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arXiv:cond-mat/0512606 · Physical Review Letters

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

M. Ll. Pons, V. Ahufinger, Christof Wunderlich, Anna Sanpera, Sibylle Braungardt, Aditi Sen, Ujjwal Sen, Maciej Lewenstein

Published Jan 9, 2007• 50 citations
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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 a 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.

DOI
10.1103/physrevlett.98.023003

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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 a 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.
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Abstract Continuous-variable quantum computing (CVQC) uses quantum states with continuous degrees of freedom, such as the quadratures of bosonic modes, to encode information, promising efficient solutions to complex problems. Trapped-ion systems provide a robust platform with long coherence times and precise qubit control, enabling the manipulation of quantum information through its motional and electronic degrees of freedom. In this theoretical work, we quantitatively analyze the fidelity of a Kerr operation in a single trapped-ion system, calibrating the interaction time using a motional-state Rabi frequency correction. Through numerical simulations, we validate Gaussian and non-Gaussian CVQC operations, achieving fidelities exceeding 99% in all cases, including non-Gaussian operations. Furthermore, we demonstrate two key applications: first, the implementation of a continuous-variable quantum neural network for regression problems, which exhibits low error rates; and second, its application to state preparation tasks, achieving a fidelity of 90% for the given example. These results open new opportunities to execute quantum computing algorithms on continuous variables with trapped ions, such as quantum machine learning algorithms.
We study quantum information processing in complex disordered many body systems that can be implemented by using lattices of ultracold atomic gases and trapped ions. We demonstrate, first in the short range case, the generation of entanglement and the local realization of quantum gates in a disordered magnetic model describing a quantum spin glass. We show that in this case it is possible to achieve fidelities of quantum gates higher than in the classical case. Complex systems with long range interactions, such as ions chains or dipolar atomic gases, can be used to model neural network Hamiltonians. For such systems, where both long range interactions and disorder appear, it is possible to generate long range bipartite entanglement. We provide an efficient analytical method to calculate the time evolution of a given initial state, which in turn allows us to calculate its quantum correlations.
Trapped ions are a near ideal system to study quantum information processing due to the high degree of control over the ion's external confinement and internal degrees of freedom. We demonstrate the key steps necessary for trapped ion quantum computing and focus on phonon-mediated entangling gates. We highlight several key algorithms implemented over the last decade with these gates and give a detailed description of Grover's quantum database search implemented with two trapped ion qubits.
In this paper we describe one of the most promising platforms for the construction of a universal quantum computer, which consists of a chain of NNN ions trapped in a harmonic potential, whose internal states work out as qubits, and are coupled to collective vibrational modes of the chain. From such coupling, it is possible to build interactions between different ions of the chain, that is, qubit-qubit interactions that, together with individual operations on the ions, allow building a quantum computer as first proposed by Cirac and Zoller in the 1990s [Phys. Rev. Lett. 74, 4091 (1995)]. Here we discuss from the physics involved in trapping ions in electromagnetic potentials to the Hamiltonian engineering needed to generate a universal set of logic gates, fundamental for the execution of more complex quantum algorithms. Finally, we present the current state of the art of quantum computing in trapped ion systems, highlighting recent advances made by companies and government projects that use such architecture, such as IonQ and AQTION.