activity
20192026
most citedQuantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems

19 citations · 99 across the 20 of their papers we have counts for

collaborators

23 papers

quant-ph2026

High quantum local differential privacy breaks entanglement

Sujeet Bhalerao, Theshani Nuradha, Felix Leditzky

Differential privacy provides a mathematical framework for guaranteeing privacy for sensitive data. In quantum information processing, the interaction of privacy constraints with q…

quant-ph2026

Conditional contraction coefficients and their applications to quantum networks

Christoph Hirche, Ian George, Theshani Nuradha +1

Contraction coefficients quantify the loss of distinguishability induced by a channel and provide a strong form of the data-processing inequality. While standard contraction coeffi…

quant-ph2026

Privacy-Utility Tradeoffs in Quantum Information Processing

Theshani Nuradha, Sujeet Bhalerao, Felix Leditzky

When sensitive information is encoded in data, it is important to ensure the privacy of information when attempting to learn useful information from the data. There is a natural tr…

cs.IT2026

Information Contraction under -Differentially Private Mechanisms

Theshani Nuradha, Ian George, Christoph Hirche

The distinguishability quantified by information measures after being processed by a private mechanism has been a useful tool in studying various statistical and operational tasks…

quant-ph2026

When Does Quantum Differential Privacy Compose?

Daniel Alabi, Theshani Nuradha

Composition is a cornerstone of classical differential privacy, enabling strong end-to-end guarantees for complex algorithms through composition theorems (e.g., basic and advanced)…

quant-ph2025

Non-Linear Strong Data-Processing for Quantum Hockey-Stick Divergences

Theshani Nuradha, Ian George, Christoph Hirche

Data-processing is a desired property of classical and quantum divergences and information measures. In information theory, the contraction coefficient measures how much the distin…

quant-ph2025

Query complexities of quantum channel discrimination and estimation: A unified approach

Zixin Huang, Johannes Jakob Meyer, Theshani Nuradha +1

The goal of quantum channel discrimination and estimation is to determine the identity of an unknown channel from a discrete or continuous set, respectively. The query complexity o…

quant-ph2025★ 1 cited

Query Complexity of Classical and Quantum Channel Discrimination

Theshani Nuradha, Mark M. Wilde

Quantum channel discrimination has been studied from an information-theoretic perspective, wherein one is interested in the optimal decay rate of error probabilities as a function…

quant-ph2025

Quantum Doeblin Coefficients: Interpretations and Applications

Ian George, Christoph Hirche, Theshani Nuradha +1

In classical information theory, the Doeblin coefficient of a classical channel provides an efficiently computable upper bound on the total-variation contraction coefficient of the…

quant-ph2025★ 3 cited

Measured Hockey-Stick Divergence and its Applications to Quantum Pufferfish Privacy

Theshani Nuradha, Vishal Singh, Mark M. Wilde

The hockey-stick divergence is a fundamental quantity characterizing several statistical privacy frameworks that ensure privacy for classical and quantum data. In such quantum priv…

quant-ph2024★ 2 cited

Extendible quantum measurements and limitations on classical communication

Vishal Singh, Theshani Nuradha, Mark M. Wilde

Unextendibility of quantum states and channels is inextricably linked to the no-cloning theorem of quantum mechanics, it has played an important role in understanding and quantifyi…

quant-ph2024★ 9 cited

Contraction of Private Quantum Channels and Private Quantum Hypothesis Testing

Theshani Nuradha, Mark M. Wilde

A quantum generalized divergence by definition satisfies the data-processing inequality; as such, the relative decrease in such a divergence under the action of a quantum channel i…

quant-ph2024★ 5 cited

Multivariate Fidelities

Theshani Nuradha, Hemant K. Mishra, Felix Leditzky +1

The main contribution of our paper is to introduce a number of multivariate quantum fidelities and show that they satisfy several desirable properties that are natural extensions o…

quant-ph2024★ 7 cited

An invitation to the sample complexity of quantum hypothesis testing

Hao-Chung Cheng, Nilanjana Datta, Nana Liu +3

Quantum hypothesis testing (QHT) has been traditionally studied from the information-theoretic perspective, wherein one is interested in the optimal decay rate of error probabiliti…

quant-ph2023★ 5 cited

QSlack: A slack-variable approach for variational quantum semi-definite programming

Jingxuan Chen, Hanna Westerheim, Zoë Holmes +6

Solving optimization problems is a key task for which quantum computers could possibly provide a speedup over the best known classical algorithms. Particular classes of optimizatio…

quant-ph2023★ 3 cited

Dual-VQE: A quantum algorithm to lower bound the ground-state energy

Hanna Westerheim, Jingxuan Chen, Zoë Holmes +6

The variational quantum eigensolver (VQE) is a hybrid quantum-classical variational algorithm that produces an upper-bound estimate of the ground-state energy of a Hamiltonian. As…

quant-ph2023★ 19 cited

Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems

Theshani Nuradha, Ziv Goldfeld, Mark M. Wilde

We propose a versatile privacy framework for quantum systems, termed quantum pufferfish privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and add…

quant-ph2023★ 13 cited

Fidelity-Based Smooth Min-Relative Entropy: Properties and Applications

Theshani Nuradha, Mark M. Wilde

The fidelity-based smooth min-relative entropy is a distinguishability measure that has appeared in a variety of contexts in prior work on quantum information, including resource t…

cs.IT2022★ 10 cited

Pufferfish Privacy: An Information-Theoretic Study

Theshani Nuradha, Ziv Goldfeld

Pufferfish privacy (PP) is a generalization of differential privacy (DP), that offers flexibility in specifying sensitive information and integrates domain knowledge into the priva…

cs.IT2022★ 5 cited

k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension

Ziv Goldfeld, Kristjan Greenewald, Theshani Nuradha +1

Sliced mutual information (SMI) is defined as an average of mutual information (MI) terms between one-dimensional random projections of the random variables. It serves as a surroga…

eess.SP2019

An Energy Efficient D2D Model with Guaranteed Quality of Service for Cloud Radio Access Networks

Isuru Janith Ranawaka, Kasun T. Hemachandra, Tharaka Samarasinghe +1

This paper proposes a spectrum selection scheme and a transmit power minimization scheme for a device-to-device (D2D) network cross-laid with a cloud radio access network (CRAN). T…

eess.SP2019

A Novel Content Caching and Delivery Scheme for Millimeter Wave Device-to-Device Communications

Theshani Nuradha, Tharaka Samarasinghe, Kasun T. Hemachandra

A novel content caching strategy is proposed for a cache enabled device-to-device (D2D) network where the user devices are allowed to communicate using millimeter wave (mmWave) D2D…

eess.SP2019★ 17 cited

Physical-Layer Security for Untrusted UAV-Assisted Full-Duplex Wireless Networks

Theshani Nuradha, Kasun T. Hemachandra, Tharaka Samarasinghe +1

The paper considers physical layer security (PLS) of an untrusted unmanned aerial vehicle (UAV) network, where a multitude of UAVs communicate in full-duplex (FD) mode. A source-ba…