Quantum State Tomography
11 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Online Learning Quantum States with the Logarithmic Loss via VB-FTRL
Online learning quantum states with the logarithmic loss (LL-OLQS) is a quantum generalization of online portfolio selection, a classic open problem in the field of online learning for over three decades.
Learning quantum states and unitaries of bounded gate complexity
While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers.
Learning Informative Latent Representation for Quantum State Tomography
Our method leverages a transformer-based encoder to extract an informative latent representation (ILR) from imperfect measurement data and employs a decoder to predict the quantum states based on the ILR.
ShadowNet for Data-Centric Quantum System Learning
Understanding the dynamics of large quantum systems is hindered by the curse of dimensionality.
Quantum State Tomography using Quantum Machine Learning
Quantum State Tomography (QST) is a fundamental technique in Quantum Information Processing (QIP) for reconstructing unknown quantum states.
Tomography of Quantum States from Structured Measurements via quantum-aware transformer
Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices.
Unrolling SVT to obtain computationally efficient SVT for n-qubit quantum state tomography
Existing works focus on estimating the density matrix that represents the state, using a compressive sensing approach, with only fewer measurements than that required for a tomographically complete set, with the assumption that the true state has a low rank.
Demonstration of machine-learning-enhanced Bayesian quantum state estimation
Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations.
Quantum Split Neural Network Learning using Cross-Channel Pooling
In recent years, the field of quantum science has attracted significant interest across various disciplines, including quantum machine learning, quantum communication, and quantum computing.
Granger Causality for Compressively Sensed Sparse Signals
In this work, we provide a mathematical proof that structured compressed sensing matrices, specifically Circulant and Toeplitz, preserve causal relationships in the compressed signal domain, as measured by Granger Causality.
