Sibylle Braungardt

We consider experimentally feasible chains of trapped ions with pseudo-spin 1/2, and find models that can potentially be used to implement error-resistant quantum computation. Similar in spirit to classical neural networks, the error-resistance of the system is achieved by encoding the qubits distributed over the whole system. We therefore call our system a ''quantum neural network'', and present a ''quantum neural network model of quantum computation''. Qubits are encoded in a few quasi-degenerated low energy levels of the whole system, separated by a large gap from the excited states, and large energy barriers between themselves. We investigate protocols for implementing a universal set of quantum logic gates in the system, by adiabatic passage of a few low-lying energy levels of the whole system. Naturally appearing and potentially dangerous distributed noise in the system leaves the fidelity of the computation virtually unchanged, if it is not too strong. The computation is also naturally resilient to local perturbations of the spins.

We demonstrate the possibility of realizing a neural network in a chain of trapped ions with induced long range interactions. Such models permit one to store information distributed over the whole system. The storage capacity of such network, which depends on the phonon spectrum of the system, can be controlled by changing the external trapping potential. We analyze the implementation of error resistant universal quantum information processing in such systems.