Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
The Q-value of electron capture in 163Ho has been determined with an uncertainty of 0.6 eV c–2 through a combination of high-precision Penning-trap mass spectrometry and precise atomic physics calculations. This high-precision measurement provides insight into systematic errors in neutrino mass measurements.
As counterparts to optical frequency combs, magnonic frequency combs could have broad applications if their initiation thresholds were low and the ‘teeth’ of the comb plentiful. Progress has now been made through exploiting so-called exceptional points to enhance the nonlinear coupling between magnons and produce wider magnonic frequency combs.
A practical and hardware-efficient blueprint for fault-tolerant quantum computing has been developed, using quantum low-density-parity-check codes and reconfigurable neutral-atom arrays. The scheme requires ten times fewer qubits and paves the way towards large-scale quantum computing using existing experimental technologies.
When photons impinge on a material, free electrons can be created by the photoelectric effect. The emitted electron current usually fluctuates with Poisson statistics, but if squeezed quantum light is applied, the electrons bunch up.
Questioning the validity of axioms can teach us about physics beyond the standard model. A recent search for the violation of charge conservation and the Pauli exclusion principle yields limits on these scenarios.
Although topological photonics has been an active field of research for some time, most studies still focus on the linear optical regime. This Perspective summarizes recent investigations into the nonlinear properties of discrete topological photonic systems.
The shape and trajectory of a crack plays a crucial role in material fracture. High-precision experiments now directly capture this phenomenon, unveiling the intricate 3D nature of cracks.
Even by shining classical light on a single opening, one can perform a double-slit experiment and discover a surprising variety of quantum mechanical multi-photon correlations — thanks to surface plasmon polaritons and photon-number-resolving detectors.
Stable regions in four-dimensional phase space have been observed by following the motion of accelerated proton beams subject to nonlinear forces. This provides insights into the physics of dynamical systems and may lead to improved accelerator designs.
Encouraging students to take ownership of their learning can improve their outcomes. This Perspective discusses ways to achieve this in the context of physics education and how digital technology can help Gen Z students in particular.
Women and ethnic and racial minority students are underrepresented in physics. This Review summarizes research on equity and inclusion in physics education and makes recommendations for making physics learning environments more equitable.
The concept of temporal mode-locking has been leveraged to study the interplay between laser mode-locking and photonic lattices that exhibit non-Hermitian topological phenomena. The results suggest new opportunities to study nonlinear and non-Hermitian topological physics as well as potential applications to sensing, optical computing and frequency-comb design.
Excitation of magnons — quanta of spin-waves — in an antiferromagnet can be used for high-speed data processing. The addition and subtraction of two such modes opens up possibilities for magnon-based information transfer in the terahertz spectral region.
The Hamiltonian describing a quantum many-body system can be learned using measurements in thermal equilibrium. Now, a learning algorithm applicable to many natural systems has been found that requires exponentially fewer measurements than existing methods.
Quantum computers promise to efficiently predict the structure and behaviour of molecules. This Perspective explores how this could overcome existing challenges in computational drug discovery.
