Quantum Information Processing最新文献

Absolute separable (AS) quantum states are those states from which it is impossible to create entanglement, even under global unitary operations. It is known from the resource theory of non-absolute separability that the set of absolute separable states forms a convex and compact set, and global unitaries are free operations. We show that the action of a quantum switch controlled by an ancilla qubit over the global unitaries can break this robustness of AS states and produce ordinary separable states. First, we consider bipartite qubit systems and find the effect of quantum switch starting from the states sitting on the boundary of the set of absolute separable states . As particular examples, we illustrate what happens to modified Werner states and Bell diagonal (BD) states. For the Bell diagonal states, we provide the structure for the set of AS BD states and show how the structure changes under the influence of a switch. Further, we consider numerical generalization of the global unitary operations and show that it is always possible to take AS states out of the convex set under switching operations. We also generalized our results in higher dimensions.

A popular machine-learning model for regression tasks, including stock-market prediction, weather forecasting and real-estate pricing, is the classical support vector regression (SVR). However, a practically realisable quantum SVR remains to be formulated. We devise annealing-based algorithms, namely simulated and quantum-classical hybrid, for training two SVR models and compare their empirical performances against the SVR implementation of Python’s scikit-learn package for facial-landmark detection (FLD), a particular use case for SVR. Our method is to derive a quadratic-unconstrained-binary formulation for the optimisation problem used for training a SVR model and solve this problem using annealing. Using D-Wave’s hybrid solver, we construct a quantum-assisted SVR model, thereby demonstrating a slight advantage over classical models regarding FLD accuracy. Furthermore, we observe that annealing-based SVR models predict landmarks with lower variances compared to the SVR models trained by gradient-based methods. Our work is a proof-of-concept example for applying quantum-assisted SVR to a supervised-learning task with a small training dataset.

Quantum discord is an important measure of quantum correlations that goes beyond the paradigm of quantum entanglement. However, calculating quantum discord involves optimization over measurements, which is computationally challenging and often infeasible. This raises the intriguing question of Gaussian extremality—whether the quantum discord of a reference Gaussian state can provide a meaningful bound to the quantum discord of the original state. In this paper, we investigate this question by comparing the Gaussian discord of a reference Gaussian state with the quantum discord.

The fully entangled fraction (FEF) measures the proximity of a quantum state to maximally entangled states. FEF (>frac{1}{d}), in (d otimes d) systems, is a significant benchmark for various quantum information processing protocols including teleportation. Quantum conditional entropy (QCE) on the other hand is a measure of correlation in quantum systems. Conditional entropies for quantum systems can be negative, marking a departure from conventional classical systems. The negativity of quantum conditional entropies plays a decisive role in tasks like state merging and dense coding. In the present work, we investigate the relation of these two important yardsticks. Our probe is mainly done in the ambit of states with maximally mixed marginals, with a few illustrations from other classes of quantum states. We start our study in two-qubit systems, where for the Werner states, we obtain lower bounds to its FEF when the conditional Rényi (alpha -)entropy is negative. We then obtain relations between FEF and QCE for two-qubit Weyl states. Moving on to two qudit states, we find a necessary and sufficient condition based on FEF, for the isotropic state to have negative conditional entropy. In two qudit systems, the relation between FEF and QCE is probed for the rank-deficient and generalized Bell diagonal states. FEF is intricately linked with k-copy nonlocality and k- copy steerability. The relations between FEF and QCE facilitates to find conditions for k- copy nonlocality and k- copy steerability based on QCE. We obtain such conditions for certain classes of states in two qubits and two qudits. Applications of the relations obtained are provided in the context of work extraction, faithful entanglement and entropic uncertainty relations.

