Abstract: A system and method for parallel data processing using photonic quantum computing. A method includes receiving tasks represented by classical binary bits. The tasks are converted to a first photon beam including converted tasks, which are represented by photonic quantum bits. The first photon beam is split into split photon beams. Each split photon beam corresponds to a subset of the converted tasks that have a same work type. The split photon beams are received by a quantum neural network, which includes quantum neural network clusters. Each split photon beam is received by a respective quantum neural network cluster. Each subset of the converted tasks is processed by a respective quantum neural network cluster in parallel. Processing the converted tasks includes identifying and removing one or more duplicate tasks in each subset of the converted tasks. Outputs are generated based on respective converted tasks as a second photon beam.

Abstract: A system and method for image processing using photonic quantum computing. A method includes receiving image data represented by classical binary bits. The image data is converted to a photon beam including converted image data represented by photonic quantum bits. The photon beam is split into red-, green-, and blue-channel photon beams, which are received by a quantum neural network including quantum neural network clusters. The converted image data is processed by processing the red-, green-, and the blue-channel photon beams. Each beam is processed by a respective neural network cluster in parallel. Processing the red-channel photon beam includes converting converted red-channel image data into first grayscale image data. First filters are applied to the first grayscale image data to extract first features. The first features are compared to pre-trained features. In response to the first features matching respective pre-trained features, identified first features are identified.

Abstract: A system and method for voice processing using photonic quantum computing. A method includes receiving voice data represented by classical binary bits. The voice data is converted to a first photon beam including converted voice data represented by photonic quantum bits. The first photon beam is split into split photon beams. The split photon beams are received by a quantum neural network, which includes quantum neural network clusters. The converted voice data is processed by processing the split photon beams. Each of the split photon beams is processed by a respective neural network cluster in parallel. Processing the converted voice data includes extracting voice features. Word embeddings are created based on the voice features. Sentences are determined based on the word embeddings.