Publications
Publications/Manuscript
Is genuine network nonlocality exclusive to pure states?
A preprint on the work has been submitted to arxiv: https://arxiv.org/abs/2501.08079
Abstract
Genuine network non-locality (GNN) refers to the existence of quantum correlations in a network with independent sources that cannot be explained by local hidden-variables (LHV) models. Even in the simplest scenario, determining whether these quantum correlations remain genuinely network non-local when derived from entangled states that deviate from their ideal forms is highly challenging due to the non-convex nature of local correlations. Understanding the boundary of these correlations thus becomes a hard problem, but one that raises academic interest specifically its robustness to noise. To address this problem, we introduce a causal domain-informed learning algorithm called the LHV $k$-rank Neural Network, which assesses the rank parameter of the non-ideal combined state produced by sources. Applied to the triangle network scenario with the three sources generating a class of quantum states known as $X$ states, the neural network reveals that nonlocality persists only if the states remain pure. Remarkably, we find that even slight deviations from ideal Bell states due to noise cause GNN to vanish, exhibiting a discrete behavior that hasn’t been witnessed in the standard bell scenario. This finding thus raises a fundamental question of whether GNN in the triangle network is exclusive to pure states or not. Additionally, we explore the case of the three sources producing dissimilar states, indicting GNN requires all its sources to send pure entangled states with joint entangled measurements as resources. Apart from these results the work succeeds in showing that machine learning approaches with domain specific constraints can greatly benefit the field of quantum foundations.
Code Availability: Check here https://github.com/ananthrishna/GNN-LHV-k-triangle
Conference Papers
Poster presentation on “Is Genuine Network Nonlocality exclusive to Pure States” - Presented at the 24th International Conference on Quantum Communication, Measurment and Computing at IIT Madras, Chennai, India. Download PDF
Poster presentation on Quantum Network Nonlocality using LHV-Neural Network Models - Presented at Frontier Symposium Physics 2024 at IISER Thiruvananthapuram, Kerala, India.
Masters Thesis - Quantum Network Nonlocality
Minor Thesis - Superadditivity of Coherent Information in Noisy Quantum Channels
Abstract
Machine learning has greatly improved the attainability of solutions when it comes to challenging problems such as those in fundamental research. Here, we use machine learning to tackle a quantum information scenario. Quantum channels exhibit a convenient property called superadditivity which can improve transmission rates in quantum information channels. In this work we found quantum states expressing Superadditivity using neural network variational ansatz and optimization algorithms; through this we explored the usefulness of using neural network as a variational state ansatz for representing Quantum qubit states in the context of quantum information-processing tasks, and also the efficiency of using different optimization algorithms to find the better case. (a) Neural network states yield quantum codes with a high Coherent-information for Qubit pauli channels, Dephrasure channel and De polarizing channel; such codes have proven to outperform all other known codes for these channels. (b) It has also been shown that the quantum codes for single channel cases were given by repetition codes. (c) Using genetic algorithm has proved to be more effective than using simple gradient-based methods for optimizing the neural network ansatz representing the states.