<p>Noise and decoherence are two major obstacles to the
implementation of large-scale quantum computing. Because of the no-cloning
theorem, which says we cannot make an exact copy of an arbitrary quantum state,
simple redundancy will not work in a quantum context, and unwanted interactions
with the environment can destroy coherence and thus the quantum nature of the
computation. Because of the parallel and distributed nature of classical neural
networks, they have long been
successfully used to deal with incomplete or damaged data. In this work,
we show that our model of a quantum
neural network (QNN) is similarly robust to noise, and that, in addition, it is
robust to decoherence. Moreover, robustness to noise and decoherence is not
only maintained but improved as the size of the system is increased. Noise and
decoherence may even be of advantage in training, as it helps correct for
overfitting. We demonstrate the robustness using entanglement as a means for
pattern storage in a qubit array. Our results provide evidence that machine
learning approaches can obviate otherwise recalcitrant problems in quantum
computing. </p>
<p> </p>
Design of Hybrid Time-Domain Schemes With Optimal Gridding Density and Material-Interface Sensitivity for Large-Scale EMC Problems
