‘Quantum Memory’ Proves Exponentially Powerful
An editorially independent publication supported by the Simons Foundation.
It’s not easy to study quantum systems — collections of particles that follow the counterintuitive rules of quantum mechanics. Heisenberg’s uncertainty principle, a cornerstone of quantum theory, says it’s impossible to simultaneously measure a particle’s exact position and its speed — pretty important information for understanding what’s going on.
In order to study, say, a particular collection of electrons, researchers have to be clever about it. They might take a box of electrons, poke at it in various ways, then take a snapshot of what it looks like at the end. In doing so, they hope to reconstruct the internal quantum dynamics at work.
But there’s a catch: They can’t measure all the system’s properties at the same time. So they iterate. They’ll start with their system, poke, then measure. Then they’ll do it again. Every iteration, they’ll measure some new set of properties. Build together enough snapshots, and machine learning algorithms can help reconstruct the full properties of the original system — or at least get really close.
