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Researcher profile

Sagnik Chatterjee

Sagnik Chatterjee contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceEmerging TechnologiesGenomicsMachine LearningMultiagent Systemsquant-phSoftware Engineering

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Published work

3 published item(s)

preprint2026arXiv

Generalized cross-resonance scheme for maximally-entangling two-qutrit gates

To utilize higher-dimensional quantum systems, in this Letter, we derive a generalized cross-resonance (GCR) scheme for realizing maximally entangling two-qutrit gates on fixed-frequency transmons beyond the 0-1 subspace. Our two-qutrit gates, namely, $U_{CR}^{01}$ and $U_{CR}^{12}$, acting on the $0{\text -}1$ and $1{\text -}2$ energy transitions of transmons, respectively, directly allow for entanglement on the $1{\text -}2$ levels. Unlike the known works, our gate is parametric in nature, enabling us to construct multiple entangling gates of interest. By performing simulations in Qiskit, we demonstrate two-qutrit generalized controlled-$X$ ($U_{CX}^{01}$ and $U_{CX}^{12}$) and controlled-$H$ ($U_{CH}^{01}$ and $U_{CH}^{12}$) gates, which are instances of the proposed $U_{CR}$ gates, with reported gate fidelities of $86.14\%~(99.73\%),~84.6\%~(97.88\%),~92.35\%~(99.39\%)$, and $91.99\%~(98.99\%)$, respectively with (and without) noise. We also reveal a two-qutrit Bell state with a fidelity of $99.06 \pm 0.01\%$, with a complete Bell state preparation in a $\sim514$ ns pulse sequence, which is less than the gate time of the known scheme by cross-Kerr-based entangling gates.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SCOPE: Siamese Contrastive Operon Pair Embeddings for Functional Sequence Representation and Classification

Identifying operons is a fundamental step in understanding prokaryotic gene regulation, as classifying genes into operons supports the reconstruction of regulatory networks, functional annotation of unannotated genes, and drug candidate development. Experimental approaches such as RT-PCR and RNA-seq provide precise evidence of operon structure, but are laborious and largely limited to well-studied model organisms, making scalable computational methods essential for genome-wide operon identification. Prior computational approaches have employed traditional classifiers such as logistic regression and decision trees, motivating our use of these as physicochemical baselines. The DGEB benchmark evaluates operonic pair classification by embedding each sequence independently with a pre-trained protein language model and computing pairwise cosine similarity. In contrast, our Siamese MLP learns a classifier over the fused embedding space, which is theoretically better motivated for binary classification, as cosine similarity can yield meaningless scores depending on the regularization of the embedding model. While protein language model embeddings substantially outperform physicochemical features in ROC-AUC, a learned Siamese MLP head does not significantly improve over unsupervised cosine similarity in Average Precision, suggesting that the geometry of the embedding space already captures the functional relationships needed for this task. Nonetheless, our Siamese MLP achieves a ROC-AUC of 0.71, competitive with state-of-the-art models on the DGEB leaderboard. These findings indicate that protein language model embeddings are a viable, scalable foundation for operonic pair classification across diverse microbial genomes, with implications for automated genome annotation, regulatory network reconstruction, and characterization of organisms lacking experimental operon annotations.

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preprint2026arXiv

Trajectory Supervision for Continual Tool-Use Learning in LLMs

Most language-model training data shows final artifacts, not the process that produced them. We study a tractable version of this question in tool use: when a model learns a stream of new API domains, does keeping tool-use trajectories help compared with stripping the intermediate API trace? We fine-tune Llama 3.1 8B Instruct with QLoRA on API-Bank using four sequential domain blocks. Condition A strips previous API request/response lines from the prompt and trains the model to predict the next API call. Condition B keeps the trajectory context. In a single-seed pilot, full held-out generation evaluation shows that Condition B reaches 56.9\% final exact full-call accuracy compared with 39.2\% for Condition A. B also improves final API-name accuracy by 7.7 points. However, B uses 25.1\% more training tokens, the run uses one seed, and the task is next-call prediction rather than full dialogue success.

0 citations0 reviewsNo code linkedOpen access