arXiv subjects

Arvind

Publications and source records attributed to Arvind.

At least 19 recordsLinked to original sources

arXiv2026

Neural networks learn to reconstruct multipartite entanglement from quantum marginals

Different sets of local correlations are not equivalent: some fragments of reduced data uniquely determine a global quantum state, while others leave it ambiguous. The quantum marginal problem asks whether a collection of reduced density matrices uniquely determines a compatible global quantum state. Although generic quantum states are uniquely specified by suitable sets of marginals, different collections of marginals are not equally informative: some uniquely determine the global state, whereas others leave it ambiguous. Identifying when uniqueness holds, and reconstructing the global state from partial information, remains computationally demanding and experimentally challenging. We show that information about the multipartite entanglement class and reconstructability in four-qubit systems is compactly encoded in small sets of two- and three-qubit marginals. Using semidefinite programming, we chart the reconstructability landscape across 49 inequivalent SLOCC entanglement classes and show that uniqueness strongly depends on both entanglement structure and marginal order. Neural networks trained only on reduced density matrices learn this structure directly. They accurately classify marginal reconstructability and, when uniqueness holds, reconstruct the full four-qubit density matrix with high fidelity from two- and three-qubit marginals. We benchmark the approach on a four-qubit nuclear magnetic resonance quantum processor and demonstrate that reconstructions from experimentally measured marginals remain faithful despite phase damping and control imperfections. Our results show that neural networks can learn when local correlations uniquely specify a global quantum state, and reveal how global quantum structure is encoded in reduced data.

quant-ph↗

arXiv2025

Graph theoretic quantum contextuality and unextendible Product Bases

Unextendible product bases(UPBs) are central to the study of local distinguishability of orthogonal product states. While their connection to quantum nonlocality via Bell inequalities is well established, their link to quantum contextuality remains largely unexplored. We establish a graph theoretic connection between contextuality and UPBs. First, an equivalence between Klyachko-Can-Binicio\u{g}lu-Shumovsky (KCBS) vectors and the Pyramid UPB is shown and then by constructing a one parameter family of UPB vectors, a quantitative connection between `contextuality strength' and bound entanglement of states associated with the corresponding UPB is demonstrated. This equivalence is extended to generalized KCBS vectors and the GenPyramid UPB. A new class of minimal UPBs in $\mathbb{C}^3 \otimes \mathbb{C}^n$ is constructed using Lov\'asz-optimal orthogonal representations (LOORs) of cycle graphs and their complements which we term the GenContextual UPB. Any minimal UPB in this dimension is shown to be graph-equivalent to the GenContextual UPB. We briefly discuss the distinguishability properties of GenContextual UPB. In the reverse direction, we observe that the constituent vectors of the QuadRes UPB are LOORs of Paley graphs. The structural properties of these graphs make them suitable candidates for constructing noncontextuality inequalities, thereby establishing a bidirectional connection between quantum contextuality and UPBs.

quant-ph↗

arXiv2025

Noisy dynamics of Gaussian entanglement: a transient bound entangled phase before separability

We discover a new class of Gaussian bound entangled states of four-mode continuous-variable systems. These states appear as a transient phase when certain NPT-entangled Gaussian states are evolved under a noisy environment. A thermal bath comprising of harmonic oscillators is allowed to interact with one or modes of the system and a wide variety of initial Gaussian entangled (NPT as well as PPT) states are studied. The robustness of entanglement is defined as the time duration for which the entanglement of the initial state is preserved under the noisy dynamics. We access the separability by utilizing standard semi-definite programming techniques. While most states lose their entanglement after a certain time across all bi-partitions, an exception is observed for a three-parameter family of states which we call the generalized four-mode squeezed vacuum (gFMSV) states, which transitions to a bound entangled state, and remains so for a finite window of time. This dynamical onset of bound entanglement in continuous-variable systems is the central observation of our work. We carry out the analysis for Haar-random four-mode states (both pure and mixed) to scan the state space for transient bound entangled phase

quant-ph↗

arXiv2025

Continuous variable quantum teleportation, $U(2)$ invariant squeezing and non-Gaussian resource states

