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Privacy-preserving vision must overcome the dual challenge of utility andprivacy. Too much anonymity renders the images useless, but too little privacydoes not protect sensitive data. We propose a novel design for privacypreservation, where the imagery is stored in quantum states. In the future,this will be enabled by quantum imaging cameras, and, currently, storing verylow resolution imagery in quantum states is possible. Quantum state imagery hasthe advantage of being both private and non-private till the point ofmeasurement. This occurs even when images are manipulated, since every quantumaction is fully reversible. We propose a control algorithm, based on doubledeep Q-learning, to learn how to anonymize the image before measurement. Afterlearning, the RL weights are fixed, and new attack neural networks are trainedfrom scratch to break the system's privacy. Although all our results are insimulation, we demonstrate, with these first steps, that it is possible tocontrol both privacy and utility in a quantum-based manner.

Schr\"odinger's Camera: First Steps Towards a Quantum-Based Privacy Preserving Camera