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To realize large-scale quantum information processes, an ideal scheme for two-qubit operations should enable diverse operations with given hardware and physical interaction. However, for spin qubits in semiconductor quantum dots, the common two-qubit operations, including CPhase gates, SWAP gates, and CROT gates, are realized with distinct parameter regions and control waveforms, posing challenges for their simultaneous implementation. Here, taking advantage of the inherent Heisenberg interaction between spin qubits, we propose and verify a fast composite two-qubit gate scheme to extend the available two-qubit gate types as well as reduce the requirements for device properties. Apart from the formerly proposed CPhase (controlled-phase) gates and SWAP gates, theoretical results indicate that the iSWAP-family gate and Fermionic simulation (fSim) gate set are additionally available for spin qubits. Meanwhile, our gate scheme limits the parameter requirements of all essential two-qubit gates to a common J~{\Delta}E_Z region, facilitate the simultaneous realization of them. Furthermore, we present the preliminary experimental demonstration of the composite gate scheme, observing excellent match between the measured and simulated results. With this versatile composite gate scheme, broad-spectrum two-qubit operations allow us to efficiently utilize the hardware and the underlying physics resources, helping accelerate and broaden the scope of the upcoming noise intermediate-scale quantum (NISQ) computing., Comment: 23 pages, 6 figures

Boson sampling is one of the main quantum computation models to demonstrate the quantum computational advantage. However, this aim may be hard to realize considering two main kinds of noises, which are photon distinguishability and photon loss. Inspired by the Bayesian validation extended to evaluate whether distinguishability is too high to demonstrate this advantage, the pattern recognition validation is extended for boson sampling, considering both distinguishability and loss. Based on clusters constructed with the K means++ method, where parameters are carefully adjusted to optimize the extended validation performances, the distribution of characteristic values is nearly monotonically changed with indistinguishability, especially when photons are close to be indistinguishable. However, this regulation may be suppressed by photon loss. The intrinsic data structure of output events is analyzed through calculating probability distributions and mean 2-norm distances of the sorted outputs. An approximation algorithm is also used to show the data structure changes with noises.

With one- and two-qubit gate fidelities approaching the fault-tolerance threshold for spin qubits in silicon, how to scale up the architecture and make large arrays of spin qubits become the more pressing challenges. In a scaled-up structure, qubit-to-qubit connectivity has crucial impact on gate counts of quantum error correction and general quantum algorithms. In our toolbox of quantum gates for spin qubits, SWAP gate is quite versatile: it can help solve the connectivity problem by realizing both short- and long-range spin state transfer, and act as a basic two-qubit gate, which can reduce quantum circuit depth when combined with other two-qubit gates. However, for spin qubits in silicon quantum dots, high fidelity SWAP gates have not been demonstrated due to the requirements of large circuit bandwidth and a highly adjustable ratio between the strength of the exchange coupling J and the Zeeman energy difference Delta E_z. Here we demonstrate a fast SWAP gate with a duration of ~25 ns based on quantum dots in isotopically enriched silicon, with a highly adjustable ratio between J and Delta E_z, for over two orders of magnitude in our device. We are also able to calibrate the single-qubit local phases during the SWAP gate by incorporating single-qubit gates in our circuit. By independently reading out the qubits, we probe the anti-correlations between the two spins, estimate the operation fidelity and analyze the dominant error sources for our SWAP gate. These results pave the way for high fidelity SWAP gates, and processes based on them, such as quantum communication on chip and quantum simulation by engineering the Heisenberg Hamiltonian in silicon., Comment: 25 pages, 5 figures

