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Found 36 papers, 11 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.
MG-Net: Learn to Customize QAOA with Circuit Depth Awareness
To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth.
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.
Quantum Compiling with Reinforcement Learning on a Superconducting Processor
To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology.
Separable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theorem
The No-Free-Lunch (NFL) theorem, which quantifies problem- and data-independent generalization errors regardless of the optimization process, provides a foundational framework for comprehending diverse learning protocols' potential.
DeepLINK-T: deep learning inference for time series data using knockoffs and LSTM
DeepLINK-T combines deep learning with knockoff inference to control FDR in feature selection for time series models, accommodating a wide variety of feature distributions.
Neural auto-designer for enhanced quantum kernels
Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the quantum feature map.
Optical Quantum Sensing for Agnostic Environments via Deep Learning
DQS incorporates two essential components: a Graph Neural Network (GNN) predictor and a trigonometric interpolation algorithm.
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.
Coreset selection can accelerate quantum machine learning models with provable generalization
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning, poised to leverage the nascent capabilities of near-term quantum computers to surmount classical machine learning challenges.
ShadowNet for Data-Centric Quantum System Learning
Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality.
Transition Role of Entangled Data in Quantum Machine Learning
Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine learning (QML) models leads to substantial reductions in training data size, surpassing a specified prediction error threshold.
Holistic Label Correction for Noisy Multi-Label Classification
The issues are, that we do not understand why label dependence is helpful in the problem, and how to learn and utilize label dependence only using training data with noisy multiple labels.
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.
Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization Feature Space
We show that the cycle-consistency regularization helps to minimize the volume of the transition matrix T indirectly without exploiting the estimated noisy class posterior, which could further encourage the estimated transition matrix T to converge to its optimal solution.
Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction
To gain such computational advantages on large-scale problems, a feasible solution is the QUantum DIstributed Optimization (QUDIO) scheme, which partitions the original problem into $K$ subproblems and allocates them to $K$ quantum machines followed by the parallel optimization.
Symmetric Pruning in Quantum Neural Networks
To fill this knowledge gap, here we propose the effective quantum neural tangent kernel (EQNTK) and connect this concept with over-parameterization theory to quantify the convergence of QNNs towards the global optima.
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.
MSR: Making Self-supervised learning Robust to Aggressive Augmentations
With the training going on, the model begins to overfit noisy pairs.
Power of Quantum Generative Learning
We further employ these generalization bounds to exhibit potential advantages in quantum state preparation and Hamiltonian learning.
Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning
We systematically evaluate the performance of our proposal in compiling quantum operators with both inverse-closed and inverse-free universal basis sets.
DyRep: Bootstrapping Training with Dynamic Re-parameterization
Structural re-parameterization (Rep) methods achieve noticeable improvements on simple VGG-style networks.
Do We Need to Penalize Variance of Losses for Learning with Label Noise?
Algorithms which minimize the averaged loss have been widely designed for dealing with noisy labels.
On exploring the potential of quantum auto-encoder for learning quantum systems
Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
Accelerating variational quantum algorithms with multiple quantum processors
Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages over classical methods.
The dilemma of quantum neural networks
The core of quantum machine learning is to devise quantum models with good trainability and low generalization error bound than their classical counterparts to ensure better reliability and interpretability.
Towards understanding the power of quantum kernels in the NISQ era
In this study, we fill this knowledge gap by exploiting the power of quantum kernels when the quantum system noise and sample error are considered.
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.
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.
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.
Bayesian Quantum Circuit
Parameterized quantum circuits (PQCs), as one of the most promising schemes to realize quantum machine learning algorithms on near-term quantum computers, have been designed to solve machine earning tasks with quantum advantages.
Quantum Physics