We investigate the role of quadrature squeezing in the quantum teleportation protocol for coherent states, using non-Gaussian resource states. For the two-mode systems, the non-Gaussian resource states that we use are obtained by an experimentally realizable scheme of photon subtraction, photon addition, and photon catalysis, on the two-mode squeezed vacuum, and two-mode squeezed thermal states. We first analyze the non-classical attribute of quadrature squeezing in these generated non-Gaussian states using the $U(2)$ invariant squeezing approach, which allows us to account for all possible quadratures. We then show that the presence of such non-classicality in non-Gaussian resource states is not necessary for successful quantum teleportation, a finding which is at variance with an earlier result in this direction. This result is important since it demonstrates how non-classicality other than quadrature squeezing present in the resource can be utilized for quantum teleportation.

quant-ph↗

arXiv2025

Leveraging ASIC AI Chips for Homomorphic Encryption

Homomorphic Encryption (HE) provides strong data privacy for cloud services but at the cost of prohibitive computational overhead. While GPUs have emerged as a practical platform for accelerating HE, there remains an order-of-magnitude energy-efficiency gap compared to specialized (but expensive) HE ASICs. This paper explores an alternate direction: leveraging existing AI accelerators, like Google's TPUs with coarse-grained compute and memory architectures, to offer a path toward ASIC-level energy efficiency for HE. However, this architectural paradigm creates a fundamental mismatch with SoTA HE algorithms designed for GPUs. These algorithms rely heavily on: (1) high-precision (32-bit) integer arithmetic to now run on a TPU's low-throughput vector unit, leaving its high-throughput low-precision (8-bit) matrix engine (MXU) idle, and (2) fine-grained data permutations that are inefficient on the TPU's coarse-grained memory subsystem. Consequently, porting GPU-optimized HE libraries to TPUs results in severe resource under-utilization and performance degradation. To tackle above challenges, we introduce CROSS, a compiler framework that systematically transforms HE workloads to align with the TPU's architecture. CROSS makes two key contributions: (1) Basis-Aligned Transformation (BAT), a novel technique that converts high-precision modular arithmetic into dense, low-precision (INT8) matrix multiplications, unlocking and improving the utilization of TPU's MXU for HE, and (2) Memory-Aligned Transformation (MAT), which eliminates costly runtime data reordering by embedding reordering into compute kernels through offline parameter transformation. CROSS (TPU v6e) achieves higher throughput per watt on NTT and HE operators than WarpDrive, FIDESlib, FAB, HEAP, and Cheddar, establishing AI ASIC as the SotA efficient platform for HE operators. Code: https://github.com/EfficientPPML/CROSS

cs.CR↗

arXiv2025

A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction

Detecting and interpreting operator actions, engagement, and object interactions in dynamic industrial workflows remains a significant challenge in human-robot collaboration research, especially within complex, real-world environments. Traditional unimodal methods often fall short of capturing the intricacies of these unstructured industrial settings. To address this gap, we present a novel Multimodal Industrial Activity Monitoring (MIAM) dataset that captures realistic assembly and disassembly tasks, facilitating the evaluation of key meta-tasks such as action localization, object interaction, and engagement prediction. The dataset comprises multi-view RGB, depth, and Inertial Measurement Unit (IMU) data collected from 22 sessions, amounting to 290 minutes of untrimmed video, annotated in detail for task performance and operator behavior. Its distinctiveness lies in the integration of multiple data modalities and its emphasis on real-world, untrimmed industrial workflows-key for advancing research in human-robot collaboration and operator monitoring. Additionally, we propose a multimodal network that fuses RGB frames, IMU data, and skeleton sequences to predict engagement levels during industrial tasks. Our approach improves the accuracy of recognizing engagement states, providing a robust solution for monitoring operator performance in dynamic industrial environments. The dataset and code can be accessed from https://github.com/navalkishoremehta95/MIAM/.

