22 citations · 52 across the 29 of their papers we have counts for
30 papers
Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization
Yuqi Huang, Vincent Y. F. Tan, Sharu Theresa Jose
We investigate Gaussian process (GP) bandit optimization with quantum kernels, assuming the mean reward function lies in the reproducing kernel Hilbert space (RKHS) induced by the…
Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
Abhishek Sadhu, Sharu Theresa Jose
Distributed quantum algorithms offer a promising pathway to scale variational quantum algorithms beyond the constraints of noisy intermediate-scale quantum hardware. However, exist…
Optimal Error Exponents for Composite Sequential Quantum Hypothesis Testing
Jacob Paul Simpson, Efstratios Palias, Sharu Theresa Jose
We study the composite sequential quantum hypothesis testing (SQHT) problem, where the objective is to distinguish a null quantum state from a set of alternative quantum states. We…
Cryptographic Fragility of Standard Quantum Repeater Protocols
Abhishek Sadhu, Sharu Theresa Jose
The security of the proposed quantum Internet relies on repeater protocols designed under the assumption of stochastic, characterizable noise. We demonstrate that in adversarial en…
Quantum-Enhanced Neural Contextual Bandit Algorithms
Yuqi Huang, Vincent Y. F Tan, Sharu Theresa Jose
Stochastic contextual bandits are fundamental for sequential decision-making but pose significant challenges for existing neural network-based algorithms, particularly when scaling…
Sample Complexity of Composite Quantum Hypothesis Testing
Jacob Paul Simpson, Efstratios Palias, Sharu Theresa Jose
This paper investigates symmetric composite binary quantum hypothesis testing (QHT), where the goal is to determine which of two uncertainty sets contains an unknown quantum state.…
QCA-MolGAN: Quantum Circuit Associative Molecular GAN with Multi-Agent Reinforcement Learning
Aaron Mark Thomas, Yu-Cheng Chen, Hubert Okadome Valencia +2
Navigating the vast chemical space of molecular structures to design novel drug molecules with desired target properties remains a central challenge in drug discovery. Recent advan…
On the Generalization of Adversarially Trained Quantum Classifiers
Petros Georgiou, Aaron Mark Thomas, Sharu Theresa Jose +1
Quantum classifiers are vulnerable to adversarial attacks that manipulate their input classical or quantum data. A promising countermeasure is adversarial training, where quantum c…
Neural Contextual Bandits Under Delayed Feedback Constraints
Mohammadali Moghimi, Sharu Theresa Jose, Shana Moothedath
This paper presents a new algorithm for neural contextual bandits (CBs) that addresses the challenge of delayed reward feedback, where the reward for a chosen action is revealed af…
VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors
Aaron Mark Thomas, Harry Youel, Sharu Theresa Jose
Recent proposals for quantum generative adversarial networks (GANs) suffer from the issue of mode collapse, analogous to classical GANs, wherein the distribution learnt by the GAN…
Adversarial Quantum Machine Learning: An Information-Theoretic Generalization Analysis
Petros Georgiou, Sharu Theresa Jose, Osvaldo Simeone
In a manner analogous to their classical counterparts, quantum classifiers are vulnerable to adversarial attacks that perturb their inputs. A promising countermeasure is to train t…
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis
Sharu Theresa Jose, Shana Moothedath
We explore a stochastic contextual linear bandit problem where the agent observes a noisy, corrupted version of the true context through a noise channel with an unknown noise param…
Statistical Complexity of Quantum Learning
Leonardo Banchi, Jason Luke Pereira, Sharu Theresa Jose +1
Recent years have seen significant activity on the problem of using data for the purpose of learning properties of quantum systems or of processing classical or quantum data via qu…
Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management
Yunchuan Zhang, Osvaldo Simeone, Sharu Theresa Jose +2
Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each can…
Learning with Limited Samples -- Meta-Learning and Applications to Communication Systems
Lisha Chen, Sharu Theresa Jose, Ivana Nikoloska +3
Deep learning has achieved remarkable success in many machine learning tasks such as image classification, speech recognition, and game playing. However, these breakthroughs are of…
Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis
Sharu Theresa Jose, Osvaldo Simeone
Variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy intermediate-scale quantum (NISQ) processors. Such systems leverage cl…
An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints
Sharu Theresa Jose, Osvaldo Simeone
In vertical federated learning (FL), the features of a data sample are distributed across multiple agents. As such, inter-agent collaboration can be beneficial not only during the…
Transfer Bayesian Meta-learning via Weighted Free Energy Minimization
Yunchuan Zhang, Sharu Theresa Jose, Osvaldo Simeone
Meta-learning optimizes the hyperparameters of a training procedure, such as its initialization, kernel, or learning rate, based on data sampled from a number of auxiliary tasks. A…
A unified PAC-Bayesian framework for machine unlearning via information risk minimization
Sharu Theresa Jose, Osvaldo Simeone
Machine unlearning refers to mechanisms that can remove the influence of a subset of training data upon request from a trained model without incurring the cost of re-training from…
Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning
Sharu Theresa Jose, Sangwoo Park, Osvaldo Simeone
The overall predictive uncertainty of a trained predictor can be decomposed into separate contributions due to epistemic and aleatoric uncertainty. Under a Bayesian formulation, as…
An Information-Theoretic Analysis of the Impact of Task Similarity on Meta-Learning
Sharu Theresa Jose, Osvaldo Simeone
Meta-learning aims at optimizing the hyperparameters of a model class or training algorithm from the observation of data from a number of related tasks. Following the setting of Ba…
Free Energy Minimization: A Unified Framework for Modelling, Inference, Learning,and Optimization
Sharu Theresa Jose, Osvaldo Simeone
The goal of these lecture notes is to review the problem of free energy minimization as a unified framework underlying the definition of maximum entropy modelling, generalized Baye…
Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization
Sharu Theresa Jose, Osvaldo Simeone, Giuseppe Durisi
Meta-learning automatically infers an inductive bias by observing data from a number of related tasks. The inductive bias is encoded by hyperparameters that determine aspects of th…
Conditional Mutual Information-Based Generalization Bound for Meta Learning
Arezou Rezazadeh, Sharu Theresa Jose, Giuseppe Durisi +1
Meta-learning optimizes an inductive bias---typically in the form of the hyperparameters of a base-learning algorithm---by observing data from a finite number of related tasks. Thi…