Continuous variables quantum computation over the vibrational modes of a single trapped ion

Ortiz-Gutiérrez, Luis; Gabrielly, Bruna; Muñoz, Luis F.; Pereira, Kainã T.; Filgueiras, Jefferson G.; Villar, A. S.

doi:10.1016/j.optcom.2017.04.011

Cited by 26 publications

(20 citation statements)

References 84 publications

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“…The inner product has to be calculated on a physical system that allows manipulation of both continuous variables and a discrete-variable qubit as control. Promising candidates of quantum platforms capable of hybrid variable quantum computing include superconducting circuit and trapped ion quantum computer [27][28][29][30][31][32]. We take trapped ion as an example.…”

Section: Physical Implementationmentioning

confidence: 99%

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Quantum kernels with squeezed-state encoding for machine learning

Li,

Zhang,

Wang

2021

Preprint

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Kernel methods are powerful for machine learning, as they can represent data in feature spaces that similarities between samples may be faithfully captured. Recently, it is realized that machine learning enhanced by quantum computing is closely related to kernel methods, where the exponentially large Hilbert space turns to be a feature space more expressive than classical ones. In this paper, we generalize quantum kernel methods by encoding data into continuous-variable quantum states, which can benefit from the infinite-dimensional Hilbert space of continuous variables. Specially, we propose squeezed-state encoding, in which data is encoded as either in the amplitude or the phase. The kernels can be calculated on a quantum computer and then are combined with classical machine learning, e.g. support vector machine, for training and predicting tasks. Their comparisons with other classical kernels are also addressed. Lastly, we discuss physical implementations of squeezed-state encoding for machine learning in quantum platforms such as trapped ions.

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Section: Physical Implementationmentioning

confidence: 99%

“…The motional state of the ion can be used as continuous variables, and various motional states have been realized experimentally [30][31][32]. Remarkably, squeezed vacuum state |z s can be created by irradiating two Raman beams with some specific frequency difference.…”

Section: Physical Implementationmentioning

confidence: 99%

Quantum kernels with squeezed-state encoding for machine learning

Li,

Zhang,

Wang

2021

Preprint

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“…Gaussian states have been largely applied in many branches of physics. They are experimental accessed in platforms such as quantum optics [25] and trapped ions [26]. Their modern applications ranging from quantum information theory [1][2][3] to quantum thermodynamics [5,6].…”

Section: Gaussian States and Gaussian Channelsmentioning

confidence: 99%

Coherence dynamics induced by attenuation and amplification Gaussian channels

Santos

Vieira

2021

Eur. Phys. J. Plus

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Quantum Gaussian channels play a key role in quantum information theory. In particular, the attenuation and amplification channels are useful to describe noise and decoherence effects on continuous variables systems. They are directly associated with the beam splitter and two-mode squeezing operations, which have operational relevance in quantum protocols with bosonic models. An important property of these channels is that they are Gaussian completely positive maps, and the action on a general input state depends on the parameters characterizing the channels. In this work, we study the coherence dynamics introduced by these channels on input Gaussian states. We derive explicit expressions for the coherence depending on the parameters describing the channels. By assuming a displaced thermal state with initial coherence as input state, for the attenuation case it is observed a revival of the coherence as a function of the transmissivity coefficient, whereas for the amplification channel the coherence reaches asymptotic values depending on the gain coefficient. Further, we obtain the entropy production for these classes of operations, showing that it can be reduced by controlling the parameters involved. We write a simple expression for computing the entropy production due to the coherence for both channels. This can be useful to simulate many processes in quantum thermodynamics, as finite-time driving on bosonic modes.

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“…In such systems, a larger computational space is provided by the collective space of multiple physical qubits. Alternatively, we can encode and process information in the infinite dimensional Hilbert space of bosonic systems such as quantum harmonic oscillators [9][10][11]. This approach offers a hardware-efficient solution with potential quantum speedups to practical machine learning problems.…”

mentioning

confidence: 99%

Quantum-enhanced bosonic learning machine

Nguyen¹,

Tseng²,

Maslennikov³

et al. 2021

Preprint

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Quantum processors enable computational speedups for machine learning through parallel manipulation of high-dimensional vectors [1]. Early demonstrations of quantum machine learning have focused on processing information with qubits [2][3][4][5][6][7][8]. In such systems, a larger computational space is provided by the collective space of multiple physical qubits. Alternatively, we can encode and process information in the infinite dimensional Hilbert space of bosonic systems such as quantum harmonic oscillators [9][10][11]. This approach offers a hardware-efficient solution with potential quantum speedups to practical machine learning problems. Here we demonstrate a quantum-enhanced bosonic learning machine operating on quantum data with a system of trapped ions. Core elements of the learning processor are the universal feature-embedding circuit that encodes data into the motional states of ions, and the constant-depth circuit that estimates overlap between two quantum states. We implement the unsupervised K-means algorithm to recognize a pattern in a set of highdimensional quantum states and use the discovered knowledge to classify unknown quantum states with the supervised k-NN algorithm. These results provide building blocks for exploring machine learning with bosonic processors.

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Continuous variables quantum computation over the vibrational modes of a single trapped ion

Cited by 26 publications

References 84 publications

Quantum kernels with squeezed-state encoding for machine learning

Quantum kernels with squeezed-state encoding for machine learning

Coherence dynamics induced by attenuation and amplification Gaussian channels

Quantum-enhanced bosonic learning machine

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