Preserving qubit coherence and maintaining high-fidelity qubit control under complex noise environment is an enduring challenge for scalable quantum computing. Here we demonstrate an addressable fault-tolerant single spin qubit with an average control fidelity of 99.12% via randomized benchmarking on a silicon quantum dot device with an integrated micromagnet. Its dephasing time T2* is 1.025 us and can be enlarged to 264 us by using the Hahn echo technique, reflecting strong low-frequency noise in our system. To break through the noise limitation, we introduce geometric quantum computing to obtain high control fidelity by exploiting its noise-resilient feature. However, the control fidelities of the geometric quantum gates are lower than 99%. According to our simulation, the noise-resilient feature of geometric quantum gates is masked by the heating effect. With further optimization to alleviate the heating effect, geometric quantum computing can be a potential approach to reproducibly achieving high-fidelity qubit control in a complex noise environment., Comment: 10 pages, 8 figures

High-fidelity singlet-triplet state readout is essential for large-scale quantum computing. However, the widely used threshold method of comparing a mean value with the fixed threshold will limit the judgment accuracy, especially for the relaxed triplet state, under the restriction of relaxation time and signal-to-noise ratio. Here, we achieve an enhanced latching readout based on Pauli spin blockade in a Si-MOS double quantum dot device and demonstrate an average singlet-triplet state readout fidelity of 97.59% by the threshold method. We reveal the inherent deficiency of the threshold method for the relaxed triplet state classification and introduce machine learning as a relaxation-independent readout method to reduce the misjudgment. The readout fidelity for classifying the simulated single-shot traces can be improved to 99.67% by machine learning method, better than the threshold method of 97.54% which is consistent with the experimental result. This work indicates that machine learning method can be a strong potential candidate for alleviating the restrictions of stably achieving high-fidelity and high-accuracy singlet-triplet state readout in large-scale quantum computing., Comment: 11 pages,11 figures

Pulse distortion, as one of the coherent error sources, hinders the characterization and control of qubits. In the semiconductor quantum dot system, the distortions on measurement pulses and control pulses disturb the experimental results, while no effective calibration procedure has yet been reported. Here, we demonstrate two different calibration methods to calibrate and correct the distortion using the two-qubit system as a detector. The two calibration methods have different correction accuracy and complexity. One is the coarse predistortion (CPD) method, with which the distortion is partly relieved. The other method is the all predistortion (APD) method, with which we measure the transfer function and significantly improve the exchange oscillation homogeneity. The two methods use the exchange oscillation homogeneity as the metric and are appropriate for any qubit that oscillates with a diabatic pulse. With the APD procedure, an arbitrary control waveform can be accurately delivered to the device, which is essential for characterizing qubits and improving gate fidelity., Comment: 19 pages, 5 figures

We present an experimental study of Rayleigh-B\'enard convection using liquid metal alloy gallium-indium-tin as the working fluid with a Prandtl number of $Pr=0.029$. The flow state and the heat transport were measured in a Rayleigh number range of $1.2\times10^{4} \le Ra \le 1.3\times10^{7}$. The temperature fluctuation at the cell centre is used as a proxy for the flow state. It is found that, as $Ra$ increases from the lower end of the parameter range, the flow evolves from a convection state to an oscillation state, a chaotic state, and finally a turbulent state for $Ra>10^5$. The study suggests that the large-scale circulation in the turbulent state is a residual of the cell structures near the onset of convection, which is in contrast with the case of $Pr\sim1$, where the cell structure is replaced by high-order flow modes transiently before the emergence of the large-scale circulation in the turbulent state. The evolution of the flow state is also reflected by the heat transport characterised by the Nusselt number $Nu$ and the probability density function (PDF) of the temperature fluctuation at the cell centre. It is found that the effective local heat transport scaling exponent $\gamma$, i.e., $Nu\sim Ra^{\gamma}$, changes continuously from $\gamma=0.49$ at $Ra\sim 10^4$ to $\gamma=0.25$ for $Ra>10^6$. Meanwhile, the PDF at the cell centre gradually evolves from a Gaussian-like shape before the transition to turbulence to an exponential-like shape in the turbulent state. For $Ra>10^6$, the flow shows self-similar behaviour, which is revealed by the universal shape of the PDF of the temperature fluctuation at the cell centre and a $Nu=0.19Ra^{0.25}$ scaling for the heat transport., Comment: 12 pages, 5 figures; Accepted for publication in the Journal of Fluid Mechanics