This article investigates thermal entanglement and quantum teleportation in a bipartite system composed of two spin-(frac{1}{2}) qubits, exposed to an external magnetic field along the Z-axis, within the framework of the squeezed spin model. We employ concurrence to quantify both the thermal entanglement in our system and the entanglement of the replicated output state in a quantum teleportation protocol through this system. Thus, we adopt fidelity to evaluate the quality of teleportation. It is shown that at the system’s ground state, a pure state favors maximal entanglement, while a mixed state leads to an absence of entanglement regardless of the magnetic field. At very low temperatures, increasing the magnetic field induces transitions from the entangled state to a separable state, but this transition is modulated by the intensity of interactions in the XY-plane. The intensities of interactions along the X- and Y-axes are studied to understand their effect on the system’s entanglement. Two spin squeezing mechanisms, one-axis twisting and two-axis counter twisting, are compared, revealing that two-axis counter twisting offers better entanglement. Finally, we explore quantum teleportation through squeezed spin states, demonstrating its feasibility with high fidelity at high temperatures and without a magnetic field, provided that the intensities of interactions in the XY-plane are negligible. By increasing the intensities (mu ) and (chi ), fidelity improves. Intriguingly, our analysis suggests that quantum teleportation, with increased fidelity, is achievable only with the one-axis twisting spin squeezing mechanism, remaining out of reach for two-axis counter twisting.

An important reason why it is currently difficult to unify relativity theory and quantum theory is the quantum information paradox. The information engulfment pointed out by general relativity violates the principles of quantum mechanics. An important reason why the industry does not have a clear understanding of this phenomenon is the current lack of a theoretically solvable cosmological model. Based on the complete model of loop quantum theory, this article solves different levels of Hamiltonian constraint models and simulates black hole information transfer dynamics, especially at extreme points, from analytical results to step-by-step quantum corrections, and attempts to compare the performance of different physical models in simulating quantum advantages during information transmission. Our study shows that even second-order expansions are sufficient to distinguish differences in dynamics at the black hole extremes, but to truly identify a model that has the potential to describe quantum information transfer mechanisms and is significantly different from other models, the theoretical analytical solution should at least extend to level three and above. In addition, the research results such as computational simulation methods and related conclusions cited and improved in this article can provide certain theoretical support and new insights for the research prospects of general relativity loop quantum cosmology and the intersection of quantum information and quantum fields.

Cooling the motion of multiple isolated, levitated particles has the potential to explore the limits of quantum mechanics in a new mass regime. This technique not only serves as a foundation for examining macroscopic quantum states and building high-precision sensors, but also crucial for overcoming detrimental cross-coupling and decoherence effects in multimode systems. In this paper, we studied that the center-of-mass modes of multi-magnons can be simultaneously cooled to their quantum ground states. Our scheme is realized by adjusting the coupling position of the particle to compensate for the reduction in coupling strength due to magnonic excitations. Additionally, we find that the cooling rate of a magnon is influenced by its own coupling strength and the effective detuning. The numerical simulation results indicate that the lowest phonon occupancy can be cooled to less than 1 simultaneously.

In this work, we present the parameterized entanglement measures, q-concurrence with (q>0) and (qnot =1). And we derive analytical lower bounds for the entanglement measures by using positive partial transposition and realignment criteria, detailed examples are presented. Moreover, we show that the increase of q-concurrence ((1<q<2)) for some superposition states are upper bounded by 1/2.

We use an artificial neural network (ANN) model to identify the entanglement class of an experimentally generated three-qubit pure state drawn from one of the six inequivalent classes under stochastic local operations and classical communication (SLOCC). The ANN model is also able to detect the presence of genuinely multipartite entanglement (GME) in the state. We apply data science techniques to reduce the dimensionality of the problem, which corresponds to a reduction in the number of required density matrix elements to be computed. The ANN model is first trained on a simulated dataset containing randomly generated states and is later tested and validated on noisy experimental three-qubit states cast in the canonical form and generated on a nuclear magnetic resonance (NMR) quantum processor. We benchmark the ANN model via support vector machines (SVMs) and K-nearest neighbor (KNN) algorithms and compare the results of our ANN-based entanglement classification with existing three-qubit SLOCC entanglement classification schemes such as 3-tangle and correlation tensors. Our results demonstrate that the ANN model can perform GME detection and SLOCC class identification with high accuracy, using a priori knowledge of only a few density matrix elements as inputs. Since the ANN model works well with a reduced input dataset, it is an attractive method for entanglement classification in real-life situations with limited experimental data sets.

In the present work, the randomness measure based on the disentropy of the autocorrelation function is used to quantify the randomness of binary sequences produced by quantum random number generators (QRNG). A homemade QRNG based on vacuum fluctuations and the Quantis QRNG, developed by IDQuantique and whose data are available at http://qrng.ethz.ch/live/, are analyzed. Our results show that the disentropy of the autocorrelation is a useful tool for performance analysis of QRNGs.