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Found 23 papers, 1 papers with code
Tensor network compressibility of convolutional models
We explore this by assessing how truncating the convolution kernels of dense (untensorized) CNNs impact their accuracy.
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, substantial energy demands, and limitations for on-site deployment.
Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks
Defect detection is one of the most important yet challenging tasks in the quality control stage in the manufacturing sector.
Tensor Networks for Explainable Machine Learning in Cybersecurity
In this paper we show how tensor networks help in developing explainability of machine learning algorithms.
Hacking Cryptographic Protocols with Advanced Variational Quantum Attacks
Our work also shows improvements in attack success rates for lightweight ciphers such as S-DES and S-AES.
Efficient tensor network simulation of IBM's largest quantum processors
Apart from simulating the original experiment for 127 qubits, we also extend our results to 433 and 1121 qubits, and for evolution times around 8 times longer, thus setting a benchmark for the newest IBM quantum machines.
Application of Tensor Neural Networks to Pricing Bermudan Swaptions
The Cheyette model is a quasi-Gaussian volatility interest rate model widely used to price interest rate derivatives such as European and Bermudan Swaptions for which Monte Carlo simulation has become the industry standard.
Improving Gradient Methods via Coordinate Transformations: Applications to Quantum Machine Learning
In this paper we introduce a generic strategy to accelerate and improve the overall performance of such methods, allowing to alleviate the effect of barren plateaus and local minima.
Quantum-Inspired Tensor Neural Networks for Option Pricing
Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions.
Financial Risk Management on a Neutral Atom Quantum Processor
In this work we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field.
Quantum artificial vision for defect detection in manufacturing
In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts.
Quantum-Inspired Tensor Neural Networks for Partial Differential Equations
We demonstrate that TNN provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN).
Variational Quantum and Quantum-Inspired Clustering
Here we present a quantum algorithm for clustering data based on a variational quantum circuit.
Quantum Portfolio Optimization with Investment Bands and Target Volatility
In this paper we show how to implement in a simple way some complex real-life constraints on the portfolio optimization problem, so that it becomes amenable to quantum optimization algorithms.
Use Cases of Quantum Optimization for Finance
In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy.
Forecasting Election Polls with Spin Systems
We show that the problem of political forecasting, i. e, predicting the result of elections and referendums, can be mapped to finding the ground state configuration of a classical spin system.
Combinatorial Optimization
Sentiment Analysis
Physics and Society
Statistical Mechanics
Quantum Physics
Benchmarking global $SU(2)$ symmetry in 2d tensor network algorithms
We implement and benchmark tensor network algorithms with $SU(2)$ symmetry for systems in two spatial dimensions and in the thermodynamic limit.
Strongly Correlated Electrons
A programming guide for tensor networks with global $SU(2)$ symmetry
This paper is a manual with tips and tricks for programming tensor network algorithms with global $SU(2)$ symmetry.
Strongly Correlated Electrons Quantum Physics
Mathematical foundations of matrix syntax
Matrix syntax is a formal model of syntactic relations in language.
Language Design as Information Renormalization
Moreover, we show how to obtain such language models from quantum states that can be efficiently prepared on a quantum computer, and use this to find bounds on the perplexity of the probability distribution of words in a sentence.
The iPEPS algorithm, improved: fast full update and gauge fixing
The infinite Projected Entangled Pair States (iPEPS) algorithm [J. Jordan et al, PRL 101, 250602 (2008)] has become a useful tool in the calculation of ground state properties of 2d quantum lattice systems in the thermodynamic limit.
Strongly Correlated Electrons High Energy Physics - Lattice Quantum Physics
Advances on Tensor Network Theory: Symmetries, Fermions, Entanglement, and Holography
This is a short review on selected theory developments on Tensor Network (TN) states for strongly correlated systems.
Strongly Correlated Electrons High Energy Physics - Lattice High Energy Physics - Theory Quantum Physics
A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States
This is a partly non-technical introduction to selected topics on tensor network methods, based on several lectures and introductory seminars given on the subject.
Strongly Correlated Electrons High Energy Physics - Lattice High Energy Physics - Theory Quantum Physics
