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Found 26 papers, 3 papers with code
Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers
This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum computers to reshape the landscape of machine learning.
Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits
The vast and complicated large-qubit state space forbids us to comprehensively capture the dynamics of modern quantum computers via classical simulations or quantum tomography.
The curse of random quantum data
Provided that the encoding of quantum data is sufficiently random, the performance, we find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in the number of qubits, which we call "the curse of random quantum data".
Multimodal deep representation learning for quantum cross-platform verification
Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms, utilizing minimal measurements.
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits
Variational quantum circuits (VQCs) hold promise for quantum machine learning on noisy intermediate-scale quantum (NISQ) devices.
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
Using these findings, we propose a method that uses loss dynamics to probe whether a QC may be more effective than a classical classifier on a particular learning task.
Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer
Factorization Machine (FM) is the most commonly used model to build a recommendation system since it can incorporate side information to improve performance.
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression
In this work, we first put forth an end-to-end quantum neural network, TTN-VQC, which consists of a quantum tensor network based on a tensor-train network (TTN) for dimensionality reduction and a VQC for functional regression.
Recent Advances for Quantum Neural Networks in Generative Learning
Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts.
Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits
Experimental results verify our theoretical findings in the quantum simulation and quantum chemistry.
Quantum Optimization for Training Quantum Neural Networks
Training quantum neural networks (QNNs) using gradient-based or gradient-free classical optimisation approaches is severely impacted by the presence of barren plateaus in the cost landscapes.
Entanglement-assisted capacity regions and protocol designs for quantum multiple-access channels
We solve the entanglement-assisted (EA) classical capacity region of quantum multiple-access channels with an arbitrary number of senders.
Quantum Physics Information Theory Information Theory
Quantum circuit architecture search for variational quantum algorithms
Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices.
Experimental Quantum Generative Adversarial Networks for Image Generation
For the first time, we experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
Quantum Differentially Private Sparse Regression Learning
The eligibility of various advanced quantum algorithms will be questioned if they can not guarantee privacy.
Quantum noise protects quantum classifiers against adversaries
This robustness property is intimately connected with an important security concept called differential privacy which can be extended to quantum differential privacy.
On Dimension-free Tail Inequalities for Sums of Random Matrices and Applications
We also develop the tail inequalities for matrix random series and matrix martingale difference sequence.
Quantum algorithm for finding the negative curvature direction
Moreover, we propose an efficient algorithm to achieve the classical read-out of the target state.
Quantum algorithm for finding the negative curvature direction in non-convex optimization
Moreover, we propose an efficient quantum algorithm to achieve the classical read-out of the target state.
A Quantum-inspired Algorithm for General Minimum Conical Hull Problems
In this paper, we propose a sublinear classical algorithm to tackle general minimum conical hull problems when the input has stored in a sample-based low-overhead data structure.
Efficient Online Quantum Generative Adversarial Learning Algorithms with Applications
The exploration of quantum algorithms that possess quantum advantages is a central topic in quantum computation and quantum information processing.
Quantum Speedup in Adaptive Boosting of Binary Classification
Here we propose a quantum extension of AdaBoost, demonstrating a quantum algorithm that can output the optimal strong classifier with a quadratic speedup in the number of queries of the weak classifiers.
Generalization Bounds for Vicinal Risk Minimization Principle
First, we prove that the complexity of function classes convolving with vicinal functions can be controlled by that of the original function classes under the assumption that the function class is composed of Lipschitz-continuous functions.
The Expressive Power of Parameterized Quantum Circuits
Parameterized quantum circuits (PQCs) have been broadly used as a hybrid quantum-classical machine learning scheme to accomplish generative tasks.
A Grover-search Based Quantum Learning Scheme for Classification
Here we devise a Grover-search based quantum learning scheme (GBLS) to address the above two issues.
The Learnability of Unknown Quantum Measurements
Quantum machine learning has received significant attention in recent years, and promising progress has been made in the development of quantum algorithms to speed up traditional machine learning tasks.
