Regensburg 2022 – wissenschaftliches Programm
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QI: Fachverband Quanteninformation
QI 3: Certification and Benchmarking of Quantum Systems
QI 3.4: Vortrag
Montag, 5. September 2022, 16:00–16:15, H8
Machine learning approaches to Optimal Gate Sequences for Quantum State Tomography under Noise — •Violeta N. Ivanova-Rohling1,2,3, Niklas Rohling1, and Guido Burkard1 — 1Department of Physics, University of Konstanz, D-78457 Konstanz — 2Zukunftskolleg, University of Konstanz, D-78457 Konstanz — 3Department of Mathematical Foundations of Computer Sciences, IMI, Bulgarian Academy of Sciences
For limited scenarios, depending on projector rank and system size, optimal measurement schemes for efficient quantum state tomography (QST) are known. In the case of errorless non-degenerate measurements, using mutually unbiased bases yields the optimal QST scheme [1]. However, in the general case, the optimal measurement scheme for efficient QST is not known and, may need to be numerically approximated. Here, we investigate the effect of noise on the optimal QST measurement sets using two noise models: the depolarizing channel, and over- and under-rotation in two-qubit gates [2]. Furthermore, we apply reinforcement learning for optimizing the effective times each quantum gate is switched on in a set of gate sequences which – combined with an elementary projective measurement – realizes a QST quorum. We extend the model by including errors from single-qubit gates and allow for longer gate sequences than necessary for realizing arbitrary measurements aiming at higher noise resilience overall.
[1] Wootters, Fields, Ann. Phys. 191, 363 (1989)
[2] Ivanova-Rohling, Rohling, Burkard, arXiv:2203.05677