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Quantum Information Processing

Self-guided quantum state learning for mixed states

  • 01-07-2022
Published in:
Authors
Ahmad Farooq
Muhammad Asad Ullah
Junaid ur Rehman
Kyesan Lee
Hyundong Shin
Published in
Quantum Information Processing | Issue 7/2022

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Abstract

The article introduces a new quantum state tomography algorithm called 'self-guided quantum state learning for mixed states.' This method leverages simultaneous perturbation stochastic approximation (SPSA) to efficiently estimate the eigenvectors and eigenvalues of unknown mixed quantum states. The algorithm sequentially estimates eigenvectors by invoking SPSA on the null space of already obtained eigenvectors, and then uses the Gram–Schmidt process to form an orthonormal basis. Numerical simulations and experimental results on IBM quantum devices demonstrate the algorithm's superior performance and robustness to noise, making it particularly suitable for near-term quantum applications. The article also highlights the algorithm's computational efficiency compared to other tomography methods, offering a significant advancement in the field.
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previous article Comfortable place for quantum walker on finite path
next article Statistics of projective measurement on a quantum probe as a witness of noncommutativity of algebra of a probed system
Title
Self-guided quantum state learning for mixed states
Authors
Ahmad Farooq
Muhammad Asad Ullah
Junaid ur Rehman
Kyesan Lee
Hyundong Shin
Publication date
01-07-2022
Publisher
Springer US
Published in
Quantum Information Processing / Issue 7/2022
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-022-03585-8
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