EPJ Quantum Technology最新文献

We aim to use quantum machine learning to detect various anomalies in image inspection by using small size data. Assuming the possibility that the expressive power of the quantum kernel space is superior to that of the classical kernel space, we are studying a quantum machine learning model. Through trials of image inspection processes not only for factory products but also for products including agricultural products, the importance of trials on real data is recognized. In this study, training was carried out on SVMs embedded with various quantum kernels on a small number of agricultural product image data sets collected in the markets. The quantum kernels prepared in this study consisted of a smaller number of rotating gates and control gates. The F1 scores for each quantum kernel showed a significant effect of using CNOT gates. After confirming the results with a quantum simulator, the usefulness of the quantum kernels was confirmed on a quantum computer. Learning with SVMs embedded with specific quantum kernels showed significantly higher values of the AUC compared to classical kernels. The reason for the lack of learning in quantum kernels is considered to be due to kernel concentration or exponential concentration similar to the Baren plateau. The reason why the F1 score does not increase as the number of features increases is suggested to be due to exponential concentration, while at the same time it is possible that only certain features have discriminative ability. Furthermore, it is suggested that controlled Toffoli gate may be a promising quantum kernel component.

Multi-photon emissions in laser sources represent a serious threat for the security of quantum key distribution (QKD). While the decoy-state technique allows to solve this problem, it requires uniform phase randomisation of the emitted pulses. However, gain-switched lasers operating at high repetition rates do not fully satisfy this requirement, as residual photons in the laser cavity introduce correlations between the phases of consecutive pulses. Here, we introduce experimental schemes to characterise the phase probability distribution of the emitted pulses, and demonstrate that an optimisation task over interferometric measures suffices in determining the impact of arbitrary order correlations, which ultimately establishes the security level of the implementation according to recent security proofs. We expect that our findings may find usages beyond QKD as well.

We present a model of a novel measurement scheme to detect small amounts of a gas species via the ground-state fluctuations of the electromagnetic field (dispersion forces) depending on the entire spectral properties of all objects.

Here, we describe an a theoretical measurement scheme of optically trapped nanoparticles in a hollow-core fibre. We calculate the effects of the gases on the thermal motion of the nanoparticles and present a neural network-based method for reconstructing the gas concentrations. We present an example of one possible setup capable of detecting concentrations of CO₂ down to 0.01 volume per cent with an accuracy of 1 ppm.

Reliable detection of small concentrations of specific molecules in a gas is essential for numerous applications such as security and environmental monitoring, medical tests, and production processes. Unlike other measurement schemes, such as surface plasmons or functionalised surfaces, this could allow fast, continuous monitoring and using small sample quantities, without influencing the probe or the sensor system.

A quantum gravity gradiometer in a low Earth orbit, operating in a cross-track configuration, could be a viable single-spacecraft measurement instrument to provide mass change data for Earth observation, at comparable or better resolutions to existing maps generated by GRACE-FO. To reach the sensitivity for these science-grade measurements, many parts of the cold-atom interferometer need to be operating at, or beyond, state-of-the-art performance. In order to raise the maturity of the technology of the cold-atom gradiometer and determine the feasibility of a science-grade instrument, a pathfinder technology demonstration platform is funded. The requirements and a notional design for such a pathfinder and the outstanding challenges for science-grade instruments are presented.

Atom interferometers are employed for numerous purposes such as inertial sensing. They measure forces by encoding their signal in phase shifts between matter waves. Signal extraction algorithms typically require the resulting interference patterns to feature a priori known spatial distributions of intensity and phase. Deviations from these assumed spatial distributions, such as those caused by inhomogeneous laser wave fronts, can lead to systematic errors. For long interrogation times, such as for space operation, these distributions can display highly complex structures. We present an extraction algorithm designed for interference patterns featuring arbitrary and unknown temporally stable spatial phase profiles utilizing Principal Component Analysis. It characterizes complex phase profiles and thereby turns effects into a measured quantity which caused systematic errors in previous algorithms. We verify the algorithm’s accuracy and assess the statistical reconstruction error in the presence of atom projection noise as a function of the number of atoms and images. Finally, we extract the spatial phase profiles from experimental data obtained by an atom gravimeter.

