Maximal entropy approach for quantum state tomography
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TLDR
In this paper, an alternative approach to quantum tomography, based on the maximal information entropy, that can predict the values of unknown observables based on available mean measurement data is proposed.Abstract:
Quantum computation has been growing rapidly in both theory and experiments. In particular, quantum computing devices with a large number of qubits have been developed by IBM, Google, IonQ, and others. The current quantum computing devices are noisy intermediate-scale quantum $($NISQ$)$ devices, and so approaches to validate quantum processing on these quantum devices are needed. One of the most common ways of validation for an n-qubit quantum system is quantum tomography, which tries to reconstruct a quantum system's density matrix by a complete set of observables. However, the inherent noise in the quantum systems and the intrinsic limitations poses a critical challenge to precisely know the actual measurement operators which make quantum tomography impractical in experiments. Here, we propose an alternative approach to quantum tomography, based on the maximal information entropy, that can predict the values of unknown observables based on the available mean measurement data. This can then be used to reconstruct the density matrix with high fidelity even though the results for some observables are missing. Of additional contexts, a practical approach to the inference of the quantum mechanical state using only partial information is also needed.read more
Citations
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Variational approach to quantum state tomography based on maximal entropy formalism.
TL;DR: In this article , the maximal entropy formalism is employed to construct the least biased mixed quantum state that is consistent with the given set of expectation values, and a hybrid quantum-classical variational algorithm is used to obtain such a target state.
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Density matrix reconstruction using non-negative matrix product states
Dongho Han,Chu Guo,Xiaoting Wang +2 more
TL;DR: This work proposes an algorithm which iteratively selects the best non-negative matrix product state approximation based on a set of measurement outcomes whose size does not necessarily grow exponentially.
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Hamiltonian Learning from Time Dynamics Using Variational Algorithms.
TL;DR: In this paper , the Hamiltonian is reconstructed in the Pauli basis using measurables on random states forming a time series data set, and the time propagation is implemented through Trotterization and optimized variationally with gradients computed on the quantum circuit.
Journal ArticleDOI
Variational approach to quantum state tomography based on maximal entropy formalism
TL;DR: In this article , the maximal entropy formalism is employed to construct the least biased mixed quantum state that is consistent with the given set of expectation values, and a hybrid quantum-classical variational algorithm is used to obtain such a target state.
Journal ArticleDOI
Dimension reduction and redundancy removal through successive Schmidt decompositions
TL;DR: In this paper , Schmidt decomposition is used to simplify quantum Hamiltonians, which can then be used to simulate quantum operations on a quantum computer, such as quantum Fourier transform and variational quantum circuits.
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