Efficient experimental characterization of quantum processes via compressed sensing on an NMR quantum processor
Akshay Gaikwad, Arvind, Kavita Dorai

TL;DR
This paper demonstrates that compressed sensing enables accurate quantum process tomography on an NMR quantum processor using significantly fewer measurements, especially when employing the Pauli-error basis, achieving high fidelity results.
Contribution
The study introduces a practical implementation of compressed sensing for quantum process tomography, showing improved performance over traditional methods in an NMR setting with reduced data requirements.
Findings
High-fidelity process matrices (>0.9) with 5-6 times less data
Compressed sensing outperforms least squares in basis-dependent scenarios
Effective characterization of two- and three-qubit quantum processes
Abstract
We employ the compressed sensing (CS) algorithm and a heavily reduced data set to experimentally perform true quantum process tomography (QPT) on an NMR quantum processor. We obtain the estimate of the process matrix corresponding to various two- and three-qubit quantum gates with a high fidelity. The CS algorithm is implemented using two different operator bases, namely, the standard Pauli basis and the Pauli-error basis. We experimentally demonstrate that the performance of the CS algorithm is significantly better in the Pauli-error basis, where the constructed matrix is maximally sparse. We compare the standard least square (LS) optimization QPT method with the CS-QPT method and observe that, provided an appropriate basis is chosen, the CS-QPT method performs significantly better as compared to the LS-QPT method. In all the cases considered, we obtained experimental…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Blind Source Separation Techniques
