Tunable coupler to fully decouple superconducting qubits

Enhancing the capabilities of superconducting quantum hardware, requires higher gate fidelities and lower crosstalk, particularly in larger scale devices, in which qubits are coupled to multiple neighbors. Progress towards both of these objectives would highly benefit from the ability to fully control all interactions between pairs of qubits. Here we propose a new coupler model that allows to fully decouple dispersively detuned Transmon qubits from each other, i.e. ZZ-crosstalk is completely suppressed while maintaining a maximal localization of the qubits' computational basis states. We further reason that, for a dispersively detuned Transmon system, this can only be the case if the anharmonicity of the coupler is positive at the idling point. A simulation of a 40ns CZ-gate for a lumped element model suggests that achievable process infidelity can be pushed below the limit imposed by state-of-the-art coherence times of Transmon qubits. On the other hand, idle gates between qubits are no longer limited by parasitic interactions. We show that our scheme can be applied to large integrated qubit grids, where it allows to fully isolate a pair of qubits, that undergoes a gate operation, from the rest of the chip while simultaneously pushing the fidelity of gates to the limit set by the coherence time of the individual qubits.Comment: 6 pages, 4 figure

Forecasting the data cube

Forecasting time series data is crucial in a number of domains such as supply chain management and display advertisement. In these areas, the time series data to forecast is typically organized along multiple dimensions leading to a high number of time series that need to be forecasted. Most current approaches focus only on selection and optimizing a forecast model for a single time series. In this paper, we explore how we can utilize time series at different dimensions to increase forecast accuracy and, optionally, reduce model maintenance overhead. Solving this problem is challenging due to the large space of possibilities and possible high model creation costs. We propose a model configuration advisor that automatically determines the best set of models, a model configuration, for a given multi-dimensional data set. Our approach is based on a general process that iteratively examines more and more models and simultaneously controls the search space depending on the data set, model type and available hardware. The final model configuration is integrated into F2DB, an extension of PostgreSQL, that processes forecast queries and maintains the configuration as new data arrives. We comprehensively evaluated our approach on real and synthetic data sets. The evaluation shows that our approach significantly increases forecast query accuracy while ensuring low model costs

The Kinematics of Molecular Cloud Cores in the Presence of Driven and Decaying Turbulence: Comparisons with Observations

In this study we investigate the formation and properties of prestellar and protostellar cores using hydrodynamic, self-gravitating Adaptive Mesh Refinement simulations, comparing the cases where turbulence is continually driven and where it is allowed to decay. We model observations of these cores in the C$^{18}$O$(2\to 1)$, NH$_3(1,1)$, and N$_2$H$^+(1\to 0)$ lines, and from the simulated observations we measure the linewidths of individual cores, the linewidths of the surrounding gas, and the motions of the cores relative to one another. Some of these distributions are significantly different in the driven and decaying runs, making them potential diagnostics for determining whether the turbulence in observed star-forming clouds is driven or decaying. Comparing our simulations with observed cores in the Perseus and $\rho$ Ophiuchus clouds shows reasonably good agreement between the observed and simulated core-to-core velocity dispersions for both the driven and decaying cases. However, we find that the linewidths through protostellar cores in both simulations are too large compared to the observations. The disagreement is noticably worse for the decaying simulation, in which cores show highly supersonic infall signatures in their centers that decrease toward their edges, a pattern not seen in the observed regions. This result gives some support to the use of driven turbulence for modeling regions of star formation, but reaching a firm conclusion on the relative merits of driven or decaying turbulence will require more complete data on a larger sample of clouds as well as simulations that include magnetic fields, outflows, and thermal feedback from the protostars.Comment: 18 pages, 12 figures, accepted to A

Experimental measurement and numerical analysis of group velocity dispersion in cladding modes of an endlessly single-mode photonic crystal fiber

The optical properties of the guided modes in the core of photonic crystal fibers (PCFs) can be easily manipulated by changing the air-hole structure in the cladding. Special properties can be achieved in this case such as endless singlemode operation. Endlessly single-mode fibers, which enable single-mode guidance over a wide spectral range, are indispensable in the field of fiber technology. A two-dimensional photonic crystal with a silica central core and a micrometer-spaced hexagonal array of air holes is an established method to achieve endless single-mode properties. In addition to the guidance of light in the core, different cladding modes occur. The coupling between the core and the cladding modes can affect the endlessly single-mode guides. There are two possible ways to determine the dispersion: measurement and calculation. We calculate the group velocity dispersion (GVD) of different cladding modes based on the measurement of the fiber structure parameters, the hole diameter and the pitch of a presumed homogeneous hexagonal array. Based on the scanning electron image, a calculation was made of the optical guiding properties of the microstructured cladding. We compare the calculation with a method to measure the wavelength-dependent time delay. We measure the time delay of defined cladding modes with a homemade supercontinuum light source in a white light interferometric setup. To measure the dispersion of cladding modes of optical fibers with high accuracy, a time-domain white-light interferometer based on a Mach-Zehnder interferometer is used. The experimental setup allows the determination of the wavelengthdependent differential group delay of light travelling through a thirty centimeter piece of test fiber in the wavelength range from VIS to NIR. The determination of the GVD using different methods enables the evaluation of the individual methods for characterizing the cladding modes of an endlessly single-mode fiber

Quantum Natural Policy Gradients: Towards Sample-Efficient Reinforcement Learning

Reinforcement learning is a growing field in AI with a lot of potential. Intelligent behavior is learned automatically through trial and error in interaction with the environment. However, this learning process is often costly. Using variational quantum circuits as function approximators can reduce this cost. In order to implement this, we propose the quantum natural policy gradient (QNPG) algorithm -- a second-order gradient-based routine that takes advantage of an efficient approximation of the quantum Fisher information matrix. We experimentally demonstrate that QNPG outperforms first-order based training on Contextual Bandits environments regarding convergence speed and stability and thereby reduces the sample complexity. Furthermore, we provide evidence for the practical feasibility of our approach by training on a 12-qubit hardware device.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. 7 pages, 5 figures, 1 tabl

Quantum Policy Gradient Algorithm with Optimized Action Decoding

Quantum machine learning implemented by variational quantum circuits (VQCs) is considered a promising concept for the noisy intermediate-scale quantum computing era. Focusing on applications in quantum reinforcement learning, we propose a specific action decoding procedure for a quantum policy gradient approach. We introduce a novel quality measure that enables us to optimize the classical post-processing required for action selection, inspired by local and global quantum measurements. The resulting algorithm demonstrates a significant performance improvement in several benchmark environments. With this technique, we successfully execute a full training routine on a 5-qubit hardware device. Our method introduces only negligible classical overhead and has the potential to improve VQC-based algorithms beyond the field of quantum reinforcement learning.Comment: Accepted to the 40th International Conference on Machine Learning (ICML 2023), Honolulu, Hawaii, USA. 22 pages, 10 figures, 3 table

Інвестиційна політика як один з пріорітетних шляхів розвитку економіки регіонів

В сучасних умовах спаду економічного розвитку регіонів важливу роль відіграють пошуки нових напрямів в регіональній реформі. На сучасному етапі дуже мало приділяється уваги новаторським питанням. Таким чином, розвиток інноваційної діяльності суб’єктів господарювання регіону може бути використаний як дієвий чинник економічного оздоровлення

The human 'pitch center' responds differently to iterated noise and Huggins pitch

A magnetoencephalographic marker for pitch analysis (the pitch onset response) has been reported for different types of pitch-evoking stimuli, irrespective of whether the acoustic cues for pitch are monaurally or binaurally produced. It is claimed that the pitch onset response reflects a common cortical representation for pitch, putatively in lateral Heschl's gyrus. The result of this functional MRI study sheds doubt on this assertion. We report a direct comparison between iterated ripple noise and Huggins pitch in which we reveal a different pattern of auditory cortical activation associated with each pitch stimulus, even when individual variability in structure-function relations is accounted for. Our results suggest it may be premature to assume that lateral Heschl's gyrus is a universal pitch center

MATCHING TRICEPS SURAE MUSCLE STRENGTH AND TENDON STIFFNESS ELIMINATES AGE-RELATED DIFFERENCES IN DROP-JUMP PERFORMANCE

We aimed to determine the influence of triceps surae (TS) muscle strength and Achilles tendon (AT) stiffness on age-related changes in locomotor performance and motor task execution strategy during a drop jump (DJ) task. After matching 12 young and 12 middleaged adults for TS muscle strength and AT stiffness, all subjects performed a series of DJs from different starting heights. Matched young and middle-aged adults showed similar DJ performance but the middle-aged adults showed significantly longer ground contact times, lower values in maximum vertical ground reaction force during the support phase and lower mechanical power, independent of starting height. These results suggest that leg extensor muscle strength and tendon stiffness are the primary drivers of age-related changes in locomotor performance, but not motor task execution strategy selection during jumping