cs.CV↗

arXiv2025

Optimizing Multitask Industrial Processes with Predictive Action Guidance

Monitoring complex assembly processes is critical for maintaining productivity and ensuring compliance with assembly standards. However, variability in human actions and subjective task preferences complicate accurate task anticipation and guidance. To address these challenges, we introduce the Multi-Modal Transformer Fusion and Recurrent Units (MMTFRU) Network for egocentric activity anticipation, utilizing multimodal fusion to improve prediction accuracy. Integrated with the Operator Action Monitoring Unit (OAMU), the system provides proactive operator guidance, preventing deviations in the assembly process. OAMU employs two strategies: (1) Top-5 MMTF-RU predictions, combined with a reference graph and an action dictionary, for next-step recommendations; and (2) Top-1 MMTF-RU predictions, integrated with a reference graph, for detecting sequence deviations and predicting anomaly scores via an entropy-informed confidence mechanism. We also introduce Time-Weighted Sequence Accuracy (TWSA) to evaluate operator efficiency and ensure timely task completion. Our approach is validated on the industrial Meccano dataset and the largescale EPIC-Kitchens-55 dataset, demonstrating its effectiveness in dynamic environments.

cs.CV↗

arXiv2024

Experimental investigation of a quantum Otto heat engine with shortcuts to adiabaticity implemented using counter-adiabatic driving

The finite time operation of a quantum Otto heat engine leads to a trade-off between efficiency and output power, which is due to the deviation of the system from the adiabatic path. This trade-off caveat can be bypassed by using the shortcut-to-adiabaticity protocol. We experimentally implemented a quantum Otto heat engine using spin-1/2 nuclei on a nuclear magnetic resonance (NMR) quantum processor. We investigated its performance using the shortcut-to-adiabaticity technique via counter-adiabatic driving with the inclusion of the cost to perform the shortcut. We use two different metrics that incorporate the cost of shortcut-to-adiabaticity to define engine efficiency and experimentally analyze which one is more appropriate for the NMR platform. We found a significant improvement in the performance of the quantum Otto heat engine driven by shortcut-to-adiabaticity, as compared to the non-adiabatic heat engine.

quant-ph↗

arXiv2024

Simulating Three-Flavor Neutrino Oscillations on an NMR Quantum Processor

Neutrino oscillations can be efficiently simulated on a quantum computer using the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) theory in close analogy to the physical processes realized in experiments. We simulate three-flavor neutrino oscillations on a two-qubit NMR quantum information processor. The three-flavor neutrino states were encoded into the two-qubit system, leaving one redundant basis state (representing an unphysical sterile neutrino). We simulated the neutrino oscillations in different scenarios, including propagation in vacuum and through surrounding matter, and both with and without a CP violating phase $\delta$ at a Deep Underground Neutrino Experiment (DUNE) baseline distance of L = 1285 km. The oscillation probabilities were obtained after unitarily time evolving the initial flavor state and and comparisons were performed between different scenarios. Further, we design and implement an optimized quantum circuit encoding all four parameters (three mixing angles-$\theta_{12},\theta_{23},\theta_{13}$ and one complex phase $\delta$) to implement the PMNS unitary matrix, which relates the flavor and mass eigenstates. The interaction with matter is considered as a perturbation to the vacuum Hamiltonian, and the same quantum circuit is employed with an approximation of two mixing angles ($\theta_{12}$ and $\theta_{13}$ ) and mass eigenvalues. Our experimental results match well with numerical simulations and highlight the potential of quantum computers for exploring the physics of neutrino oscillations.

quant-ph↗

arXiv2024

Experimental investigation of coherence contributions to a nonequilibrium thermodynamic process in a driven quantum system

