For this, the researchers took inspiration from neural networks — which solve problems through many layers of computation to build a novel “quantum neural network” (QNN), where each layer represents a set of quantum operations.
To run the QNN, they used traditional silicon fabrication techniques to build a 2-by-5-millimeter NISQ chip with more than 170 control parameters such as tunable circuit components that make manipulating the photon path easier. Pairs of photons are generated at specific wavelengths from an external component and injected into the chip and as the photons travel through the chip’s phase shifters, they interfer with each other. This produces a random quantum output state which represents what would happen during computation. The output is measured by an array of external photodetector sensors.
That output is then sent to the QNN. The first layer uses complex optimization techniques to dig through the noisy output to pinpoint the signature of a single photon among all those scrambled together. Then, it “unscrambles” that single photon from the group to identify what circuit operations return it to its known input state. Those operations should match exactly the circuit’s specific design for the task. All subsequent layers do the same computation — removing from the equation any previously unscrambled photons — until all photons are unscrambled.