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Found 24 papers, 7 papers with code
Universal scaling laws in quantum-probabilistic machine learning by tensor network towards interpreting representation and generalization powers
We reveal that while gaining information through training, the linear scaling law is suppressed by a negative quadratic correction, leading to $L \simeq \beta M - \alpha M^2 + const$.
Universal replication of chaotic characteristics by classical and quantum machine learning
Replicating chaotic characteristics of non-linear dynamics by machine learning (ML) has recently drawn wide attentions.
Tensor networks for interpretable and efficient quantum-inspired machine learning
It is a critical challenge to simultaneously gain high interpretability and efficiency with the current schemes of deep machine learning (ML).
Persistent Ballistic Entanglement Spreading with Optimal Control in Quantum Spin Chains
Entanglement propagation provides a key routine to understand quantum many-body dynamics in and out of equilibrium.
Compressing neural network by tensor network with exponentially fewer variational parameters
In this work, we propose a general compression scheme that significantly reduces the variational parameters of NN by encoding them to deep automatically-differentiable tensor network (ADTN) that contains exponentially-fewer free parameters.
Intelligent diagnostic scheme for lung cancer screening with Raman spectra data by tensor network machine learning
The accuracy of the samples with high certainty is almost 100$\%$.
Deep Machine Learning Reconstructing Lattice Topology with Strong Thermal Fluctuations
In this work, we demonstrate the validity of the deep convolutional neural network (CNN) on reconstructing the lattice topology (i. e., spin connectivities) in the presence of strong thermal fluctuations and unbalanced data.
Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations
Here we investigate such a ``white shoe'' recognition problem from the perspective of tensor network (TN) machine learning and quantum entanglement.
Quantum compiling with a variational instruction set for accurate and fast quantum computing
The quantum instruction set (QIS) is defined as the quantum gates that are physically realizable by controlling the qubits in quantum hardware.
Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity
In quantum and quantum-inspired machine learning, the very first step is to embed the data in quantum space known as Hilbert space.
Predicting Quantum Potentials by Deep Neural Network and Metropolis Sampling
The hybridizations of machine learning and quantum physics have caused essential impacts to the methodology in both fields.
Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions
State preparation is of fundamental importance in quantum physics, which can be realized by constructing the quantum circuit as a unitary that transforms the initial state to the target, or implementing a quantum control protocol to evolve to the target state with a designed Hamiltonian.
Automatically Differentiable Quantum Circuit for Many-qubit State Preparation
Taking the ground states of quantum lattice models and random matrix product states as examples, with the number of qubits where processing the full coefficients is unlikely, ADQC obtains high fidelities with small numbers of layers $N_L \sim O(1)$.
Residual Matrix Product State for Machine Learning
Tensor network, which originates from quantum physics, is emerging as an efficient tool for classical and quantum machine learning.
Deep learning Local Reduced Density Matrices for Many-body Hamiltonian Estimation
In this work, we demonstrate that convolutional neural network (CNN) can learn from the coefficients of local reduced density matrices to estimate the physical parameters of the many-body Hamiltonians, such as coupling strengths and magnetic fields, provided the states as the ground states.
Tangent-Space Gradient Optimization of Tensor Network for Machine Learning
The gradient-based optimization method for deep machine learning models suffers from gradient vanishing and exploding problems, particularly when the computational graph becomes deep.
Bayesian Tensor Network with Polynomial Complexity for Probabilistic Machine Learning
To testify its validity for exponentially many events, BTN is implemented to the image recognition, where the classification is mapped to capturing the conditional probabilities in an exponentially large sample space.
Quantum Compressed Sensing with Unsupervised Tensor-Network Machine Learning
To transfer a specific piece of information with $|\Psi \rangle$, our proposal is to encode such information in the separable state with the minimal distance to the measured state $|\Phi \rangle$ that is obtained by partially measuring on $|\Psi \rangle$ in a designed way.
Generative Tensor Network Classification Model for Supervised Machine Learning
By investigating the distances in the many-body Hilbert space, we find that (a) the samples are naturally clustering in such a space; and (b) bounding the bond dimensions of the TN's to finite values corresponds to removing redundant information in the image recognition.
Quantum simulation for thermodynamics of infinite-size many-body systems by O(10) sites
In this work we propose to simulate many-body thermodynamics of infinite-size quantum lattice models in one, two, and three dimensions, in terms of few-body models of only O(10) sites, which we coin as quantum entanglement simulators (QES's).
Strongly Correlated Electrons Computational Physics Quantum Physics
Entanglement-guided architectures of machine learning by quantum tensor network
In this work, we implement simple numerical experiments, related to pattern/images classification, in which we represent the classifiers by many-qubit quantum states written in the matrix product states (MPS).
Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures
The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention.
Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
We study the quantum features of the TN states, including quantum entanglement and fidelity.
Review of Tensor Network Contraction Approaches
One goal is to provide a systematic introduction of TN contraction algorithms (motivations, implementations, relations, implications, etc.
Computational Physics Statistical Mechanics Strongly Correlated Electrons Applied Physics Quantum Physics
