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QC-Forest: a Classical-Quantum Algorithm to Provably Speedup Retraining of Random Forest
In this setting, performing periodic model retraining with the old and new data accumulated is beneficial as it fully captures possible drifts in the data distribution over time.
Prospects of Privacy Advantage in Quantum Machine Learning
We establish conditions on the encoding map such as classical simulatability, overlap with DLA basis, and its Fourier frequency characteristics that enable such a privacy breach of VQC models.
Privacy-preserving quantum federated learning via gradient hiding
Distributed quantum computing, particularly distributed quantum machine learning, has gained substantial prominence for its capacity to harness the collective power of distributed quantum resources, transcending the limitations of individual quantum nodes.
Blind quantum machine learning with quantum bipartite correlator
Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes.
Expressive variational quantum circuits provide inherent privacy in federated learning
However, standard neural network-based federated learning models have been shown to be susceptible to data leakage from the gradients shared with the server.
Des-q: a quantum algorithm to provably speedup retraining of decision trees
Decision trees are widely adopted machine learning models due to their simplicity and explainability.
Integral Transforms in a Physics-Informed (Quantum) Neural Network setting: Applications & Use-Cases
Recently, the machine learning paradigm of Physics-Informed Neural Networks emerged with increasing popularity as a method to solve differential equations by leveraging automatic differentiation.
Graph neural network initialisation of quantum approximate optimisation
In this work, we focus on the quantum approximate optimisation algorithm (QAOA) for solving the MaxCut problem.
Efficient Construction of Quantum Physical Unclonable Functions with Unitary t-designs
Quantum physical unclonable functions, or QPUFs, are rapidly emerging as theoretical hardware solutions to provide secure cryptographic functionalities such as key-exchange, message authentication, entity identification among others.
Quantum Physics
Variational Quantum Cloning: Improving Practicality for Quantum Cryptanalysis
In this work, we propose variational quantum cloning (VQC), a quantum machine learning based cryptanalysis algorithm which allows an adversary to obtain optimal (approximate) cloning strategies with short depth quantum circuits, trained using hybrid classical-quantum techniques.
Quantum versus Classical Generative Modelling in Finance
Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impacted by quantum technologies.
A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts
To solve these problems, we have introduced a unified and robust multi-modal deep learning architecture which works for English code-mixed dataset and uni-lingual English dataset both. The devised system, uses psycho-linguistic features and very ba-sic linguistic features.
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OntoCat: Automatically categorizing knowledge in API Documentation
Most application development happens in the context of complex APIs; reference documentation for APIs has grown tremendously in variety, complexity, and volume, and can be difficult to navigate.
Exploring the Role of Logically Related Non-Question Phrases for Answering Why-Questions
Next, we apply an improved version of ranking with a prior-based approach, which ranks all words in the candidate document with respect to a set of root words (i. e. non-stopwords present in the question and in the candidate document).
