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Sanidhya Gupta

Publications and source records attributed to Sanidhya Gupta.

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Boundary-Aware Stabilizer Scheduling for Distributed Quantum Error Correction

Future quantum architectures are expected to be modular, with quantum processors connecting multiple quantum processing units (QPUs) via photonic interconnects. In topological quantum error correction, such as color codes, this creates seam boundaries where parity checks require remote CNOT operations using heralded Bell pairs. These non-local checks are slower and noisier than bulk local checks because entanglement generation is probabilistic, causing data qubits to accumulate idle noise while waiting for remote operations. A natural way to reduce this overhead is to skip some seam measurements; however, doing so makes seam syndrome information stale and can degrade decoding. The central scheduling problem is therefore to determine how frequently seam checks should be measured so as to balance remote-operation and waiting noise against syndrome staleness. To address this trade-off, we develop a scheduling module that integrates directly into standard syndrome-extraction circuits. We consider two policies: Skip-Seam-$\tau$ (SS-$\tau$), which measures all bulk checks every round while measuring seam checks once every $\tau$ rounds and copying the most recent syndrome in skipped rounds, and Adaptive Skip-$\tau$ (AST), which selects $\tau$ as a function of code distance and entanglement generation rate (EGR). We evaluate these policies on triangular color codes under circuit-level noise in Stim, including idling errors induced by Bell-pair generation delays. Our simulations show that SS-tau and AST reduce remote-operation overhead and can lower the logical error rate (LER) relative to the Measure-All (MA) baseline. For physical error rate $p = 10^{-3}$, we identify an EGR regime in which both SS-$\tau$ and AST exhibit behavior consistent with fault-tolerant scaling, with LER decreasing as code distance increases. Across these regimes, SS-$\tau$ and AST outperform MA.

quant-ph

A Provably Secure Framework for Noise-Aware Delegated Quantum Computation and Storage

As large-scale quantum computers become a reality, they will likely exist as centralized cloud resources accessible to a broad user base. Securely delegating private quantum computations to untrusted servers is therefore a foundational challenge. This requires rigorous guarantees of privacy (blindness), correctness (completeness), and integrity against malicious actions (verifiability). This paper presents an integrated architectural framework for noise-aware distributed quantum computation. The framework combines three technical components into a unified system: (1) a distributed stabilizer-code backbone to encode and store quantum states across multiple server nodes, with security analyzed under non-communication and bounded-collusion assumptions; (2) a two-level error-management structure, where each server node can locally handle errors based on its specific noise model; and (3) a trap-based verification protocol to detect malicious deviations with probability controlled by a security parameter. We provide a security analysis showing that, under the stated assumptions, the framework achieves completeness, blindness, and verifiability with respect to the permitted leakage. Our work provides an architectural blueprint for trustworthy distributed quantum computation under explicitly stated assumptions, paving the way for further development of secure quantum cloud services.

quant-ph

Proactive Emotion Tracker: AI-Driven Continuous Mood and Emotion Monitoring

This research project aims to tackle the growing mental health challenges in today's digital age. It employs a modified pre-trained BERT model to detect depressive text within social media and users' web browsing data, achieving an impressive 93% test accuracy. Simultaneously, the project aims to incorporate physiological signals from wearable devices, such as smartwatches and EEG sensors, to provide long-term tracking and prognosis of mood disorders and emotional states. This comprehensive approach holds promise for enhancing early detection of depression and advancing overall mental health outcomes.

cs.HC