Dynamical Properties of Euclidean Solutions in a Multidimensional Cosmological Model

In the framework of the Hartle-Hawking no-boundary proposal, we investigated quantum creation of the multidimensional universe with a cosmological constant ($\Lambda$) but without matter fields. We have found that the classical solutions of the Euclidean Einstein equations in this model have ``quasi-attractors'', i.e., most trajectories on the a-b plane, where a and b are the scale factors of external and internal spaces, go around a point. It is presumed that the wave function of the universe has a hump near this quasi-attractor point. In the case that both the curvatures of external and internal spaces are positive, and $\Lambda>0$, there exist Lorentzian solutions which start near the quasi-attractor, the internal space remains microscopic, and the external space evolves into our macroscopic universe.Comment: 13 pages and 5 figure

Water Pricing Strategy for the City of Albuquerque\u27s Sustainable Water Use

The requirement for sustainable water use is that the city uses only renewable water resources. The city has renewable, surface water rights of 69,000 acre-feet per year. These water rights are titled for consumptive use. Currently, the city returns one half of the supplied water to the river through the wastewater treatment plant. Therefore, the city can divert and use twice the titled water rights for supply, 138,000 acre-feet per year. The city must still rely on ground water for some portion of its water supply. Without a ground water component, the city would need large and more costly treatment facilities to meet seasonal peak demands. However, the amount to be pumped should be limited to the renewable portion of ground water. Renewable ground water is 50,000 acre-feet per year that is replenished by streamflow. To use 88,000 acre-feet of surface water, the city needs water treatment facilities. However, this measure alone can meet the city\u27s increasing demand for only a few decades depending on the growth scenario. The city requires additional measures for long-term supply. The most viable measure is water conservation

A Practical and Scalable Decoder for Topological Quantum Error Correction with Digital Annealer

Quantum error correction is one of the most important milestones for realization of large-scale quantum computation. To achieve this, it is essential not only to integrate a large number of qubits with high fidelity, but also to build a scalable classical system that can perform error correction. Here, we propose an efficient and scalable decoder for quantum error correction using Fujitsu Digital Annealer (DA). Specifically, the error correction problem of stabilizer codes is mapped into an Ising-type optimization problem, so-called quadratic unconstrained binary optimization (QUBO) problem, which is solved by DA. In particular, we implement the proposed DA decoder for the surface code and perform detailed numerical experiments for various code distances to see its performance and scalability. We observe that computational scaling for the DA decoder has a lower order of polynomial than the decoding methods using simulated annealing (SA) and minimum-weight perfect matching (MWPM) algorithm under all tested conditions. Furthermore, the threshold behavior of the logical error probability for the DA decoder is analyzed and the resultant threshold value is about 9.7% which is very close to that obtained by the MWPM decoder. This result clearly shows the high potential of the DA decoder for quantum error correction.Comment: 10 pages, 11 figure

Reaction of layered carbon fluorides CₓF (x=2.5–3.6) and hydrogen

The layered carbon fluorides CₓF (x = 2.5, 2.8, 3.6), generally classified as fluorine–graphite intercalation compounds, were heat-treated in hydrogen gas. These fluorides are more reactive with hydrogen compared to (CF)ₙ and (C₂F)ₙ. Reduction of CₓF to graphite-like carbon starts at about 573 K, and proceeds gradually along with the elevation of temperature. Fluorine atoms in CₓF are eliminated as HF in the reduction process without being substituted by hydrogen atoms. Systematic difference was not found in the average crystallite sizes of the carbon material prepared from CₓF by the reduction with hydrogen and that by the pyrolysis in vacuum. On the other hand, interlayer distance and fluorine content of the former are smaller than those of the latter. In the case that the CₓF precursor maintains a large particle size, the reduced carbon as well as the pyrolytically prepared carbon possesses a foam-like shape due to the exfoliation during the heat treatment

Braking and Body Angles Control of an Insect-Computer Hybrid Robot by Electrical Stimulation of Beetle Flight Muscle in Free Flight

While engineers put lots of effort, resources, and time in building insect scale micro aerial vehicles (MAVs) that fly like insects, insects themselves are the real masters of flight. What if we would use living insect as platform for MAV instead? Here, we reported a flight control via electrical stimulation of a flight muscle of an insect-computer hybrid robot, which is the interface of a mountable wireless backpack controller and a living beetle. The beetle uses indirect flight muscles to drive wing flapping and three major direct flight muscles (basalar, subalar and third axilliary (3Ax) muscles) to control the kinematics of the wings for flight maneuver. While turning control was already achieved by stimulating basalar and 3Ax muscles, electrical stimulation of subalar muscles resulted in braking and elevation control in flight. We also demonstrated around 20 degrees of contralateral yaw and roll by stimulating individual subalar muscle. Stimulating both subalar muscles lead to an increase of 20 degrees in pitch and decelerate the flight by 1.5 m/s2 as well as an induce an elevation of 2 m/s2.Comment: 9 pages, 7 figures, supplemental video: https://youtu.be/P9dxsSf14LY . Cyborg and Bionic Systems 202

Activity of Hokkaido University Neutron Source, HUNS

AbstractHokkaido University neutron source, HUNS was completed in 1973, and has been used actively for developments of moderators, neutron instruments, neutron devices and new methods for 40 years although its power is not so high. Recently, a pulsed neutron imaging method has been developed and a new type of small angle neutron scattering method has been also developed. The pulsed neutron imaging is a unique method that can give the physical quantities such as crystallographic quantities of materials over wide area of the real space. So far, the small angle neutron scattering (SANS) is considered to be impossible at a neutron source with a power of HUNS. However, mini focusing SANS (mfSANS) was developed and proved to be useful. Here, we present the present activities on the pulsed neutron imaging and mfSANS at HUNS

Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data

A quantum convolutional neural network (QCNN) is a promising quantum machine learning (QML) model to achieve quantum advantages in classically intractable problems. However, QCNN requires a large number of measurements for data learning, limiting its practical applications for large-scale problems. To relieve this requirement, we propose a novel architecture called split-parallelizing QCNN (sp-QCNN), which exploits the prior knowledge of quantum data for designing efficient circuits. This architecture draws inspiration from geometric quantum machine learning and targets translationally symmetric quantum data commonly encountered in condensed matter physics. By splitting the quantum circuit based on translational symmetry, sp-QCNN substantially parallelizes conventional QCNN without increasing the number of qubits and further improves the measurement efficiency by an order of the number of qubits. To demonstrate its effectiveness, we apply sp-QCNN to a quantum phase recognition task and show that it can achieve similar performance to conventional QCNN while considerably reducing the measurement resources required. Due to its high measurement efficiency, sp-QCNN can mitigate statistical errors in estimating the gradient of the loss function, thereby accelerating the learning process. These results open up new possibilities for incorporating the prior knowledge of data into the efficient design of QML models, leading to practical quantum advantages.Comment: 15 pages, 10 figure