Spin qubits based on silicon metal-oxide semiconductor (Si-MOS) quantum dots (QDs) are promising platforms for large-scale quantum computers. To control spin qubits in QDs, electric dipole spin resonance (EDSR) has been most commonly used in recent years. By delocalizing an electron across a double quantum dots charge state, flopping-mode EDSR has been realized in Si/SiGe QDs. Here, we demonstrate a flopping-mode spin qubit in a Si-MOS QD via Elzerman single-shot readout. When changing the detuning with a fixed drive power, we achieve s-shape spin resonance frequencies, an order of magnitude improvement in the spin Rabi frequencies, and virtually constant spin dephasing times. Our results offer a route to large-scale spin qubit systems with higher control fidelity in Si-MOS QDs., Comment: 5 pages, 4 figures

The single-shot readout data process is essential for the realization of high-fidelity qubits and fault-tolerant quantum algorithms in semiconductor quantum dots. However, the fidelity and visibility of the readout process is sensitive to the choice of the thresholds and limited by the experimental hardware. By demonstrating the linear dependence between the measured spin state probabilities and readout visibilities along with dark counts, we describe an alternative threshold-independent method for the single-shot readout of spin qubits in semiconductor quantum dots. We can obtain the extrapolated spin state probabilities of the prepared probabilities of the excited spin state through the threshold-independent method. Then, we analyze the corresponding errors of the method, finding that errors of the extrapolated probabilities cannot be neglected with no constraints on the readout time and threshold voltage. Therefore, by limiting the readout time and threshold voltage we ensure the accuracy of the extrapolated probability. Then, we prove that the efficiency and robustness of this method is 60 times larger than that of the most commonly used method. Moreover, we discuss the influence of the electron temperature on the effective area with a fixed external magnetic field and provide a preliminary demonstration for a single-shot readout up to 0.7 K/1.5T in the future., Comment: 18 pages, 6 figures

The buoyancy-driven dynamics of a pair of gas bubbles released in line is investigated numerically, focusing on highly inertial conditions under which isolated bubbles follow non-straight paths. In an early stage, the second bubble always drifts out of the wake of the leading one. Then, depending on the ratios of the buoyancy, viscous and capillary forces which define the Galilei ($Ga$) and Bond ($Bo$) numbers of the system, five distinct regimes specific to such conditions are identified, in which the two bubbles may rise independently or continue to interact and possibly collide in the end. In the former case, they usually perform large-amplitude planar zigzags within the same plane or within two distinct planes, depending on the oblateness of the leading bubble. However, for large enough $Ga$ and low enough $Bo$, they follow nearly vertical paths with small-amplitude erratic horizontal deviations. Increasing $Bo$ makes the wake-induced attraction toward the leading bubble stronger, forcing the two bubbles to realign vertically one or more times along their ascent. During such sequences, wake vortices may hit the trailing bubble, deflecting its path and, depending on the case, promoting or hindering further possibilities of interaction. In some regimes, varying the initial distance separating the two bubbles modifies their lateral separation beyond the initial stage. Similarly, minute initial angular deviations favour the selection of a single vertical plane of rise common to both bubbles. These changes may dramatically affect the fate of the tandem as, depending on $Bo$, they promote or prevent future vertical realignments., Comment: 34 pages, 22 figures

This paper describes the development and validation of a Monte Carlo (MC) dose computing module dedicated to organ dose calculations of patients undergoing nuclear medicine (NM) internal radiation exposures involving 18F-FDG PET/CT examination. This new module extends the more-than-10-years-long ARCHER project that developed a GPU-accelerated MC dose engine by adding dedicated NM source-definition features. To validate the code, we compared dose distributions from the 0.511-MeV point photon source calculated for a water phantom as well as a patient PET/CT phantom against a well-tested MC code, GATE. The water-phantom results show excellent agreement, suggesting that the radiation physics module in the new NM code is adequate. To demonstrate the clinical utility and advantage of ARCHER-NM, one set of PET/CT data for an adult male NM patient is calculated using the new code. Radiosensitive organs in the CT dataset are segmented using a CNN-based tool called DeepViewer. The PET image intensity maps are converted to radioactivity distributions to allow for MC radiation transport dose calculations at the voxel level. The dose rate maps and corresponding statistical uncertainties were calculated for the duration of PET image acquisition. The dose rate results of the 18F-FDG PET imaging patient show that ARCHER-NM's results agree very well with those of the GATE within 0.58% to 4.11%. Most impressively, ARCHER-NM obtains such results in less than 0.5 minutes while it takes GATE as much as 376 minutes. This is the first study presenting GPU-accelerated patient-specific MC internal radiation dose rate calculations for clinically realistic 18F-FDG PET/CT imaging cases involving auto-segmentation of whole-body PET/CT images. This study suggests that modern computing tools -- ARCHER-NM and DeepViewer -- are accurate and fast enough for routine internal dosimetry in NM clinics., Comment: 17 pages, 5 figures, 1 table

Direct numerical simulations (DNS) are performed to study the turbulent shear flow of an electrically conducting fluid in a cylindrical container. The flow is driven by the interaction between the radial electric currents ($I$) injected through a large number of small electrodes at the bottom wall and an axial magnetic field. All the numerical parameters, including the geometry of the container, the total injcected currents and the magnetic field, are in line with the experiment performed in J. Fluid Mech. 456, 137-159. First, witth laminar Hartmann layers, three dimensional simulations recover experimentally measured quantities (global angular momentum, velocity profiles). The variation laws for the wall shear stresses, the energy spectra and visualizations of flow structures near the side wall highlight separation and turbulence within the side wall layers. Furthermore, a parametric analysis of the flow reveals that Ekman recirculations have significant influence on the vortex size, the free shear layer, and the global dissipation. Second, we recover the scaling laws of the cutoff scale that separate the large quasi-two-dimensional scales from the small three-dimensional ones (J. Fluid Mech. 118, 507-518), and thus establish their validity in sheared MHD turbulence. Furthermore, we find that three-componentality are and the three-dimensionality appear concurrently and that both the two-dimensional cutoff frequency and the mean energy associated to the axial component of velocity scale with $N_t$, respectively as $0.063N_t^{0.37}$ and $0.126 N_t^{-0.92}$.

Tunable synthetic spin-orbit coupling (s-SOC) is one of the key challenges in various quantum systems, such as ultracold atomic gases, topological superconductors, and semiconductor quantum dots. Here we experimentally demonstrate controlling the s-SOC by investigating the anisotropy of spin-valley resonance in a silicon quantum dot. As we rotate the applied magnetic field in-plane, we find a striking nonsinusoidal behavior of resonance amplitude that distinguishes s-SOC from the intrinsic spin-orbit coupling (i-SOC), and associate this behavior with the previously overlooked in-plane transverse magnetic field gradient. Moreover, by theoretically analyzing the experimentally measured s-SOC field, we predict the quality factor of the spin qubit could be optimized if the orientation of the in-plane magnetic field is rotated away from the traditional working point., Comment: 26 pages, 10 figures

The buoyancy-driven motion of two identical gas bubbles released in line in a liquid at rest is examined with the help of highly resolved simulations, focusing on moderately inertial regimes in which the path of an isolated bubble is vertical. Assuming first an axisymmetric evolution, equilibrium configurations of the bubble pair are determined as a function of the buoyancy-to-viscous and buoyancy-to-capillary force ratios which define the Galilei ($Ga$) and Bond ($Bo$) numbers of the system, respectively. The three-dimensional solutions reveal that this axisymmetric equilibrium is actually never reached. Instead, provided $Bo$ stands below a critical $Ga$-dependent threshold determining the onset of coalescence, two markedly different evolutions are observed. At the lower end of the explored $(Ga,\,Bo)$-range, the tandem follows a Drafting-Kissing-Tumbling scenario which eventually yields a planar side-by-side motion. For larger $Ga$, the trailing bubble drifts laterally and gets out of the wake of the leading bubble, barely altering the path of the latter. In this second scenario, the late configuration is characterized by a significant inclination of the tandem ranging from $30^\circ$ to $40^\circ$ with respect to the vertical. Bubble deformation has a major influence on the evolution of the system. It controls the magnitude of vortical effects in the wake of the leading bubble, hence the strength of the attractive force acting on the trailing bubble. It also governs the strength and even the sign of the lateral force acting on this bubble, a mechanism of particular importance when the tandem is released with a small angular deviation., Comment: 44 pages, 27 figures

In silicon quantum dots (QDs), at a certain magnetic field commonly referred to as the "hot spot", the electron spin relaxation rate (T_1^(-1)) can be drastically enhanced due to strong spin-valley mixing. Here, we experimentally find that with a valley splitting of 78.2 ${\pm}$ 1.6 ${\mu}$eV, this hot spot in spin relaxation can be suppressed by more than 2 orders of magnitude when the in-plane magnetic field is oriented at an optimal angle, about 9{\deg} from the [100] sample plane. This directional anisotropy exhibits a sinusoidal modulation with a 180{\deg} periodicity. We explain the magnitude and phase of this modulation using a model that accounts for both spin-valley mixing and intravalley spin-orbit mixing. The generality of this phenomenon is also confirmed by tuning the electric field and the valley splitting up to 268.2 ${\pm}$ 0.7 ${\mu}$eV., Comment: 13 pages and 4 figures for Manuscript, 17 pages and 7 figures for Supplementary material

To achieve high throughput in the POW based blockchain systems, researchers proposed a series of methods, and DAG is one of the most active and promising fields. We designed and implemented the StreamNet, aiming to engineer a scalable and endurable DAG system. When attaching a new block in the DAG, only two tips are selected. One is the parent tip whose definition is the same as in Conflux[1]; another is using Markov Chain Monte Carlo (MCMC) technique by which the definition is the same as IOTA [2]. We infer a pivotal chain along the path of each epoch in the graph, and a total order of the graph could be calculated without a centralized authority. To scale up, we leveraged the graph streaming property; high transaction validation speed will be achieved even if the DAG is growing. To scale out, we designed the direct signal gossip protocol to help disseminate block updates in the network, such that messages can be passed in the network more efficiently. We implemented our system based on IOTA's reference code (IRI) and ran comprehensive experiments over the different sizes of clusters of multiple network topologies., Comment: FTC 2020 - Future Technologies Conference 2020 5-6 November 2020 | Vancouver, Canada

This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan areas: Northern Virginia and New York City. Our results show that paired tokens can capture the association rules inherent in the accident-related tweets and further increase the accuracy of the traffic accident detection. Second, two deep learning methods: Deep Belief Network (DBN) and Long Short-Term Memory (LSTM) are investigated and implemented on the extracted token. Results show that DBN can obtain an overall accuracy of 85% with about 44 individual token features and 17 paired token features. The classification results from DBN outperform those of Support Vector Machines (SVMs) and supervised Latent Dirichlet allocation (sLDA). Finally, to validate this study, we compare the accident-related tweets with both the traffic accident log on freeways and traffic data on local roads from 15,000 loop detectors. It is found that nearly 66% of the accident-related tweets can be located by the accident log and more than 80% of them can be tied to nearby abnormal traffic data. Several important issues of using Twitter to detect traffic accidents have been brought up by the comparison including the location and time bias, as well as the characteristics of influential users and hashtags., Comment: 17