Ensembles of nitrogen-vacancy (NV) centers in diamond are versatile quantum sensors with broad applications in the physical and life sciences. The concentration of neutral substitutional nitrogen ( [ N s 0 ]) strongly influences NV electronic spin coherence times, sensitivity, and optimal sensing strategies. Diamonds with [ N s 0 ] ∼ 1–10 ppm are a focus of recent material engineering efforts, with higher concentrations being favorable for continuous-wave optically detected magnetic resonance (CW-ODMR) and lower concentrations expected to benefit pulsed magnetometry techniques through extended NV spin coherence times and improved sensing duty cycles. In this work, we synthesize and characterize low- [ N s 0 ] ( ∼ 0.8 ppm), NV-enriched diamond material, engineered through low-strain chemical vapor deposition (CVD) growth on high-quality substrates, 12 C isotopic purification, and controlled electron irradiation and annealing. Our results demonstrate good strain homogeneity in diamonds grown on CVD substrates and spin-bath-limited NV dephasing times. By measuring NV spin and charge properties across a wide range of optical NV excitation intensity, we provide direct comparisons of photon-shot-noise-limited magnetic field sensitivity between the current low- [ N s 0 ] and previously studied higher- [ N s 0 ] ( ∼ 14 ppm) NV-diamond sensors. We show that low- [ N s 0 ] diamond can outperform higher- [ N s 0 ] diamond at moderate and low optical NV excitation intensity. Our results provide practical benchmarks and guidance for selecting NV-diamond sensors tailored to specific experimental constraints and sensing requirements.

This study examines the dynamics of polarizable particles, coupled to a lossy cavity mode, that are transversally driven by a laser. The analysis is performed in a regime where the cavity linewidth exceeds the recoil frequency by several orders of magnitude. Using a two-stage cooling protocol, we show that the particles’ kinetic energy can be reduced to the recoil energy. This cooling protocol relies in its first stage on a high laser power such that the particles cool into a strongly self-organized pattern. This can be seen as a strongly magnetized state. In a second stage, we adiabatically ramp down the laser intensity such that the particles’ kinetic energy is transferred to their potential energy and the particles are “demagnetized”. In this second stage, we optimize the ramping speed, which needs to be fast enough to avoid unwanted heating and slow enough such that the dynamics remains approximately adiabatic.

The study of many-body quantum systems out of equilibrium remains a significant challenge, with complexity barriers arising in both state- and operator-based representations. Here, we review the recent approaches based on finding better contraction strategies for the full spatiotemporal tensor networks that encode the path integral of the dynamics, as well as the conceptual integration of influence functionals, process tensors, and transfer matrices within the tensor network formalism. We discuss recent algorithmic developments, highlight the complexity of influence functionals in various dynamical regimes, and present consistent results of different communities, showing how ergodic dynamics render these functionals exponentially difficult to compress. Finally, we provide an outlook on strategies to encode complementary influence functional overlaps, paving the way for accurate descriptions of open and closed quantum systems with tensor networks.

We present mathematical models that also may be formulated as computer models for experiments that feature single photon resolution and multiple pairs of polarizers to determine the sorting into ordinary and extraordinary channels. The models are based on Einstein’s hypothesis of elements of physical reality that determine the photon properties and are at first developed for Malus-type experiments. It is then shown that analogous models apply to the well-known Clauser-Aspect-Zeilinger experiments and violate all Bell-type inequalities without violating Einstein’s separation principle. The Bell-type inequalities do not apply to the actual experiments, because they cannot obey the physically necessary symmetry with respect to polarizer-pair rotations. We believe that these findings suggest a change of current interpretations of quantum entanglement away from instantaneous influences at a distance, as promoted in the physics Nobel-lectures 2022, and back toward Einstein’s ideas as well as the more recent ideas of Gerard ‘t Hooft.

Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges stemming from datasets that span more spatial dimensions. In fact, the best-performing clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qCLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qCLUE is inspired by CLUE, an algorithm developed to address the challenging time and memory budgets of Event Reconstruction (ER) in future High-Energy Physics experiments. As such, qCLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qCLUE in several scenarios, demonstrating its effectiveness and proving it to be a promising route to handle complex data analysis tasks – especially in high-dimensional datasets with high densities of points.

Ring Laser Gyroscopes, based on the Sagnac effect, are currently the most sensitive rotation sensors. GINGERINO, a RLG installed underground, shows a proved sensitivity that enters the few frad/s regime in about 2.5 days of integration time. On one hand, this sensitivity is well below the shot–noise–level as predicted applying to GINGERINO the so called independent beam model. On the other hand, it paves the way to the use of RLG in fundamental and quantum physics research. Indeed, high sensitivity rotation measurement opens to test general relativity and alternative theory of gravity. Moreover, it make possible to study the interplay between quantum effects in the optical domain and non-inertial reference frames.

Over the past decade many researchers have applied machine learning algorithms with computational chemistry and materials science tools to explore properties of catalysts. There is a rapid increase in publications demonstrating the use of machine learning for rational catalyst design. In our perspective, targeted tools for rational catalyst design will continue to make significant contributions. However, the community should focus on developing high-throughput simulation tools that utilize molecular dynamics capabilities for thorough exploration of the complex potential energy surfaces that exist, particularly in heterogeneous catalysis. Catalyst-specific databases should be developed to contain enough data to represent the complex multi-dimensional space that defines structure-function relationships. Machine learning tools will continue to impact rational catalyst design; however, we believe that more sophisticated pattern recognition algorithms would yield better understanding of structure-function relationships for heterogeneous catalysis.

There is an increasing interest in the sensing of magnetic, electric, and temperature effects in biological systems on the nanoscale. While there are existing classical sensors, the possibility of using quantum systems promises improved sensitivity and faster acquisition time. So far, much progress has been made in diamond color centers like the nitrogen-vacancy (NV) which not only satisfy key requirements for biosensing, like extraordinary photostability and non-toxicity, but they also show promise as room-temperature quantum computers/sensors. Unfortunately, the most-impressive demonstrations have been done in bulk diamond, since NVs in fluorescent nanodiamonds (FNDs) tend to have inferior properties. Yet FNDs are required for widespread nanoscale biosensing. In order for FND-based quantum sensors to approach the performance of bulk diamond, novel approaches are needed for their fabrication. To address this need we discuss opportunities for engineering the growth of FNDs.

Quantum key agreement (QKA) is an important quantum cryptography primitive. In a QKA protocol, two or more untrusted parties can agree on an identical key in such a way that they equally influence the key and no subset can decide it alone. However, in practical QKA, the imperfections of the participant’s detectors can be exploited to compromise the security and fairness of QKA. To remove all the detector-side-channel loopholes, a measurement-device-independent multi-party QKA protocol is proposed. The protocol exploits the post-selected GHZ states to generate a secure agreement key between legitimate participants, while ensuring the fairness of key agreement. Our protocol provides a new clue for the design of practical QKA protocols.

Most currently used cryptographic tools for protecting data are based on certain computational assumptions, which makes them vulnerable with respect to technological and algorithmic developments, such as quantum computing. One existing option to counter this potential threat is quantum key distribution, whose security is based on the laws of quantum physics. Quantum key distribution is secure against unforeseen technological developments. A second approach is post-quantum cryptography, which is a set of cryptographic primitives that are believed to be secure even against attacks with both classical and quantum computing technologies. From this perspective, this study reviews recent progress in the deployment of the quantum-secured infrastructure based on quantum key distribution, post-quantum cryptography, and their combinations. Various directions in the further development of the full-stack quantum-secured infrastructure are also indicated. Distributed applications, such as blockchains and distributed ledgers, are also discussed.