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Quantum State Tomography
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent
Universal Quantum Tomography With Deep Neural Networks
Learning pure quantum states (almost) without regret
Comprehensive characterization of three-qubit Grover search algorithm on IBM's 127-qubit superconducting quantum computers
Online Learning Quantum States with the Logarithmic Loss via VB-FTRL
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging
Learning quantum states and unitaries of bounded gate complexity
Learning Informative Latent Representation for Quantum State Tomography
ShadowNet for Data-Centric Quantum System Learning
Quantum State Tomography using Quantum Machine Learning
MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification
Tomography of Quantum States from Structured Measurements via quantum-aware transformer
Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography
Demonstration of machine-learning-enhanced Bayesian quantum state estimation
Faster Stochastic First-Order Method for Maximum-Likelihood Quantum State Tomography
Quantum Split Neural Network Learning using Cross-Channel Pooling
Granger Causality for Compressively Sensed Sparse Signals
Local Stochastic Factored Gradient Descent for Distributed Quantum State Tomography
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
Improving application performance with biased distributions of quantum states
Maximum-Likelihood Quantum State Tomography by Soft-Bayes
Classification and reconstruction of optical quantum states with deep neural networks
Classical Shadows With Noise
Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks
Quantum State Tomography with Conditional Generative Adversarial Networks
Machine learning assisted quantum state estimation
Self-Concordant Analysis of Frank-Wolfe Algorithms
Variational Quantum Circuits for Quantum State Tomography
Adaptive Quantum State Tomography with Neural Networks
Neural network state estimation for full quantum state tomography
Adversarial quantum circuit learning for pure state approximation
Provably convergent acceleration in factored gradient descent with applications in matrix sensing
Nonconvex Matrix Factorization from Rank-One Measurements
Transforming Bell's Inequalities into State Classifiers with Machine Learning
Stabiliser states are efficiently PAC-learnable
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.