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Found 6 papers, 4 papers with code
Empirical Sample Complexity of Neural Network Mixed State Reconstruction
Quantum state reconstruction using Neural Quantum States has been proposed as a viable tool to reduce quantum shot complexity in practical applications, and its advantage over competing techniques has been shown in numerical experiments focusing mainly on the noiseless case.
Learning ground states of gapped quantum Hamiltonians with Kernel Methods
Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem.
Positive-definite parametrization of mixed quantum states with deep neural networks
We introduce the Gram-Hadamard Density Operator (GHDO), a new deep neural-network architecture that can encode positive semi-definite density operators of exponential rank with polynomial resources.
From Tensor Network Quantum States to Tensorial Recurrent Neural Networks
We show that any matrix product state (MPS) can be exactly represented by a recurrent neural network (RNN) with a linear memory update.
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics.
An efficient quantum algorithm for the time evolution of parameterized circuits
Our approach is efficient in the sense that it exhibits an optimal linear scaling with the total number of variational parameters.
Quantum Physics Other Condensed Matter Computational Physics
