Experimental demonstration of the Deutsch-Jozsa algorithm in homonuclear multispin systems

Wu, Zhizhou; Li, Jun; Zheng, Wenqiang; Luo, Jianlin; Feng, Mang; Peng, Xinhua

doi:10.1103/physreva.84.042312

Cited by 14 publications

(10 citation statements)

References 31 publications

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“…Since its invention in 2005, the GRadient Ascent Pulse Engineering (GRAPE) [18] technique has been applied to design optimal control pulse sequences in different quantum systems, such as: magnetic resonance [42][43][44][45], superconducting qubits [24,46], circuit QED [47], and nitrogenvacancy centers [48]. In this section we discuss the application of GRAPE to control the vibrational states of atoms trapped in an optical lattice.…”

Section: Gradient Ascent Pulse Engineeringmentioning

confidence: 99%

Quantum control of population transfer between vibrational states in an optical lattice

Hallaji¹,

Chen²,

Hayat³

et al. 2015

Preprint

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We study quantum control techniques, specifically Adiabatic Rapid Passage (ARP) and Gradient Ascent Pulse Engineering (GRAPE), for transferring atoms trapped in an optical lattice between different vibrational states. We compare them with each other and with previously studied coupling schemes in terms of performance. In our study of ARP, we realize control of the vibrational states by tuning the frequency of a spatial modulation through the inhomogeneously broadened vibrational absorption spectrum. We show that due to the presence of multiple crossings, the population transfer depends on the direction of the frequency sweep, in contrast to traditional ARP. In a second study, we control these states by applying a pulse sequence involving both the displacement of the optical lattice and modulation of the lattice depth. This pulse is engineered via the GRAPE algorithm to maximize the number of atoms transferred from the initial (ground) state to the first excited state. We find that the ARP and the GRAPE techniques are superior to the previously tested techniques at transferring population into the first excited state from the ground state: 38.9±0.2% and 39±2% respectively. GRAPE outperforms ARP in leaving the higher excited states unpopulated (less than 3.3% of the ground state population, at 84% confidence level), while 18.7 ± 0.3% of the ground state population is transferred into higher excited states by using ARP. On the other hand, ARP creates a normalized population inversion of 0.21 ± 0.02, which is the highest obtained by any of the control techniques we have investigated.

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Section: Gradient Ascent Pulse Engineeringmentioning

confidence: 99%

Quantum control of population transfer between vibrational states in an optical lattice

Hallaji¹,

Chen²,

Hayat³

et al. 2015

Preprint

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“…Quantum machine learning algorithms employing quantum features such as superposition and entanglement [7][8][9][10][11][12][13][14][15] promise enhancements in terms of the computing resources and the speed compared to the classical counterparts. Several experimental researches have been done to implement these algorithms [16][17][18][19][20][21]. In this article, we present a quantum algorithm for face recognition as one of the potential applications of quantum algorithms in machine learning.…”

Section: Introductionmentioning

confidence: 99%

Quantum Face Recognition Protocol with Ghost Imaging

Salari¹,

Paneru²,

Sağlamyürek³

et al. 2021

Preprint

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Face recognition is one of the most ubiquitous examples of pattern recognition in machine learning, with numerous applications in security, access control, and law enforcement, among many others. Pattern recognition with classical algorithms requires significant computational resources, especially when dealing with high-resolution images in an extensive database. Quantum algorithms have been shown to improve the efficiency and speed of many computational tasks, and as such, they could also potentially improve the complexity of the face recognition process. Here, we propose a quantum machine learning algorithm for pattern recognition based on quantum principal component analysis (QPCA), and quantum independent component analysis (QICA). A novel quantum algorithm for finding dissimilarity in the faces based on the computation of trace and determinant of a matrix (image) is also proposed. The overall complexity of our pattern recognition algorithm is O(N log N ) -N is the image dimension. As an input to these pattern recognition algorithms, we consider experimental images obtained from quantum imaging techniques with correlated photons, e.g. "interaction-free" imaging or "ghost" imaging. Interfacing these imaging techniques with our quantum pattern recognition processor provides input images that possess a better signal-to-noise ratio, lower exposures, and higher resolution, thus speeding up the machine learning process further. Our fully quantum pattern recognition system with quantum algorithm and quantum inputs promises a much-improved image acquisition and identification system with potential applications extending beyond face recognition, e.g., in medical imaging for diagnosing sensitive tissues or biology for protein identification.

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“…For this reason, the algorithm remains important from a theoretical point of view of the power of quantum computing, and from an experimental point of view as a proof-of-principle operation of quantum computer prototypes. Examples of experimental demonstration of Deutsch-Jozsa algorithm include NMR [6,7], superconducting qubits [8], single-photon linear optics [9], and trapped ions [10]. Currently, demonstrations of quantum algorithms are typically limited to 10 qubits, due to limitations with decoherence and scalability of current quantum computing technologies.…”

Section: Introductionmentioning

confidence: 99%

Implementing the Deutsch-Jozsa algorithm with macroscopic ensembles

Semenenko

Byrnes

2016

Phys. Rev. A

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms PHYSICAL REVIEW A 93, 052302 (2016) Implementing the Deutsch-Jozsa algorithm with macroscopic ensembles Quantum computing implementations under consideration today typically deal with systems with microscopic degrees of freedom such as photons, ions, cold atoms, and superconducting circuits. The quantum information is stored typically in low-dimensional Hilbert spaces such as qubits, as quantum effects are strongest in such systems. It has, however, been demonstrated that quantum effects can be observed in mesoscopic and macroscopic systems, such as nanomechanical systems and gas ensembles. While few-qubit quantum information demonstrations have been performed with such macroscopic systems, a quantum algorithm showing exponential speedup over classical algorithms is yet to be shown. Here, we show that the Deutsch-Jozsa algorithm can be implemented with macroscopic ensembles. The encoding that we use avoids the detrimental effects of decoherence that normally plagues macroscopic implementations. We discuss two mapping procedures which can be chosen depending upon the constraints of the oracle and the experiment. Both methods have an exponential speedup over the classical case, and only require control of the ensembles at the level of the total spin of the ensembles. It is shown that both approaches reproduce the qubit Deutsch-Jozsa algorithm, and are robust under decoherence.

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Experimental demonstration of the Deutsch-Jozsa algorithm in homonuclear multispin systems

Cited by 14 publications

References 31 publications

Quantum control of population transfer between vibrational states in an optical lattice

Quantum control of population transfer between vibrational states in an optical lattice

Quantum Face Recognition Protocol with Ghost Imaging

Implementing the Deutsch-Jozsa algorithm with macroscopic ensembles

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