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| 001 | 910893 | ||
| 005 | 20250416202205.0 | ||
| 024 | 7 | _ | |a 10.1103/PhysRevLett.128.150503 |2 doi |
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| 024 | 7 | _ | |a 1079-7114 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2022-04243 |
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| 100 | 1 | _ | |a Dalgaard, Mogens |0 P:(DE-HGF)0 |b 0 |
| 245 | _ | _ | |a Dynamical Uncertainty Propagation with Noisy Quantum Parameters |
| 260 | _ | _ | |a College Park, Md. |c 2022 |b APS |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Many quantum technologies rely on high-precision dynamics, which raises the question of how these are influenced by the experimental uncertainties that are always present in real-life settings. A standard approach in the literature to assess this is Monte Carlo sampling, which suffers from two major drawbacks. First, it is computationally expensive. Second, it does not reveal the effect that each individual uncertainty parameter has on the state of the system. In this Letter, we evade both these drawbacks by incorporating propagation of uncertainty directly into simulations of quantum dynamics, thereby obtaining a method that is orders of magnitude faster than Monte Carlo simulations and directly provides information on how each uncertainty parameter influences the system dynamics. Additionally, we compare our method to experimental results obtained using the IBM quantum computers. |
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| 536 | _ | _ | |a EXC 2004: Matter and Light for Quantum Computing (ML4Q) (390534769) |0 G:(BMBF)390534769 |c 390534769 |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Weidner, Carrie A. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Motzoi, Felix |0 P:(DE-Juel1)179158 |b 2 |e Corresponding author |
| 773 | _ | _ | |a 10.1103/PhysRevLett.128.150503 |g Vol. 128, no. 15, p. 150503 |0 PERI:(DE-600)1472655-5 |n 15 |p 150503 |t Physical review letters |v 128 |y 2022 |x 0031-9007 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/910893/files/Invoice_INV_22_APR_008202.pdf |
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