Two new methods reduce noise and remove errors in quantum observables by focusing on individual noise sources. They add little qubit overhead and can be used in quantum sensing and general quantum experimentation, as well as quantum computing.
In a Nature Communications article, a team led by Center for Nanoscale Materials researchers introduces a machine learning workflow of models for water transformations that increases accuracy at lower computational cost.
Using a single actuation signal, a frequency comb is generated in a micromechanical resonator from two vibrational modes, flexural and torsional, whose interactions are responsible for the unique response.
In a study published in Nano Letters, researchers experimentally show that excitations or defects carrying magnetic charge in artificial spin ices introduce a topological defect in incident coherent electron waves.
In a study published in Science, researchers’ findings enable a broad exploration of synthetic 2D polymer structures and properties. This work was a multidisciplinary team effort including DOE’s CNM and APS user facilities at Argonne.