Metal--topological-insulator transition in the quantum kicked rotator with Z2 symmetry

The quantum kicked rotator is a periodically driven dynamical system with a metal-insulator transition. We extend the model so that it includes phase transitions between a metal and a topological insulator, in the universality class of the quantum spin Hall effect. We calculate the Z2 topological invariant using a scattering formulation that remains valid in the presence of disorder. The scaling laws at the phase transition can be studied efficiently by replacing one of the two spatial dimensions with a second incommensurate driving frequency. We find that the critical exponent does not depend on the topological invariant, in agreement with earlier independent results from the network model of the quantum spin Hall effect.Comment: 5 figures, 6 page

Real-time two-axis control of a spin qubit

Optimal control of qubits requires the ability to adapt continuously to their ever-changing environment. We demonstrate a real-time control protocol for a two-electron singlet-triplet qubit with two fluctuating Hamiltonian parameters. Our approach leverages single-shot readout classification and dynamic waveform generation, allowing full Hamiltonian estimation to dynamically stabilize and optimize the qubit performance. Powered by a field-programmable gate array (FPGA), the quantum control electronics estimates the Overhauser field gradient between the two electrons in real time, enabling controlled Overhauser-driven spin rotations and thus bypassing the need for micromagnets or nuclear polarization protocols. It also estimates the exchange interaction between the two electrons and adjusts their detuning, resulting in extended coherence of Hadamard rotations when correcting for fluctuations of both qubit axes. Our study highlights the role of feedback in enhancing the performance and stability of quantum devices affected by quasistatic noise

The use of non-invasive stool tests for verification of Helicobacter pylori eradication and clarithromycin resistance

Background: Clarithromycin resistance of Helicobacter pylori (H. pylori) represents a major challenge in eradication therapy. In this study, we assessed if non-invasive stool tests can be used to verify successful H. pylori eradication and determine clarithromycin resistance. Materials and methods:In this prospective study, patients undergoing urea breath testing (UBT) for confirmation of H. pylori eradication were asked to collect the stool as both a dry fecal sample and fecal immunochemical test (FIT). Stool H. pylori antigen testing (SAT) was performed on these samples and assessed for its accuracy in eradication verification. Type and duration of antibiotic treatment were retrospectively collected from patient records and compared with clarithromycin resistance determined by PCR of stool samples. Results: H. pylori eradication information was available for a total of 145 patients (42.7% male, median age: 51.2). Successful eradication was achieved in 68.1% of patients. SAT on FIT samples had similar accuracy for eradication assessment compared to dry fecal samples, 72.1% [95% CI 61.4–81.2] versus 72.2% [95% CI 60.9–81.7]. Clarithromycin resistance rate was 13.4%. Conclusion: H. pylori antigen testing on FIT stool samples to verify H. pylori eradication is feasible and has similar accuracy as H. pylori antigen testing on dry stool samples. Dry stool, but not FIT, was suitable for non-invasive identification of H. pylori clarithromycin resistance by rt-PCR personalizing antibiotic treatment strategies without the need for invasive diagnostics is desirable, as the cure rate of first-line empirical H. pylori treatment remains low.</p

NetKet: A machine learning toolkit for many-body quantum systems

We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wavefunctions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wavefunction data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics