130 research outputs found
Psychological distances to climate change and public preferences for biodiversity-augmenting attributes in family-owned production forests
Understanding public perceptions on how management can help adapt forests to climate change is fundamental to the design of socially-acceptable policies. A binary discrete choice experiment in Norway and Sweden was conducted to elicit public preferences for biodiversity-augmenting changes in three forest management attributes (set-aside, proportion of uneven-aged tree stands, and number and type of tree species) compared to typical status quo conditions in family-owned production forests. Importantly, how self-constructed psychological (spatial, social, temporal and hypothetical) distances to climate change were associated with management preferences was investigated. Following integrated choice and latent variable modeling approaches to account for their latency, our econometric results show that closer psychological distances to climate change were associated with increased support for biodiversity-augmenting changes in management attributes from status quo conditions of family-owned production forests. On average, the Norwegian public preferred larger set-asides and introducing one more broadleaved species, while the Swedish public favored changes in all attributes. The highest utility was derived from increasing set-aside areas from the status quo (5%) to 10% and 20% in both countries with respective average WTP of about 10 to 11 EUR/month in Norway, and approximately 10 to 14 EUR/month in Sweden. Findings point to universal acceptability of increasing set-aside areas in both nations, and public approval for uneven-aged and mixed forest management in Sweden
Electronic Transport in Bismuth Selenide in the Topological Insulator Regime
The 3D topological insulators (TIs) have an insulating bulk but spin-momentum coupled metallic surface states stemming from band inversion due to strong spin-orbit interaction, whose existence is guaranteed by the topology of the band structure of the insulator. While the STI surface state has been studied spectroscopically by e.g. photoemission and scanned probes, transport experiments have failed to demonstrate clear signature of the STI due to high level of bulk conduction. In this thesis, I present experimental results on the transport properties of TI material Bi2Se3 in the absence of bulk conduction (TI regime), achieved by applying novel p-type doping methods. Field effect transistors consisting of thin (thickness: 5-17 nm) Bi2Se3 are fabricated by mechanical exfoliation of single crystals, and a combination of conventional dielectric (300 nm thick SiO2) and electrochemical or chemical gating methods are used to move the Fermi energy through the surface Dirac point inside bulk band gap, revealing the ambipolar gapless nature of transport in the Bi2Se3 surface states. The minimum conductivity of the topological surface state is understood within the self-consistent theory of Dirac electrons in the presence of charged impurities. The intrinsic finite-temperature resistivity of the topological surface state due to electron-acoustic phonon scattering is measured to be 60 times larger than that of graphene largely due to the smaller Fermi and sound velocities in Bi2Se3, which will have implications for topological electronic devices operating at room temperature. Along with semi-classical Boltzmann transport, I also discuss 2D weak anti-localization (WAL) behavior of the topological surface states. By investigating gate-tuned WAL behavior in thin (5-17 nm) TI films, I show that WAL in the TI regime is extraordinarily sensitive to the hybridization induced quantum mechanical tunneling between top and bottom topological surfaces, and interplay of phase coherence time and inter-surface tunneling time results in a crossover from two decoupled (top and bottom) symplectic 2D metal surfaces to a coherently coupled single channel. Furthermore, a complete suppression of WAL is observed in the 5 nm thick Bi2Se3 film which was found to occur when the hybridization gap becomes comparable to the disorder strength
Machine learning on quantum experimental data toward solving quantum many-body problems
Advancements in the implementation of quantum hardware have enabled the
acquisition of data that are intractable for emulation with classical
computers. The integration of classical machine learning (ML) algorithms with
these data holds potential for unveiling obscure patterns. Although this hybrid
approach extends the class of efficiently solvable problems compared to using
only classical computers, this approach has been realized for solving
restricted problems because of the prevalence of noise in current quantum
computers. Here, we extend the applicability of the hybrid approach to problems
of interest in many-body physics, such as predicting the properties of the
ground state of a given Hamiltonian and classifying quantum phases. By
performing experiments with various error-reducing procedures on
superconducting quantum hardware with 127 qubits, we managed to acquire refined
data from the quantum computer. This enabled us to demonstrate the successful
implementation of classical ML algorithms for systems with up to 44 qubits. Our
results verify the scalability and effectiveness of the classical ML algorithms
for processing quantum experimental data.Comment: 25 pages, 5 figures; supplementary information 35 pages, 17 figures,
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Fresnel-type Solid Immersion Lens for efficient light collection from quantum defects in diamond
Quantum defects in diamonds have been studied as a promising resource for
quantum science. The subtractive fabrication process for improving photon
collection efficiency often require excessive milling time that can adversely
affect the fabrication accuracy. We designed and fabricated a Fresnel-type
solid immersion lens using the focused ion beam. For a 5.8 um-deep
Nitrogen-vacancy (NV-) center, the milling time was highly reduced (1/3
compared to a hemispherical structure), while retaining high photon collection
efficiency (> 2.24 compared to a flat surface). In numerical simulation, this
benefit of the proposed structure is expected for a wide range of milling
depths.Comment: 16 pages, 9 figure
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