Abstract: Quantum entanglement lies at the heart of the ongoing quantum technological revolution, underpinning breakthroughs in quantum communication and networking, distributed quantum computing, and secure cryptographic protocols. However, as quantum devices transition from isolated laboratory demonstrations to scalable, real-world technologies, a critical bottleneck emerges: How can we efficiently certify the presence of entanglement and verify its quality for large-scale operational tasks?

This dissertation addresses this challenge by introducing a pioneering quantum-classical framework for provably efficient entanglement certification and quality assessment. By reframing operational entanglement inference as a problem of adaptive sequential learning, the proposed framework bridges sequential decision-making theory with quantum information science, achieving resource efficiency over conventional approaches while maintaining rigorous statistical guarantees. The defense will detail two primary contributions: Part I: Entanglement Certification: By establishing a pioneering connection between witness-based entanglement detection tests and modern multi-armed bandit theory, we introduce experimental measurement strategies that adaptively allocate sample resources in real time based on incoming empirical data. This methodology is validated across two distinct regimes: structured state families, providing finite-confidence guarantees while drastically reducing the required sample complexity, and arbitrary two-qubit states, achieved by synthesizing adaptive measurement allocation, statistical state estimation, and optimization-based certification techniques Part II: Operational Quality Assessment: Moving beyond binary classification, the second part of the dissertation addresses the practically vital problem of quality assessment. In real-world quantum networks, entanglement must satisfy stringent performance benchmarks. To bridge this gap, this work develops distributed measurement protocols designed to estimate operational entanglement measures under realistic locality constraints, establishing explicit copy complexity guarantees that provide clear mathematical bounds on verification resources. Ultimately, this dissertation establishes a principled bridge between quantum information theory, statistical inference, and sequential learning–offering a scalable toolkit to accelerate next-generation quantum infrastructure.

Event Details
Title: Provably Efficient Entanglement Certification and Quality Assessment via Sequential Bandit Learning (PhD viva voce)
Date: July 02, 2026 at 11:00 AM
Venue: Google Meet (https://meet.google.com/zxw-zwdq-nch)
Speaker: Ms. Bharati K (EE20D700)
Guide: Dr. Krishna Jagannathan
Type: PHD seminar

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