Practical learning method for multi-scale entangled states

Abstract

We describe a method for reconstructing multi-scale entangled states from a small number of efficiently-implementable measurements and fast post-processing. The method only requires single particle measurements and the total number of measurements is polynomial in the number of particles. Data post-processing for state reconstruction uses standard tools, namely matrix diagonalisation and conjugate gradient method, and scales polynomially with the number of particles. Our method prevents the build-up of errors from both numerical and experimental imperfections.

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• Demonstration of Two-Qubit Algorithms with a Superconducting Quantum Processor
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• Matrix product states represent ground states faithfully
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• Practical characterization of quantum devices without tomography
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Cited by in corpus (29)

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• Scalable reconstruction of density matrices
• Systematic errors in current quantum state tomography tools
• Classical Shadows With Noise
• Discriminative Cooperative Networks for Detecting Phase Transitions
• Learning Relevant Features of Data with Multi-scale Tensor Networks
• Quantum Hamiltonian Learning Using Imperfect Quantum Resources
• Experimental Comparison of Efficient Tomography Schemes for a Six-Qubit State
• Genuine multiparticle entanglement of permutationally invariant states
• A Scalable Maximum Likelihood Method for Quantum State Tomography
• Recovering quantum gates from few average gate fidelities
• Practical adaptive quantum tomography
• Scalable reconstruction of unitary processes and Hamiltonians
• Deep learning and the renormalization group
• Rank-based model selection for multiple ions quantum tomography
• Multipartite entanglement detection with minimal effort
• High posterior density ellipsoids of quantum states
• Scalable Quantum Tomography with Fidelity Estimation
• A Practical Introduction to Benchmarking and Characterization of Quantum Computers
• Quantum Model Averaging
• Spectral thresholding quantum tomography for low rank states
• Coarse graining the phase space of $N$ qubits
• Tomography from collective measurements
• Practical variational tomography for critical 1D systems
• Extracting the physical sector of quantum states
• Practical implementation of mutually unbiased bases using quantum circuits
• Least-bias state estimation with incomplete unbiased measurements