Anna Sanpera

Trapped Ion Chain as a Neural Network: Error Resistant Quantum Computation

Marisa Pons [1], Veronica Ahufinger, Christof Wunderlich [3], Anna Sanpera, Sibylle Braungardt, Aditi Sen De, Ujjwal Sen, Maciej Lewenstein [5]

Abstract

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.

Disordered complex systems using cold gases and trapped ions

Aditi Sen De, Ujjwal Sen [1], Maciej Lewenstein [1], Veronica Ahufinger [2], Marisa Pons [3], Anna Sanpera [4]

Abstract

We report our research on disordered complex systems using cold gases and trapped ions, and address the possibility of using complex systems for quantum information processing. Two simple paradigmatic models of disordered complex systems are revisited here. The first one corresponds to a short range disordered Ising Hamiltonian (spin glasses), which can be implemented with a Bose-Fermi (Bose-Bose) mixture in a disordered optical lattice. The second model we address here is a long range disordered Hamiltonian, characteristic of neural networks (Hopfield model), which can be implemented in a chain of trapped ions with appropriately designed interactions.

Quantum Information Processing in Disordered and Complex Quantum Systems

Aditi Sen De, Ujjwal Sen [1,2], Veronica Ahufinger [3], Hans J. Briegel [4,5], Anna Sanpera [2,6], Maciej Lewenstein [1,2]

Abstract

We investigate quantum information processing and manipulations in disordered systems of ultracold atoms and trapped ions. First, we demonstrate generation of entanglement and local realization of quantum gates in a quantum spin glass system. Entanglement in such systems attains significantly high values, after quenched averaging, and has a stable positive value for arbitrary times. Complex systems with long range interactions, such as ion chains or dipolar atomic gases, can be modeled by neural network Hamiltonians. In such systems, we find the characteristic time of persistence of quenched averaged entanglement, and also find the time of its revival.