Massachusetts Institute of Technology

Gregory R Steinbrecher

Dirk Robert Englund

Jacques Johannes Carolan

Many of the features of neural networks for machine learning can naturally be mapped into the quantum optical domain by introducing the quantum optical neural network (QONN). A QONN can be performed to perform a range of quantum information processing tasks, including newly developed protocols for quantum optical state compression, reinforcement learning, black-box quantum simulation and one way quantum repeaters. A QONN can generalize from only a small set of training data onto previously unseen inputs. Simulations indicate that QONNs are a powerful design tool for quantum optical systems and, leveraging advances in integrated quantum photonics, a promising architecture for next generation quantum processors.

Quantum optical neural networks

A method of nonblocking optical switching includes guiding a first optical beam from a first input to a first output via a first path through an optical switching fabric. The first path traverses a phase shifter disposed between a pair of cascaded Mach-Zehnder interferometers. The method also includes receiving a second optical beam for a second path intersecting with the first path through the optical switching fabric. The method also includes moving the first optical beam from the first path to a third path connecting the first input to the first output without intersecting the second path. The method also includes shifting a phase of the first optical beam, with the phase shifter, while moving the first optical beam from the first path to the third path to prevent the first optical beam from interfering with the second optical beam.

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Brookline, MA, United States of America

Gregory R Steinbrecher