| 1. |
- Kudyshev, Z. A., et al.
(författare)
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Rapid Classification of Quantum Sources Enabled by Machine Learning
- 2020
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Ingår i: Advanced Quantum Technologies. - : Wiley-VCH Verlag. - 2511-9044. ; 3:10
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Tidskriftsartikel (refereegranskat)abstract
- Deterministic nanoassembly may enable unique integrated on-chip quantum photonic devices. Such integration requires a careful large-scale selection of nanoscale building blocks such as solid-state single-photon emitters by means of optical characterization. Second-order autocorrelation is a cornerstone measurement that is particularly time-consuming to realize on a large scale. Supervised machine learning-based classification of quantum emitters as “single” or “not-single” is implemented based on their sparse autocorrelation data. The method yields a classification accuracy of 95% within an integration time of less than a second, realizing roughly a 100-fold speedup compared to the conventional Levenberg–Marquardt fitting approach. It is anticipated that machine learning-based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices.
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| 2. |
- Dobrautz, Werner, 1987, et al.
(författare)
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Combined unitary and symmetric group approach applied to low-dimensional Heisenberg spin systems
- 2022
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Ingår i: Physical Review B. - 2469-9969 .- 2469-9950. ; 105:19
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Tidskriftsartikel (refereegranskat)abstract
- A novel combined unitary and symmetric group approach is used to study the spin-1/2 Heisenberg model and related Fermionic systems in a total spin-adapted representation, using a linearly-parameterised Ansatz for the many-body wave function. We show that a more compact ground-state wave function representation-indicated by a larger leading ground-state coefficient-is obtained when combining the symmetric group S-n, in the form of permutations of the underlying lattice site ordering, with the cumulative spin coupling based on the unitary group U(n). In one-dimensional systems the observed compression of the wave function is reminiscent of block-spin renormalization group approaches, and allows us to study larger lattices (here taken up to 80 sites) with the spin-adapted full configuration interaction quantum Monte Carlo method, which benefits from the sparsity of the Hamiltonian matrix and the corresponding sampled eigenstates that emerge from the reordering. We find that in an optimal lattice ordering the configuration state function with highest weight already captures with high accuracy the spin-spin correlation function of the exact ground-state wave function. This feature is found for more general lattice models, such as the Hubbard model, and ab initio quantum chemical models, exemplified by one-dimensional hydrogen chains. We also provide numerical evidence that the optimal lattice ordering for the unitary group approach is not generally equivalent to the optimal ordering obtained for methods based on matrix-product states, such as the density-matrix renormalization group approach.
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| 3. |
- Zabelina, Svetlana A., et al.
(författare)
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Carbon emission from thermokarst lakes in NE European tundra
- 2021
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Ingår i: Limnology and Oceanography. - : John Wiley & Sons. - 0024-3590 .- 1939-5590. ; 66:S1, s. S216-S230
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Tidskriftsartikel (refereegranskat)abstract
- Emission of greenhouse gases (GHGs) from inland waters is recognized as highly important and an understudied part of the terrestrial carbon (C) biogeochemical cycle. These emissions are still poorly quantified in subarctic regions that contain vast amounts of surface C in permafrost peatlands. This is especially true in NE European peatlands, located within sporadic to discontinuous permafrost zones which are highly vulnerable to thaw. Initial measurements of C emissions from lentic waters of the Bolshezemelskaya Tundra (BZT; 200,000 km2) demonstrated sizable CO2 and CH4 concentrations and fluxes to the atmosphere in 98 depressions, thaw ponds, and thermokarst lakes ranging from 0.5 × 106 to 5 × 106 m2 in size. CO2 fluxes decreased by an order of magnitude as waterbody size increased by > 3 orders of magnitude while CH4 fluxes showed large variability unrelated to lake size. By using a combination of Landsat‐8 and GeoEye‐1 images, we determined lakes cover 4% of BZT and thus calculated overall C emissions from lentic waters to be 3.8 ± 0.65 Tg C yr−1 (99% C‐CO2, 1% C‐CH4), which is two times higher than the lateral riverine export. Large lakes dominated GHG emissions whereas small thaw ponds had a minor contribution to overall water surface area and GHG emissions. These data suggest that, if permafrost thaw in NE Europe results in disappearance of large thermokarst lakes and formation of new small thaw ponds and depressions, GHG emissions from lentic waters in this region may decrease.
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