arXiv subjects

Abhishek Bhatt

Publications and source records attributed to Abhishek Bhatt.

4 recordsLinked to original sources

arXiv2026

Full-stack Physics-level model of cascaded entanglement links

While the last few decades have seen a proliferation of experimental demonstrations of entanglement sources, practicality of deployment has been a secondary concern. Recently, the ZALM source was introduced, as a well-engineered functional device, easily integrated within a complete networking system. It addresses numerous concerns which make typical academic demonstrations less practical: reliable heralding signals, multiplexing across multiple dimensions, and efficient use of input power. We present a stack of tools for modeling mode-by-mode a ZALM source under realistic conditions, in isolation or as a part of a complete network testbed. Our modeling formalism builds upon a hybrid Gaussian and non-Gaussian representation, providing a flexible tradeoff between performance and accuracy, while also greatly simplifying the exact calculation of otherwise expensive scalar figures of merit. This toolkit, implemented in the Python package called "genqo", is integrated within the QuantumSavory full-stack simulator and the QuantumSymbolics computer algebra system. We use this software stack to demonstrate a number of complete networking protocols built upon the ZALM source.

quant-ph↗

arXiv2025

QuantumSavory: Write Symbolically, Run on Any Backend -- A Unified Simulation Toolkit for Quantum Computing and Networking

Progress in quantum computing and networking depends on codesign across abstraction layers: device-level noise and heterogeneous hardware, algorithmic structure, and distributed classical control. We present QuantumSavory, an open-source toolkit built to make such end-to-end studies practical by cleanly separating a symbolic computer-algebra frontend from interchangeable numerical simulation backends. States, operations, measurements, and protocol logic are expressed in a backend-agnostic symbolic language; the same model can be executed across multiple backends (e.g., stabilizer, wavefunction, phase-space), enabling rapid exploration of accuracy-performance tradeoffs without rewriting the model. Furthermore, new custom backends can be added via a small, well-defined interface that immediately reuses existing models and protocols. QuantumSavory also addresses the classical-quantum interaction inherent to LOCC protocols via discrete-event execution and a tag/query system for coordination. Tags attach structured classical metadata to quantum registers and message buffers, and queries retrieve, filter, or wait on matching metadata by wildcards or arbitrary predicates. This yields a data-driven control plane where protocol components coordinate by publishing and consuming semantic facts (e.g., resource availability, pairing relationships, protocol outcomes) rather than by maintaining rigid object graphs or bespoke message plumbing, improving composability and reuse as models grow. Our toolkit is also not limited to qubits and Bell pairs; rather, any networking dynamics of any quantum system under any type of multipartite entanglement can be tackled. Lastly, QuantumSavory ships reusable libraries of standard states, circuits, and protocol building blocks with consistent interfaces, enabling full-stack examples to be assembled, modified, and compared with minimal glue code.

quant-ph↗

arXiv2023

Ultrafast Third-Order Nonlinear Optical Response of Charge Coupled Gold Nanoparticle-Ge24Se76 Heterostructure

The donor-acceptor interaction of a charge-coupled heterostructure encompassing a metal and an amorphous semiconductor subjected to a laser field has many potential applications in the realm of nonlinear optics. In this work, we fabricate an electron donor gold nanoparticle (AuNP) and acceptor amorphous Ge24Se76 heterostructure on a quartz substrate using a sequential thermal evaporation technique. In this charge-coupled heterostructure, we demonstrate the ultrafast third-order nonlinear absorptive and refractive response and their sign reversal compared to pristine Ge24Se76. Enhanced optical nonlinearity in these heterostructures of varying plasmonic wavelengths is due to charge transfer, verified by the Raman spectroscopy. Further, the ultrafast transient absorption measurements support the thesis of charge transfer in the AuNP/Ge24Se76 heterostructure. These findings open up exciting opportunities for developing novel device technologies with far-reaching applications in nonlinear optics.

cond-mat.mtrl-sci↗

arXiv2022

Adversarial synthesis based data-augmentation for code-switched spoken language identification

Spoken Language Identification (LID) is an important sub-task of Automatic Speech Recognition(ASR) that is used to classify the language(s) in an audio segment. Automatic LID plays an useful role in multilingual countries. In various countries, identifying a language becomes hard, due to the multilingual scenario where two or more than two languages are mixed together during conversation. Such phenomenon of speech is called as code-mixing or code-switching. This nature is followed not only in India but also in many Asian countries. Such code-mixed data is hard to find, which further reduces the capabilities of the spoken LID. Hence, this work primarily addresses this problem using data augmentation as a solution on the on the data scarcity of the code-switched class. This study focuses on Indic language code-mixed with English. Spoken LID is performed on Hindi, code-mixed with English. This research proposes Generative Adversarial Network (GAN) based data augmentation technique performed using Mel spectrograms for audio data. GANs have already been proven to be accurate in representing the real data distribution in the image domain. Proposed research exploits these capabilities of GANs in speech domains such as speech classification, automatic speech recognition, etc. GANs are trained to generate Mel spectrograms of the minority code-mixed class which are then used to augment data for the classifier. Utilizing GANs give an overall improvement on Unweighted Average Recall by an amount of 3.5% as compared to a Convolutional Recurrent Neural Network (CRNN) classifier used as the baseline reference.

eess.AS↗