Quantum State Tomography
11 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Quantum State Tomography
Most implemented papers
Quantum tomography benchmarking
As a validation of the proposed methodology and software, we analyzed and compared a set of QT methods.
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging
For the Poisson inverse problem, our algorithm attains an $\varepsilon$-optimal solution in $\smash{\tilde{O}}(d^2/\varepsilon^2)$ time, matching the state of the art, where $d$ denotes the dimension.
Variational Quantum Circuits for Quantum State Tomography
We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator.
Self-Concordant Analysis of Frank-Wolfe Algorithms
Projection-free optimization via different variants of the Frank-Wolfe (FW), a. k. a.
Quantum State Tomography with Conditional Generative Adversarial Networks
We augment a CGAN with custom neural-network layers that enable conversion of output from any standard neural network into a physical density matrix.
Classification and reconstruction of optical quantum states with deep neural networks
quantshah/qst-nn
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For pure states with additive or convolutional Gaussian noise, the QST-CGAN is able to adapt to the noise and reconstruct the underlying state.
Spin qudit tomography and state reconstruction error
We consider the task of performing quantum state tomography on a $d$-level spin qudit, using only measurements of spin projection onto different quantization axes.
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
netket/netket
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We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics.
Faster Stochastic First-Order Method for Maximum-Likelihood Quantum State Tomography
In maximum-likelihood quantum state tomography, both the sample size and dimension grow exponentially with the number of qubits.
MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-classification
MORE adopts the same variational ansatz as binary classifiers while performing multi-classification by fully utilizing the quantum information of a single readout qubit.
