Quantum State Discrimination on Reconfigurable Noise-Robust Quantum
Networks
- URL: http://arxiv.org/abs/2003.11586v1
- Date: Wed, 25 Mar 2020 19:07:03 GMT
- Title: Quantum State Discrimination on Reconfigurable Noise-Robust Quantum
Networks
- Authors: Nicola Dalla Pozza, Filippo Caruso
- Abstract summary: A fundamental problem in Quantum Information Processing is the discrimination amongst a set of quantum states of a system.
In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a Quantum Walk.
We optimize the parameters of the network to obtain the highest probability of correct discrimination.
- Score: 6.85316573653194
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A fundamental problem in Quantum Information Processing is the discrimination
amongst a set of quantum states of a system. In this paper, we address this
problem on an open quantum system described by a graph, whose evolution is
defined by a Quantum Stochastic Walk. In particular, the structure of the graph
mimics those of neural networks, with the quantum states to discriminate
encoded on input nodes and with the discrimination obtained on the output
nodes. We optimize the parameters of the network to obtain the highest
probability of correct discrimination. Numerical simulations show that after a
transient time the probability of correct decision approaches the theoretical
optimal quantum limit. These results are confirmed analytically for small
graphs. Finally, we analyze the robustness and reconfigurability of the network
for different set of quantum states, and show that this architecture can pave
the way to experimental realizations of our protocol as well as novel quantum
generalizations of deep learning.
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