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| Home > Publications database > Improving the resilience of quantum denoising process |
| Conference Presentation (Invited) | FZJ-2023-00038 |
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2022
Abstract: Quantum autoencoders aim to automate denoising algorithms. These quantum neural networks are trained to surpass noise channels and return arbitrary entangled states of our interest with high-fidelity. So far the successful training has shown tolerance up to 30% of bit flip and depolarization. Stronger noise results in poor training and denoising failure. [1]In this talk I describe an inexpensive change in the network topology that can be extendable to all scales and can improve the tolerance significantly. This has a side advantage that it can provide even higher fidelity values for successful training. It indeed helps the encoder by reducing the dimension of the decision boundary between perfect and noisy states. Such a simplification of the classification task relies heavily on quantum properties of the neural units. We show that Renyi entropy associated with a small partition of the network undergoes a second order phase transition when training fails, and this can serve as a good measure to distinguish between failure and success in denoising process. [1] D. Bondarenko and P. Feldmann, “Quantum autoencoders to denoise quantum data”, Phys. Rev. Lett., vol. 124, no. 13, p. 130502, 2020.
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