arXiv subjects

Gautami Sanjay Naik

Publications and source records attributed to Gautami Sanjay Naik.

5 recordsLinked to original sources

arXiv2026

Enabling Quantum Natural Language Processing for Hindi Language

Quantum Natural Language Processing (QNLP) is taking huge leaps in solving the shortcomings of classical Natural Language Processing (NLP) techniques and moving towards a more "Explainable" NLP system. The current literature around QNLP focuses primarily on implementing QNLP techniques in sentences in the English language. In this paper, we propose to enable the QNLP approach to HINDI, which is the third most spoken language in South Asia. We present the process of building the parameterized quantum circuits required to undertake QNLP on Hindi sentences. We use the pregroup representation of Hindi and the DisCoCat framework to draw sentence diagrams. Later, we translate these diagrams to Parameterised Quantum Circuits based on Instantaneous Quantum Polynomial (IQP) style ansatz. Using these parameterized quantum circuits allows one to train grammar and topic-aware sentence classifiers for the Hindi Language.

cs.CL↗

arXiv2026

Scaling Hindi Quantum Natural Language Processing through Automatic Pregroup Supertagging

Quantum Natural Language Processing (QNLP) uses pregroup grammars to translate grammatical structure into diagrammatic representations and quantum circuits. Recent Hindi QNLP work has shown that Hindi-specific pregroup grammars can support grammar-sensitive compositional models, but grammatical type assignment is still largely manual, limiting scalability. This paper formulates automatic Hindi pregroup supertagging as a token-level classification task. Using a manually annotated corpus of 380 Hindi sentences, we evaluate lexical, contextual, prompting-based, lexical-repair, and suffix/morphology-aware methods. Results show that simple lexical and contextual models are strong in this low-resource setting: contextual backoff achieves the best completed accuracy of 64.56\%, while raw Qwen2.5 prompting reaches only 11.65\%. Lexical repair raises LLM-assisted prediction to 64.08\%, demonstrating the value of constraining generative outputs with symbolic grammar knowledge. Diagnostic analysis further shows that seen and unambiguous tokens are much easier than unseen tokens, and suffix/morphology features improve karaka-token accuracy but not overall performance. These results show that automatic Hindi pregroup assignment is feasible and can reduce reliance on manual annotation in future multilingual QNLP pipelines.

cs.CL↗

arXiv2026

QEVOLVE-Bench: A Seed Benchmark for Quantum SDK Evolution and Repair Planning

Quantum software is increasingly built on fast-moving Python SDKs such as Qiskit, PennyLane, and Cirq. When these SDKs evolve, user programs can fail because execution helpers are removed, import paths change, simulator abstractions shift, device names are deprecated, or circuit export interfaces are revised. Although some failures surface as simple missing-symbol errors, repairing them is not only a syntactic problem: developers must identify the replacement workflow, preserve the quantum-facing intent, and validate behavior under pinned framework versions. This paper presents QEVOLVE-Bench, a small executable seed benchmark for quantum SDK evolution. The current public release contains 14 controlled migration tasks across Qiskit, PennyLane, and Cirq. Each task is a self-contained Python project with pinned dependencies, failing and passing outputs, pytest acceptance checks, metadata, and benchmark notes. The artifact also includes a manifest, sanity checker, representative smoke-test logs, Zenodo archive, screencast, and a lightweight QEVOLVE-Agent prototype that converts task evidence into structured repair proposals. QEVOLVE-Bench is intentionally not a statistically powered benchmark; instead, it is an extensible seed artifact that lowers the setup cost for studying automated repair, LLM-based software engineering, and maintenance of quantum SDK-based projects.

quant-ph↗

arXiv2026

Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design

Quantum reservoir computing (QRC) uses fixed quantum dynamics as a high-dimensional temporal feature map and trains only a lightweight classical readout. QRC is attractive for near-term quantum machine learning, but its performance depends strongly on architecture choices such as input encoding, reservoir depth, entanglement topology, measurement features, state-reset policy, feature construction, and readout regularization. We introduce \method, a simulator-based benchmark that formulates QRC design as constrained black-box architecture search and evaluates whether large language models can act as proposal controllers for this search problem. The benchmark compares five policies under identical evaluation budgets: random search, evolutionary search, Bayesian/TPE optimization, a feedback-based LLM agent, and \hybrid, which combines LLM proposals with memory, mutation, crossover, duplicate avoidance, and exploration. On NARMA10, Mackey-Glass forecasting, and temporal parity, \hybrid{} is the most consistent policy: it ranks first on NARMA10 and temporal parity and second on Mackey-Glass, narrowly behind evolutionary search. Under a 25-evaluation budget and three seeds, \hybrid{} improves over random search on all tasks, including a 23.6\% relative reduction in Mackey-Glass error. The results do not show that LLMs are universal QRC optimizers; rather, they show that generative models can be useful high-level controllers when embedded inside validated, reproducible hybrid search loops.

quant-ph↗

arXiv2026

Extending the Frontiers of QNLP Beyond English: Grammar-Sensitive Pipeline for Hindi Sentiment Classification Using Compositional Quantum Models

Advancements in Natural Language Processing (NLP), whether on classical or quantum platforms, have predominantly focused on English due to its widespread use and abundant linguistic resources. Although English remains the most studied language in computational linguistics, Hindi, the third most spoken language worldwide after Mandarin, has received comparatively limited attention. Spoken primarily in India, Hindi differs significantly from English in its script, syntactic structure, and linguistic characteristics. Hindi uses the Devanagari script, exhibits rich morphological inflection, and follows a subject-object-verb (SOV) word order, unlike English's subject-verb-object (SVO) structure. Motivated by Hindi's linguistic complexity and its underrepresentation in Quantum Natural Language Processing (QNLP), we propose a grammar-aware QNLP pipeline for Hindi sentiment classification with a focus on sentential negation. We use a manually annotated Hindi sentiment dataset labeled as positive, negative, or neutral, and encode sentences using pregroup grammar types. Sentences are processed with Lambeq to generate quantum circuits using a novel negation-aware compositional grammar. Hybrid Quantum Neural Networks (HQNNs) are trained for both binary and ternary sentiment classification. Our results demonstrate effective sentiment classification and highlight the potential of compositional QNLP for morphologically rich languages.

quant-ph↗