The work done when a system at thermal equilibrium is externally driven by a unitary control parameter leads to irreversible entropy production. The entropy produced can be thought of as a combination of coherence generation and a population mismatch between the target equilibrium state and the actually achieved final state. We experimentally explored this out-of-equilibrium process in an NMR quantum processor and studied the contribution of coherence to irreversible entropy generation. We verified a generalized Clausius inequality, which affirms that irreversible entropy production is lower-bounded.

quant-ph↗

arXiv2024

Entanglement Classification of Arbitrary Three-Qubit States via Artificial Neural Networks

We design and successfully implement artificial neural networks (ANNs) to detect and classify entanglement for three-qubit systems using limited state features. The overall design principle is a feed forward neural network (FFNN), with the output layer consisting of a single neuron for the detection of genuine multipartite entanglement (GME) and six neurons for the classification problem corresponding to six entanglement classes under stochastic local operations and classical communication (SLOCC). The models are trained and validated on a simulated dataset of randomly generated states. We achieve high accuracy, around 98%, for detecting GME as well as for SLOCC classification. Remarkably, we find that feeding only 7 diagonal elements of the density matrix into the ANN results in an accuracy greater than 94% for both the tasks, showcasing the strength of the method in reducing the required input data while maintaining efficient performance. Reducing the feature set makes it easier to apply ANN models for entanglement classification, particularly in resource-constrained environments, without sacrificing accuracy. The performance of the ANN models was further evaluated by introducing white noise into the data set, and the results indicate that the models are robust and are able to well tolerate noise.

quant-ph↗

arXiv2024

Evolution of different orders of coherence of a three-qubit system and their protection via dynamical decoupling on an NMR quantum processor

We generate different orders of quantum coherence in a three-qubit NMR system and study their dynamics in the presence of inherent noise. Robust dynamical decoupling (DD) sequences are applied to preserve the different coherence orders. Initially, DD sequences are implemented simultaneously on all three spins, which effectively protects third-order coherence; however, other coherence orders decay rapidly instead of being preserved. The robust DD sequences were suitably modified in order to preserve other coherence orders. These sequences are applied to the two participating qubits that generate each zero and second order coherence, ensuring their effective preservation. In contrast, first-order coherence is preserved more efficiently when DD sequences are applied exclusively on the qubit responsible for generating it. Instead of performing full state tomography, coherence orders are measured directly using single pulses. The robust DD protection schemes are finally applied to successfully protect two-qubit entanglement in three-qubit star states.

quant-ph↗

arXiv2024

Using a quantum SWAP engine to experimentally validate thermodynamic uncertainty relations

Thermodynamic uncertainty relations (TURs) arise from the bounds on fluctuations of thermodynamics quantities during a non-equilibrium process and they impose constraints on the corresponding process. We experimentally implement a quantum SWAP engine on a nuclear magnetic resonance setup and demonstrate that a Gibbs thermal state can be prepared in two different ways, either directly from a thermal equilibrium state, or by first initializing the system in a pseudopure state. We show that the quantum SWAP engine can work both as a heat engine and as a refrigerator. Starting from a pseudopure state, we construct the SWAP engine, and investigate the violation of two different TURs, namely a generalized TUR and a tighter, more specific TUR. Our results validate that the generalized TUR is obeyed in all the working regimes of the SWAP engine, while the tighter TUR is violated in certain regimes. ~

