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Recently proposed quantum-chaotic sensors achieve quantum enhancements in measurement precision by applying nonlinear control pulses to the dynamics of the quantum sensor while using classical initial states that are easy to prepare. Here, we use the cross-entropy method of reinforcement learning (RL) to optimize the strength and position of control pulses. Compared to the quantum-chaotic sensors with periodic control pulses in the presence of superradiant damping, we find that decoherence can be fought even better and measurement precision can be enhanced further by optimizing the control. In some examples, we find enhancements in sensitivity by more than an order of magnitude. By visualizing the evolution of the quantum state, the mechanism exploited by the RL method is identified as a kind of spin-squeezing strategy that is adapted to the superradiant damping.

In this study, we compared the performance of quantum temperature sensors (QTSs) with conventional sensors (CSs), highlighting differences in measurement accuracy and stability. Quantum sensors (QSs), known for their ability to provide ultra-precise measurements (UPMs), were tested across a temperature range of −10 to 40 °C. The results indicate that QSs offer superior accuracy, with a lower average error and a smaller standard deviation compared to CSs, indicating better measurement stability. For this comparison, we utilized Python scripts to conduct simulations and statistical analyses, leading to precise and reproducible results. The sensor performance was simulated in a controlled environment, and the obtained data were compared with experimental results. This comparison reveals that QSs are more reliable for applications requiring high precision, such as those in the Internet of Things (IoT) domain. These findings underscore the potential advantage of QSs in critical systems where measurement accuracy is paramount.

This paper proposes a perimeter detection scheme based on the quantum physical properties of photons. Existing perimeter intrusion detection schemes, if using light, rely on the classical properties of light only. Our quantum sensor network uses the quantum property of spatial superposition of photons, meaning that a photon can simultaneously follow two different paths after going through a beam splitter. Using multiple Mach–Zehnder interferometers, an entire web of paths can be generated, such that one single photon occupies them all. If an intruder violates this web in some arbitrary point, the entire photon superposition is destroyed, the photon does not self-interfere any more and this event is detected by measurements. For one single photon, the intruder detection probability is limited theoretically but can be increased arbitrarily with the usage of a sequence of photons. We show both theoretical bounds as well as practical results of the proposed schemes. The practical results are obtained by simulation experiments on IBM Quantum platforms. The benefits of our quantum approach are: low power, invisibility to potential intruders, scalability and easy practical implementation.

The aim of this review is to provide a survey of the recent advances and the main remaining challenges related to the ultrananocrystalline diamond (UNCD) nanowires and other nanostructures which exhibit excellent capability as the core components for many diverse novel sensing devices, due to the unique material properties and geometry advantages. The boron or nitrogen doping introduced in the gas phase during deposition promotes p-type or n-type conductivity. With the establishment of the UNCD nanofabrication techniques, more and more nanostructure-based devices are being explored in measuring basic physical and chemical parameters via classic and quantum methods, as exemplified by gas sensors, ultraviolet photodetectors, piezoresistance effect-based devices, biological applications and biosensors, and nitrogen-vacancy color center-based magnetic field quantum sensors. Highlighted finally are some of the remaining challenges and the future outlook in this area.

Precision quantum sensors using cold atom interferometers with long interrogation times are often limited by the ballistic expansion of atomic samples after release from traps, manifesting by means of laser beam wavefront uncertainties. In this study we utilize near-resonant light interacting with an ultracold atomic sample for collective-mode excitation of a 87 Rb Bose Einstein condensate (BEC) in a magnetic trap. The collective motion is initiated after abruptly modifying the atom-atom interaction energy by reduction of the BEC atom number density via photon scattering using the two-photon transition from 5 S 1 / 2 to 5 D 5 / 2 . We show that the two-photon transition can induce matter-wave lensing of the atomic cloud with minimal center-of-mass perturbation providing an optimal ultra-cold atomic sample for atom-based quantum sensors such as quantum gravimeters and accelerometers.

Emerging miniaturized atomic sensors such as optically pumped magnetometers (OPMs) have attracted widespread interest due to their application in high-spatial-resolution biomagnetism imaging. While optical probing systems in conventional OPMs require bulk optical devices including linear polarizers and lenses for polarization conversion and wavefront shaping, which are challenging for chip-scale integration. In this study, an integrated optical probing scheme based on localized-interference metasurface for chip-scale OPM is developed. Our monolithic metasurface allows tailorable linear polarization conversion and wavefront manipulation. Two silicon-based metasurfaces namely meta-polarizer and meta-polarizer-lens are fabricated and characterized, with maximum transmission efficiency and extinction ratio (ER) of 86.29 % and 14.2 dB for the meta-polarizer as well as focusing efficiency and ER of 72.79 % and 6.4 dB for the meta-polarizer-lens, respectively. A miniaturized vapor cell with 4 × 4 × 4 mm dimension containing Rb and N is combined with the meta-polarizer to construct a compact zero-field resonance OPM for proof of concept. The sensitivity of this sensor reaches approximately 9 fT/Hz with a dynamic range near zero magnetic field of about ±2.3 nT. This study provides a promising solution for chip-scale optical probing, which holds potential for the development of chip-integrated OPMs as well as other advanced atomic devices where the integration of optical probing system is expected.

