Qubit stabilisation via learning capable materials
Abstract
I describe the engineered decoherence of a qubit state by means of an environment formed out of a neurally architected material. Such a material is a material that can adjust its inner properties in the same way a neural network is adjusting its weights, subject to a built-in cost function. Such a material is naturally found in biological structures (like a brain) but can in principle be engineered at a microscopic level. If such a material is used as an environment for a Nakajima-Zwanzig equation describing the controlled decoherence of a quantum state, we obtain a modified decoherence that allows for correlated states to exist longer or even to become robust. Such a neural material can also be architected to implement certain quantum gate operations on the encapsulated qubit.
Cite
@article{arxiv.2406.04352,
title = {Qubit stabilisation via learning capable materials},
author = {Andrei T. Patrascu},
journal= {arXiv preprint arXiv:2406.04352},
year = {2024}
}