group based at Google Quantum AI in Venice, California, published Another sheet who came to a similar conclusion. This work focused on applications in quantum chemistry.

The combined results also speak to a more fundamental goal. For decades, the quantum computing community has been trying to create quantum advantage, a task that quantum computers can do while classical computers have difficulty achieving. Typically, researchers understand that quantum advantage means that a quantum computer can do the task in much fewer steps.

New research shows that quantum memory allows a quantum computer to perform a task not necessarily with fewer steps, but with less data. As a result, the researchers believe that this in itself could be a way to demonstrate quantum advantage. “It allows us, in the near term, to achieve this kind of quantum advantage,” he said. Hsin Yuan Huanga physicist at Google Quantum AI.

But researchers are excited about the practical benefits as well, as the new findings make it easier for researchers to understand complex quantum systems.

“We're getting closer to the things that people really want to measure in these physical systems,” he said. Jarrod McLeancomputer scientist at Google Quantum AI.