Tensor Networks
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Reinforcement Learning with Tensor Networks: Application to Dynamical Large Deviations
We present a framework to integrate tensor network (TN) methods with reinforcement learning (RL) for solving dynamical optimisation tasks.
Verifying Fairness in Quantum Machine Learning
In this work, we define a formal framework for the fairness verification and analysis of quantum machine learning decision models, where we adopt one of the most popular notions of fairness in the literature based on the intuition -- any two similar individuals must be treated similarly and are thus unbiased.
PROTOtypical Logic Tensor Networks (PROTO-LTN) for Zero Shot Learning
We focus here on the subsumption or \texttt{isOfClass} predicate, which is fundamental to encode most semantic image interpretation tasks.
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks
The problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) which, due to its NP-hard complexity, is solved using Quantum Annealing on a quantum computer provided by D-Wave.
Stack operation of tensor networks
The tensor network, as a facterization of tensors, aims at performing the operations that are common for normal tensors, such as addition, contraction and stacking.
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance.
Privacy-preserving machine learning with tensor networks
Tensor networks, widely used for providing efficient representations of low-energy states of local quantum many-body systems, have been recently proposed as machine learning architectures which could present advantages with respect to traditional ones.
Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data
Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently.
Representing Prior Knowledge Using Randomly, Weighted Feature Networks for Visual Relationship Detection
Furthermore, background knowledge represented by RWFNs can be used to alleviate the incompleteness of training sets even though the space complexity of RWFNs is much smaller than LTNs (1:27 ratio).
lambeq: An Efficient High-Level Python Library for Quantum NLP
We present lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP).