Quantum topology identification with deep neural networks and quantum walks

Understand

Topologically ordered materials may serve as a platform for new quantum technologies such as fault-tolerant quantum computers.

• To fulfil this promise, efficient and general methods are needed to discover and classify new topological phases of matter.
• We demonstrate that deep neural networks augmented with external memory can use the density profiles formed in quantum walks to efficiently identify properties of a topological phase as well as phase transitions.
• On a trial topological ordered model, our method's accuracy of topological phase identification reaches 97.4%, and is shown to be robust to noise on the data.