Bright Quantum-Grade Fluorescent Nanodiamonds

Next-Generation Nanodiamond Sensors Achieve Quantum-Grade Performance

Japanese researchers have achieved a significant advancement in quantum sensing technology by developing nanodiamond sensors that combine excellent brightness for bioimaging with quantum-grade spin properties comparable to bulk diamonds. This breakthrough, published in ACS Nano […]

Visualization diagram of magnetic domains in a quantum antiferromagnet using nonreciprocal directional dichroism

Illuminating quantum magnets: Light unveils magnetic domains

Scientists have used light to visualize magnetic domains, and manipulated these regions using an electric field, in a quantum antiferromagnet. This method allows real-time observation of magnetic behaviors, paving the way for advancements in next-generation […]

The basic idea is to achieve quantum control through the application of the AI agent (left). For instance, to cool the quantum ball (red) down to the bottom of the well in presence of environmental noises, the AI controller, which is based on reinforcement learning, would identify intelligent control pulses (middle polar graph).

Pulses driven by artificial intelligence tame quantum systems

It’s easy to control the trajectory of a basketball: all we have to do is apply mechanical force coupled with human skill. But controlling the movement of quantum systems such as atoms and electrons is much more challenging, as these minuscule scraps of matter often fall prey to perturbations that knock them off their path in unpredictable ways. Movement within the system degrades — a process called damping — and noise from environmental effects such as temperature also disturbs its trajectory.

Results of geometry optimizations for H2 molecule. Geometry optimizations with various initial values of the H–H interatomic distance revealed that the calculation quickly converges to the equilibrium bond length within 10 iterations, no matter which interatomic distance is used to start the calculation.

Quantum algorithm of the direct calculation of energy derivatives developed for molecular geometry optimization

Researchers have successfully extended the quantum phase difference estimation algorithm, a general quantum algorithm for the direct calculations of energy gaps, to enable the direct calculation of energy differences between two different molecular geometries. This allows for the computation, based on the finite difference method, of energy derivatives with respect to nuclear coordinates in a single calculation.