Electron beam irradiation into type-Ib diamond is known as a good method for the creation of high concentration negatively-charged nitrogen-vacancy (NV−) centers by which highly sensitive quantum sensors can be fabricated. In order to understand the creation mechanism of NV− centers, we study the behavior of substitutional isolated nitrogen (P1 centers) and NV− centers in type-Ib diamond, with an initial P1 concentration of 40–80 ppm by electron beam irradiation up to 8.0 × 1018 electrons/cm2. P1 concentration and NV− concentration were measured using electron spin resonance and photoluminescence measurements. P1 center count decreases with increasing irradiation fluence up to 8.0 × 1018 electrons/cm2. The rate of decrease in P1 is slightly lower at irradiation fluence above 4.0 × 1018 electrons/cm2 especially for samples of low initial P1 concentration. Comparing concentration of P1 centers with that of NV− centers, it suggests that a part of P1 centers plays a role in the formation of other defects. The usefulness of electron beam irradiation to type-Ib diamonds was confirmed by the resultant conversion efficiency from P1 to NV− center around 12–19%.

Quantum sensing utilizes quantum effects, such as entanglement and coherence, to measure physical signals. The performance of a sensing process is characterized by error which requires comparison to a true value. However, in practice, such a true value might be inaccessible. In this study, we utilize quantum reliability as a metric to evaluate quantum sensor’s performance based solely on the apparatus itself, without any prior knowledge of the true value. We derive a general relationship among reliability, sensitivity, and systematic error, and demonstrate this relationship using a typical quantum sensing process. That is to measure magnetic fields (as a signal) by a spin- 1/2 particle and using the Stern–Gerlach apparatus to read out the signal information. Our findings illustrate the application of quantum reliability in quantum sensing, opening new perspectives for reliability analysis in quantum systems.

The miniaturization of quantum sensors is a popular trend for the development of quantum technology. One of the key components of these sensors is a coil which is used for spin modulation and manipulation. The bi-planar coils have the advantage of producing three-dimensional magnetic fields with only two planes of current confinement, whereas the traditional Helmholtz coils require three-dimensional current distribution. Thus, the bi-planar coils are compatible with the current micro-fabrication process and are quite suitable for the compact design of the chip-scale atomic devices that require stable or modulated magnetic fields. This paper presents a design of a miniature bi-planar coil. Both the magnetic fields produced by the coils and their inhomogeneities were designed theoretically. The magnetic field gradient is a crucial parameter for the coils, especially for generating magnetic fields in very small areas. We used a NMR (Nuclear Magnetic Resonance) method based on the relaxation of 131Xe nuclear spins to measure the magnetic field gradient in situ. This is the first time that the field inhomogeneities of the field of such small bi-planar coils have been measured. Our results indicate that the designed gradient caused error is 0.08 for the By and the Bx coils, and the measured gradient caused error using the nuclear spin relaxation method is 0.09±0.02, suggesting that our method is suitable for measuring gradients. Due to the poor sensitivity of our magnetometer under a large Bz bias field, we could not measure the Bz magnetic field gradient. Our method also helps to improve the gradients of the miniature bi-planar coil design, which is critical for chip-scale atomic devices.

This study proposes QYieldOpt, a hybrid quantum-classical framework for real-time resource optimization in precision farming, integrating a Quantum Approximate Optimization Algorithm (QAOA-R), Quantum Gradient Allocation Optimizer (QGAO), and quantum algorithm for Sensor Feedback Calibration (QSFC). All results presented in this study are based on simulation experiments using realistic agricultural data sets and quantum circuit emulators. Addressing the classical limitations in dynamic, multi-constraint agricultural environments, the system leverages quantum computing parallelism and ultra-sensitive environmental monitoring using quantum sensor networks (QSNs). QAOA-R solves discrete resource allocation (irrigation valve on/off decisions) via cost Hamiltonian optimization, achieving 89% water utilization and 8492 kg yield in the simulations. QGAO refines continuous variables (fertilizer dosage) using quantum-enhanced gradient descent, reducing resource waste by 30% using penalty-augmented utility functions. QSFC dynamically calibrates utility parameters via quantum sensor data, encoding variables like soil moisture into rotation gates ( ) with < 2% spectral error. The closed-loop architecture of the framework enables adaptive adjustments every 15–30 min using real-time QSN feedback. Empirical validation conducted entirely through simulation against classical models (LP, GA, PSO, RL) demonstrated superior performance with 12–18% yield improvements, 22% resource savings, and 4.3 s convergence for 100-zone farms. Under 20% sensor noise, QYieldOpt maintained robustness (R² = 0.919), outperforming classical baselines in terms of accuracy (MAE: 5.41 kg/zone) and scalability (10.6 s for 250 zones). By unifying quantum optimization with high-precision sensing, this study advances sustainable agriculture through energy-efficient resource management, which was validated in simulated and hybrid emulated cloud-edge environments. The modular design ensures theoretical compatibility with existing IoT systems, whereas field trials are essential to establish the practical feasibility of climate-resilient farming. As quantum hardware matures, QYieldOpt paves the way for autonomous and scalable solutions to global food security challenges in the future.