arXiv subjects

Vivek Balasaheb Sabale

Publications and source records attributed to Vivek Balasaheb Sabale.

6 recordsLinked to original sources

arXiv2025

Heisenberg spin networks for realizing quantum battery with the aid of Dzyaloshinskii Moriya interaction

This work investigates the energy storage properties of quantum spin chains in the context of quantum batteries by introducing Heisenberg spin network models organized into different configurations, open, closed, supercube geometries, and c regular graphs. The charging dynamics of these systems are examined using Hamiltonians that include contributions from the battery, spin spin interactions, and a transverse magnetic field. Incorporating the Dzyaloshinskii Moriya interaction into the charging Hamiltonian is found to enhance the ergotropy in the XXZ model, particularly for the supercube configuration, thereby improving quantum battery performance. To explore the role of structural variations, we extend our study to c regular graphs with system sizes ranging from 3 to 12 qubits, including highly symmetric geometries such as the tetrahedron, octahedron, and icosahedron. These analyzes reveal that such symmetric structures retain ideal sinusoidal charging discharging behavior when DMI is tuned appropriately, establishing symmetry and coordination as key principles for scalable quantum battery architectures.

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arXiv2025

Quantum neural networks facilitating quantum state classification

The classification of quantum states into distinct classes poses a significant challenge. In this study, we address this problem using quantum neural networks in combination with a problem-inspired circuit and customised as well as predefined ansätz. To facilitate the resource-efficient quantum state classification, we construct the dataset of quantum states using the proposed problem-inspired circuit. The problem-inspired circuit incorporates two-qubit parameterised unitary gates of varying entangling power, which is further integrated with the ansätz, developing an entire quantum neural network. To demonstrate the capability of the selected ansätz, we visualise the mitigated barren plateaus. The designed quantum neural network demonstrates the efficiency in binary and multi-class classification tasks. This work establishes a foundation for the classification of multi-qubit quantum states and offers the potential for generalisation to multi-qubit pure quantum states.

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arXiv2025

Exploring the Non-Markovian Dynamics in Depolarizing Maps

The non-Markovian depolarizing channel is explored from the perspective of understanding its non-Markovian behavior as well as the occurrence of singularities. The study brings together the various ways to identify and quantify non-Markovianity. This includes dynamical techniques such as quantum information backflow witness, Breuer-Laine-Piilo, Rivas-Huelga-Plenio and Hall- Cresser-Li-Andersson measures. In addition, geometrical visualization of non-Markovian effects is presented using the variation in the volume of accessible states during dynamical evolution. Further, a trajectory-based visualization of the dynamical map within the parameter space is presented. The trajectories traced during evolution demonstrate the loss of CP divisibility and the emergence of non-Markovianity under systematic variations of the system parameters. The effects of increasing system dimensions and qubit numbers on singularity and non-Markovianity are presented, with an extension of characterization techniques to higher-dimensional systems.

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arXiv2024

Harnessing Quantum Support Vector Machines for Cross-Domain Classification of Quantum States

In the present study, we use cross-domain classification using quantum machine learning for quantum advantages to readdress the entanglement versus separability paradigm. The inherent structure of quantum states and its relation to a particular class of quantum states are used to intuitively classify testing states from domains different from training states, called \textit{cross-domain classification}. Using our quantum machine learning algorithm, we demonstrate efficient classifications of two-qubit mixed states into entangled and separable classes. For analyzing the quantumness of correlations, our model adequately classifies Bell diagonal states as zero and non-zero discord states. In addition, we also extend our analysis to evaluate the robustness of our model using random local unitary transformations. Our results demonstrate the potential of the quantum support vector machine for classifying quantum states across the multi-dimensional Hilbert space in comparison to classical support vector machines and neural networks.

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arXiv2024

Towards realization of universal quantum teleportation using weak measurements

In this manuscript, we analyze universal quantum teleportation in the presence of memory or memory-less dynamics with applications of partial collapse measurement operators. Our results show that the combined effects of memory or non-Markovianity and weak measurements can lead to universal quantum teleportation (UQT). Our study involves noise models of physical importance having characteristic Markovian and non-Markovian regions allowing one to observe a transition in quantum properties as one switches from non-Markovian to Markovian dynamics. For this, we characterize the effects of different types of non-Markovianity for efficient UQT both due to retention of correlations for a longer duration and due to information backflow. We further analyze memory effects arising from a correlated channel with or without weak measurements. Interestingly, our analysis for a correlated amplitude damping channel shows that memory effects are of significant advantage to minimize the fidelity deviation. The presence of weak measurements further enhances the realization of UQT in the presence of memory. The ability of memory effects in achieving zero fidelity deviation at non-zero time is interesting and of experimental importance.

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arXiv2024

Facets of correlated non-Markovian channels

We investigate the domain of correlated non-Markovian channels, exploring the potential memory arising from the correlated action of channels and the inherent memory due to non-Markovian dynamics. The impact of channel correlations is studied using different non-Markovianity indicators and measures. In addition, the dynamical aspects of correlated non-Markovian channels, including entanglement dynamics as well as changes in the volume of accessible states, are explored. The analysis is presented for both unital and non-unital correlated channels. A new correlated channel constructed with modified Ornstein-Uhlenbeck noise is also presented and explored. Further, the geometrical effects of the non-Markovianity of the correlated non-Markovian channels are discussed with a study of change in the volume of the accessible states. The link between the correlation factor and error correction success probability is highlighted.

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