International Journal of Quantum Information

Canonical coherent states of a quantum harmonic oscillator have been generalized by requiring the conditions of normalizability, continuity in the label and resolution of the identity operator with a positive weight function. Superpositions of these states are considered in the present scenario as a generalization of the optical Schrödinger cat states. The Fock space is assumed to be canonical or finite-dimensional. The photon number distribution of these generalized Schrödinger cat states departs from the Poisson statistics in various ways for high photon numbers. For small nonvanishing values of the label, the photon number distribution is sub-Poissonian (nonclassical) or super-Poissonian, according to the interference properties. In fact, the sub- or super-Poissonian statistics is determined by the interplay between the relative phase and a critical value of the phase. The photon number distribution is uniquely sub-Poissonian for large values of the label.

The task of preserving entanglement against noises is of crucial importance for both quantum communication and quantum information transfer. To this aim, quantum error correction (QEC) codes may be employed to compensate, at least partially, the detriments induced by environmental noise that can be modeled as a bit-flip or a phase-flip error channel. In this paper, we investigate the effects of the simple three-qubit QEC codes to restore entanglement and nonlocality in a two-qubit system and consider two practical applications: superdense coding and quantum teleportation. Though the considered three-qubit QEC codes are known to perfectly work in the presence of very small noise, we show that they can avoid the sudden death of entanglement and improve the performance of the addressed protocols also for larger noise amplitudes.

Quantum nonlocality, as described by Einstein–Podolsky–Rosen (EPR) paradox, represents one of the mysteries at the very foundations of quantum mechanics. Here, we suggest to investigate if it can be understood by considering extra dimensions.

Nowadays, optical Quantum Communication has reached a mature level, especially in free space and through optical fibers. Experimental implementations usually involve single-photon states or weak coherent states detected by single-photon detectors. In contrast to these standard configurations, in our work we consider mesoscopic twin-beam (TWB) states revealed by photon-number-resolving detectors. By properly acting on the natural divergence of the generated beams, we show that they remain nonclassical even at a moderate distance from the nonlinear crystal in which they are produced. We also consider the case where one of the two parties of TWB is partially transmitted through water, and show that the nonclassicality of the states is preserved. This result suggests that mesoscopic TWB states can be considered for the new and growing scenario of underwater Quantum Communication.

The Bell inequalities stand at the cornerstone of the developments of quantum theory on both the foundational and applied side. The discussion started as a way to test whether the quantum description of reality is complete or not, but it developed in such a way that a new research area stemmed from it, namely quantum information. Far from being and exhausted topic, in this paper, we present a constructive and geometrically intuitive description of the local polytope and its facets in a bipartite Bell scenario with two dichotomic measurements per party.

Private set union (PSU) allows several parties to obtain the union of their private sets without disclosing each party’s private information. Existing PSU protocols often have polynomial complexity for the complete set size or complicated process. In this paper, a quantum multi-party PSU protocol based on least common multiple (LCM) and Shor’s algorithm is proposed, which enables the union of multiple sets to be computed all at once. In order to increase the one-time success probability of the protocol, we first improved Shor’s period-finding algorithm, which is used in LCM computation and integer factoring. Each party’s private set is encoded into an integer obtained by multiplying several prime numbers, thus the PSU problem is transformed into an LCM problem. The LCM of these integers is computed by using the improved Shor’s period-finding algorithm, and then factored to derived the union set. We prove the correctness of the proposed protocol, and its unconditional security against semi-honest attacks. Complexity analysis shows that our protocol has logarithmic complexity for the complete set size.

This study investigates the local quantum uncertainty (${ℒ}{𝒬}{𝒰}$) of a two-qubit Heisenberg XY chain with different directions of Dzyaloshinskii–Moriya (DM) interactions. The DM interaction parameters and coupling coefficient J are demonstrated to be beneficial in managing correlation. The DM interaction’s x-axis parameter has more influence on correlation than the DM interaction’s z-axis. As a result, adjusting the direction of the DM interaction with the coupling coefficient J may produce a more efficient operation to enhance the correlation.

A common assumption in an open quantum system is that the noise induced by the environment, due to the interaction between a quantum system and its environment, is responsible for the disappearance of quantum properties. Interestingly, Barreiro et al. [Nature 470 (2011) 486] show, experimentally, using an open-system quantum ion traps simulator, an environment state can be engineered and controlled to pump an arbitrary quantum system toward a maximally entangled state and thus can be considered a resource for quantum information processing. Here, we demonstrate this idea to pump an arbitrary maximally mixed state into Greenberger–Horne–Zeilinger (GHZ) state by simulating our quantum circuit using one of the IBM Q processors. Barreiro et al. [Nature 470 (2011) 486] offer the circuits for the execution of the GHZ pumping. Nevertheless, those are collected gates that are suitable to the trapped-ions platform, so their circuits implementation on the IBM Q devices would result in large depth circuits and thus requires many gates. Consequently, we suggest a different circuit structure that follows a similar goal, but has been considered the features of the IBM Q platform. Moreover, we run the simulation of our circuit using the QASM simulator and free-web-based interface, IBM Quantum Experience, with and without error mitigation, to investigate the effect of the noise on the preparation of the initial mixed states of the qubits in addition to the population of the target state of the system.

Big Data Analysis and Deep Learning are two fields of data science that are getting a proportion of interest nowadays. The relevance of big data has increased as a result of the massive amounts of domain-specific data that many public and private firms have gathered. This data may be useful for research on topics like national security, fraud, and medical informatics and detection. Huge volumes of data are analyzed by businesses like Google and Microsoft for business analysis and decision-making that affects both present and future technology. By using deep learning algorithms, high-level information is extracted. Multifaceted intellections are articulated as data illustrations using a tiered erudition approach. On the basis of comparably simpler abstractions created at the level before, sophisticated abstractions are learnt at a given level. The field of image processing is still in its infancy. Agriculture, textiles, transportation, and other fields have all made use of image alteration, image coding, compression, picture segmentation, and other technologies. Traditional image processing techniques, on the other hand, are unable to handle the huge quantity of image mockups available today. As a result, to raise the level and efficiency of image processing, a novel methodical area has emerged that focuses on exploring big data-based image processing technology and developing an image processing archetypal. The image processing prototypical based on big data offers recompenses including strong repeatability, high accuracy, extensive applicability, good flexibility, and a high potential for information reduction, according to existing big data research results. In this review, potential of Deep Learning and images processing to address some of the most challenging problems in Big Data Analytics, including the extraction of multifaceted designs from gigantic data sets, semantic indexing, data tagging, rapid facts repossession, and simplification of discriminative tasks will be explored. The integration and interaction of the three main topic is image processing, deep learning, and big data will be explained in this review. All three of these areas hold great promise for a variety of industries. The research issues highlighted in the integration and interplay of these broad domains are examined, as are some potential study avenues.