Alleviating high workloads for teachers is crucial for continuous high quality education. To evaluate if Large Language Models (LLMs) can alleviate this problem in the quantum computing domain, we conducted two complementary studies exploring the use of GPT-4 to automatically generate tips for students. (1) A between-subject survey in which students (N = 46) solved four multiple-choice quantum computing questions with either the help of expert-created or LLMgenerated tips. To correct for possible biases, we additionally introduced two deception conditions. (2) Experienced educators and students (N = 23) directly compared the LLM-generated and expert-created tips. Our results show that the LLM-generated tips were significantly more helpful and pointed better towards relevant concepts while also giving away more of the answers. Furthermore, we found that participants in the first study performed significantly better in answering the quantum computing questions when given tips labeled as LLM-generated, even if they were expert-created. This points towards a placebo effect induced by the participants’ biases for LLM-generated content. Ultimately, we contribute that LLM-generated tips can be used instead of expert tips to support teaching of quantum computing basics.

Recently many data centers have been constructed for artificial intelligence (AI) research. The important condition of the data center is to supply sufficient electricity, resulting in many electricity transformers being installed. Especially, these electricity transformers have led to significant heat generation in many data centers. Therefore, managing the temperature of electricity transformers has emerged as an important task. Notably, numerous studies are being conducted to manage and forecast the temperature of electricity transformers using artificial intelligence models. However, as the size of predictive models increases and computational demands grow, substantial computing resources are required. Consequently, there are instances where the lack of computing resources makes these models difficult to operate. To address these challenges, we propose a quantum segment recurrent neural network (QSegRNN), a time series forecasting model utilizing quantum computing. QSegRNN leverages quantum computing to achieve comparable performance with fewer parameters than classical counterpart models under similar conditions. QSegRNN inspired by a classical SegRNN uses the quantum cell instead of the classical cell in the model. The advantage of this structure is that it can be designed with fewer parameters under similar architecture. To construct the quantum cell, we benchmark the quantum convolutional circuit with amplitude embedding as the variational quantum circuit, minimizing information loss while considering the limit of noisy intermediate-scale quantum (NISQ) devices. The experiment result illustrates that the forecasting performance of QSegRNN achieves better performance than SegRNN and other forecasting models even though QSegRNN has only 85 percent of the parameters.

Compact optical atomic clocks have become increasingly important in field applications and clock networks. Systems based on Ramsey-Bordé interferometry (RBI) with a thermal atomic beam seem promising to fill a technology gap in optical atomic clocks, as they offer higher stability than optical vapour cell clocks while being less complex than cold atomic clocks.

Here, we demonstrate RBI with strontium atoms, utilizing the narrow intercombination line at 689 nm, yielding a 60 kHz broad spectral feature. The obtained Ramsey fringes for varying laser power are analyzed and compared with a numerical model. The transition at 461 nm is used for fluorescence detection. Analyzing the slope of the RBI signal and the fluorescence detection noise yields an estimated short-term stability of (<4times 10^{-14} / sqrt{tau / {1~s}}). We present our experimental setup in detail, including the atomic beam source, frequency-modulation spectroscopy to lock the 461 nm laser, laser power stabilization and the high-finesse cavity pre-stabilization of the 689 nm laser.

Our system serves as a ground testbed for future clock systems in mobile and space applications.

Satellite to ground quantum communication typically operates at night to reduce background signals, however it remains susceptible to noise from light pollution of the night sky. In this study we compare several methodologies for determining whether a Quantum Ground Station (QGS) site is viable for exchanging quantum signals with the upcoming Quantum Encryption and Science Satellite (QEYSSat) mission. We conducted ground site characterization studies at three locations in Canada: Waterloo, Ontario, Calgary, Alberta, and Priddis, Alberta. Using different methods we estimate the background counts expected to leak into the satellite-ground quantum channel, and determined whether the noise levels could prevent a quantum key transfer. We also investigate how satellite data recorded from the Visible Infrared Imaging Radiometer Suite (VIIRS) can help estimate conditions of a particular site, and find reasonable agreement with the locally recorded data. Our results indicate that the Waterloo, Calgary, and Priddis QGS sites should allow both quantum uplinks and downlinks with QEYSSat, despite their proximity to urban centres. Furthermore, our approach allows the use of satellite borne instrument data (VIIRS) to remotely and efficiently determine the potential of a ground site.