Current protocols to revert time evolutions of quantum systems suffer either from low success probabilities, or from not being universal - they require knowledge about the evolving state, its evolution, or the interaction through which the evolution is reverted.
We overcome these limitations, and demonstrate a novel universal time-reversal protocol by implementing it on a photonic platform using a quantum SWITCH. The schemes universality is verified and a clear quantum advantage with respect to the optimal classical stragegy is shown through running the protocol on a large parameter set and reverting a photons' polarization state with an average state fidelity of over 95%.
Current protocols to revert time evolutions of quantum systems suffer either from low success probabilities, or from not being universal - they require knowledge about the evolving state, its evolution, or the interaction through which the evolution is reverted.
We overcome these limitations, and demonstrate a novel universal time-reversal protocol by implementing it on a photonic platform using a quantum SWITCH. The schemes universality is verified and a clear quantum advantage with respect to the optimal classical stragegy is shown through running the protocol on a large parameter set and reverting a photons' polarization state with an average state fidelity of over 95%.
We introduce a quantum token scheme that relies neither on quantum memories nor on space-time constraints. While this is achieved by limiting the flexibility, our protocol still exhibits interesting advantages. Among them is the capability of enhancing the protection against screen monitoring while retaining user privacy. To show that our protocol is secure even in realistic scenarios, we implemented it utilising an asymmetric SPDC source.
As the field of artificial intelligence is pushed forward, the question arises of how fast autonomous machines can learn. Within artificial intelligence, an important paradigm is reinforcement learning, where agents - learning entities capable of decision making - interact with the world they are placed in, called an environment. Thanks to these interactions, agents receive feedback from the environment and thus progressively adjust their behaviour to accomplish a given goal. An important question in reinforcement learning is how fast agents can learn to fulfill their tasks. To answer this question we consider a novel reinforcement learning framework where quantum mechanics is used. In particular, we quantize the agent and the environment and grant them the possibility to also interact quantum-mechanically, that is, by using a quantum channel for their communication. We demonstrate that this feature enables a speed-up in the agent's learning process, and we further show that combining this scenario with classical communication enables the evaluation of such an improvement. This learning protocol is implemented on an integrated re-programmable photonic platform interfaced with photons at telecommunication wavelengths. Thanks to the full tunability of the device, this platform proves the best candidate for the implementation of learning protocols, where a continuous update of the learning process is required.
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