the original version of this story appeared in Quanta Magazine.
It’s not easy to study quantum systems: collections of particles that follow the counterintuitive rules of quantum mechanics. Heisenberg’s uncertainty principlea cornerstone of quantum theory, says that it is impossible to simultaneously measure the exact position of a particle and its speed, quite important information to understand what is happening.
To study, say, a particular set of electrons, researchers have to be smart about it. They could take a box of electrons, touch it in various 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 is a problem: they cannot measure all the properties of the system at the same time. Then they iterate. They will start with your system, puncture, and then measure. Then they will do it again. In each iteration, they will measure a new set of properties. Create enough snapshots and machine learning Algorithms can help reconstruct all the properties of the original system, or at least get very close.
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 functioning 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 much richer and more precise particle descriptions. It also means that the computer could maintain multiple copies of a quantum state in its working memory.
A few years ago, a team from the California Institute of Technology proven that certain algorithms that use quantum memory require exponentially fewer snapshots than algorithms that do not use it. Their method was a breakthrough, but it required a relatively large amount of quantum memory.
This is decisive, because in practice, quantum memory is difficult to achieve. A quantum computer is made of interconnected quantum bits called qubits, and qubits can be used for computing or memory, but not both.
Now, two independent teams have come up with ways to get by with much less quantum memory. in the first paper, Sitan Chena computer scientist at Harvard University, and his co-authors showed that just two copies of the quantum state could exponentially reduce the number of times you need to take a snapshot of your quantum system. In other words, the investment in quantum memory is almost always worth it.