quant-ph↗

arXiv2024

Identifying Money Laundering Subgraphs on the Blockchain

Anti-Money Laundering (AML) involves the identification of money laundering crimes in financial activities, such as cryptocurrency transactions. Recent studies advanced AML through the lens of graph-based machine learning, modeling the web of financial transactions as a graph and developing graph methods to identify suspicious activities. For instance, a recent effort on opensourcing datasets and benchmarks, Elliptic2, treats a set of Bitcoin addresses, considered to be controlled by the same entity, as a graph node and transactions among entities as graph edges. This modeling reveals the "shape" of a money laundering scheme - a subgraph on the blockchain. Despite the attractive subgraph classification results benchmarked by the paper, competitive methods remain expensive to apply due to the massive size of the graph; moreover, existing methods require candidate subgraphs as inputs which may not be available in practice. In this work, we introduce RevTrack, a graph-based framework that enables large-scale AML analysis with a lower cost and a higher accuracy. The key idea is to track the initial senders and the final receivers of funds; these entities offer a strong indication of the nature (licit vs. suspicious) of their respective subgraph. Based on this framework, we propose RevClassify, which is a neural network model for subgraph classification. Additionally, we address the practical problem where subgraph candidates are not given, by proposing RevFilter. This method identifies new suspicious subgraphs by iteratively filtering licit transactions, using RevClassify. Benchmarking these methods on Elliptic2, a new standard for AML, we show that RevClassify outperforms state-of-the-art subgraph classification techniques in both cost and accuracy. Furthermore, we demonstrate the effectiveness of RevFilter in discovering new suspicious subgraphs, confirming its utility for practical AML.

cs.LG↗

arXiv2024

ANN-Enhanced Detection of Multipartite Entanglement in a Three-Qubit NMR Quantum Processor

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.

quant-ph↗

arXiv2024

Experimental decoherence mitigation using a weak measurement-based scheme and the duality quantum algorithm

We experimentally demonstrate a weak measurement and measurement reversal-based scheme to ameliorate the effects of decoherence due to amplitude damping, on an NMR quantum processor. The weak measurement and measurement reversal processes require the implementation of non-unitary operations, which are typically infeasible on conventional quantum processors, where only unitary quantum operations are allowed. The duality quantum algorithm is used to efficiently implement the required non-unitary quantum operations corresponding to weak measurement and measurement reversal. We experimentally validate the efficacy of the weak measurement-based decoherence mitigation scheme by showing state protection on a four-qubit system, with one qubit being designated as the 'system qubit', while the remaining three qubits serve as 'ancilla qubits'. Our experimental results clearly demonstrate the success of the weak measurement-based decoherence mitigation scheme in protecting the desired state. Since the measurement process involved has trace less than unity, the scheme can be thought of as a filtration scheme, where a subset of the spins is protected while the rest of the spins can be discarded.

quant-ph↗

arXiv2024

Continuous variable quantum teleportation using photon subtracted and photon added two mode squeezed coherent state

We consider non-Gaussian states generated by photon subtraction (PS) and photon addition (PA) on two-mode squeezed coherent (TMSC) states, as resource states for continuous variable (CV) quantum teleportation (QT). To this end, we derive the Wigner characteristic function for the family of photon subtracted and photon added TMSC states, which is then utilized to calculate the fidelity of teleporting a single mode coherent state and a squeezed vacuum state. The analysis shows that while symmetric PS enhances the fidelity of QT in an extensive range of squeezing, asymmetric PS enhances the performance marginally and only in the low squeezing regime. The addition operations on the other hand are less useful, symmetric three-PA leads to a marginal improvement while the other addition operations are useless. We have considered the actual experimental setup for PS and PA operations and computed their success probabilities which should be kept in mind while advocating the use of these operations. We could compute the fidelity of QT for a broad range of states because we analytically derived the Wigner characteristic function for these family of states which we think will be useful for various other applications of these families of states.

quant-ph↗

arXiv2024

Ground and excited state energy calculations of the H2 molecule using a variational quantum eigensolver algorithm on an NMR quantum simulator

Variational quantum algorithms are emerging as promising candidates for near-term practical applications of quantum information processors, in the field of quantum chemistry. We implement the variational quantum eigensolver algorithm to calculate the molecular ground-state energy of the H2 molecule and experimentally demonstrated it on an NMR quantum processor. Further, we simulate the excited states of the H2 molecule using the variational quantum deflation algorithm and experimentally demonstrate it on the same NMR quantum processor. We also develop the first simulation of the energy calculation of the H2 molecule using only a single qubit, and verify the results on an NMR quantum computer. Our experimental results demonstrate that only a single NMR qubit suffices to calculate the molecular energies of the H2 molecule to the desired accuracy.

quant-ph↗