original version Of this story appeared in quanta magazine,
It is not easy to study quantum systems – collections of particles that obey the counter-intuitive rules of quantum mechanics. Heisenberg's uncertainty principleA cornerstone of quantum theory, states that it is impossible to measure a particle's exact position and its momentum simultaneously – quite important information to understand what is happening.
To study a particular collection of electrons, researchers have to be smart about it. They can take a box of electrons, look at it in different ways, then finally take a snapshot of what it looks like. In doing so, they hope to reconstruct the internal quantum dynamics at work.
But there's a problem: They can't measure all the properties of the system at the same time. So they iterate. They'll start with their system, hit, then measure. Then they'll do it again. Each iteration, they will measure some new set of properties. create enough snapshots simultaneously, and machine learning Algorithms can help to recreate the full properties of the original system – or at least come really close.
This is a difficult process. But in principle, quantum computer Can help. These machines, which operate according to quantum rules, have the potential to be much better than ordinary computers at modeling the functioning of quantum systems. They can store information not in classic binary memory, but in a more complex form called quantum memory. This allows a richer and more accurate description of the particles. This also means that the computer can keep multiple copies of the quantum state in its working memory.
A few years ago, a team based at the California Institute of Technology Exhibited Some algorithms that use quantum memory require far fewer snapshots than algorithms that do not use it. Their method was a major advance, but it required relatively large amounts of quantum memory.
This is kind of a deal-breaker, because as a practical matter, quantum memory is hard to come by. A quantum computer is made of interconnected quantum bits called qubits, and qubits can be used for computation or memory, but not both.
Now, two independent teams have come up with ways to work with much smaller quantum memories. At first, me paper, Seitan ChainA computer scientist at Harvard University and his co-authors showed that having just two copies of a quantum state can exponentially reduce the number of times needed to take a snapshot of your quantum system. In other words, quantum memory is almost always worth the investment.