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We report on the experimental investigation of an individual pseudomolecule using trapped ions with adjustable magnetically induced J-type coupling between spin states. Resonances of individual spins are well separated and are addressed…

Quantum Physics · Physics 2012-08-13 A. Khromova , Ch. Piltz , B. Scharfenberger , T. F. Gloger , M. Johanning , A. F. Varón , Ch. Wunderlich

We reduce measurement errors in a quantum computer using machine learning techniques. We exploit a simple yet versatile neural network to classify multi-qubit quantum states, which is trained using experimental data. This flexible approach…

Fully automated self-driving laboratories are promising to enable high-throughput and large-scale scientific discovery by reducing repetitive labour. However, effective automation requires deep integration of laboratory knowledge, which is…

Artificial Intelligence · Computer Science 2025-09-30 Shuxiang Cao , Zijian Zhang , Mohammed Alghadeer , Simone D Fasciati , Michele Piscitelli , Mustafa Bakr , Peter Leek , Alán Aspuru-Guzik

A digital quantum simulator is an envisioned quantum device that can be pro- grammed to efficiently simulate any other local system. We demonstrate and investigate the digital approach to quantum simulation in a system of trapped ions.…

We present a route towards the quantum simulation of exotic quantum magnetism in ion traps by exploiting dual relations between different spin models. Our strategy allows one to start from Hamiltonians that can be realized with current…

Quantum Gases · Physics 2016-03-24 Tobias Graß , Maciej Lewenstein , Alejandro Bermudez

We describe a general approach to modeling rational decision-making agents who adopt either quantum or classical mechanics based on the Quantum Bayesian (QBist) approach to quantum theory. With the additional ingredient of a scheme by which…

Quantum Physics · Physics 2022-05-18 John B. DeBrota , Peter J. Love

We propose to use a large cloud of cold trapped ions as a medium for quantum optics and quantum information experiments. Contrary to most recent realizations of qubit manipulation based on a small number of trapped and cooled ions, we study…

Quantum Physics · Physics 2016-08-16 Thomas Coudreau , Frédéric Grosshans , Samuel Guibal , Luca Guidoni

This paper proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks. The goal of the procedure is to apply quantum walk as a search algorithm in a complete graph to find all synaptic…

Quantum Physics · Physics 2021-09-06 Luciano S. de Souza , Jonathan H. A. de Carvalho , Tiago A. E. Ferreira

Over the last few decades, quantum chemistry has progressed through the development of computational methods based on modern digital computers. However, these methods can hardly fulfill the exponentially-growing resource requirements when…

Quantum Physics · Physics 2014-01-08 M. -H. Yung , J. Casanova , A. Mezzacapo , J. McClean , L. Lamata , A. Aspuru-Guzik , E. Solano

Quantum walks are recognizably useful for the development of new quantum algorithms, as well as for the investigation of several physical phenomena in quantum systems. Actual implementations of quantum walks face technological difficulties…

Quantum Physics · Physics 2017-01-31 Renato Portugal , Marcos Cesar de Oliveira , Jalil Khatibi Moqadam

This paper proposes a quantum-classical algorithm to evaluate and select classical artificial neural networks architectures. The proposed algorithm is based on a probabilistic quantum memory and the possibility to train artificial neural…

A system of trapped ions under the action of off--resonant standing--waves can be used to simulate a variety of quantum spin models. In this work, we describe theoretically quantum phases that can be observed in the simplest realization of…

Quantum Physics · Physics 2009-11-11 X. -L. Deng , D. Porras , J. I. Cirac

Trapped ions are among the leading candidates for quantum computing technologies. Interfacing ion qubits in separate traps and interfacing ion qubits with superconducting qubits are two of the many challenges to scale up quantum computers.…

Quantum Physics · Physics 2021-06-01 Noah Van Horne , Manas Mukherjee

Efficiently entangling pairs of qubits is essential to fully harness the power of quantum computing. Here, we devise an exact protocol that simultaneously entangles arbitrary pairs of qubits on a trapped-ion quantum computer. The protocol…

It is a fundamental, but still elusive question whether the schemes based on quantum mechanics, in particular on quantum entanglement, can be used for classical information processing and machine learning. Even partial answer to this…

Machine Learning · Statistics 2022-06-24 Yuhan Liu , Xiao Zhang , Maciej Lewenstein , Shi-Ju Ran

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 $N$ ions trapped in a harmonic potential, whose internal states work out as qubits, and are…

A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. The extended Agassi model is an analytically solvable model including both short range pairing and long range…

Trapped atomic ion qubits or effective spins are a powerful quantum platform for quantum computation and simulation, featuring densely connected and efficiently programmable interactions between the spins. While native interactions between…

Quantum Physics · Physics 2023-08-01 Or Katz , Marko Cetina , Christopher Monroe

The quantum walk formalism is a widely used and highly successful framework for modeling quantum systems, such as simulations of the Dirac equation, different dynamics in both the low and high energy regime, and for developing a wide range…

Quantum computers hold the promise to solve certain computational task much more efficiently than classical computers. We review the recent experimental advancements towards a quantum computer with trapped ions. In particular, various…

Quantum Physics · Physics 2008-11-20 H. Haeffner , C. F. Roos , R. Blatt