EPJ Quantum Technology最新文献

Tsunami is one of the deadliest natural disasters which can occur, leading to great loss of life and property. This study focuses on predicting tsunamis, using earthquake dataset from the year 1995 to 2023. The research introduces the Hybrid Quantum Neural Network (HQNN), an innovative model that combines Neural Network (NN) architecture with Parameterized Quantum Circuits (PmQC) to tackle complex machine learning (ML) problems where deep learning (DL) models struggle, aiming for higher accuracy in prediction while maintaining a compact model size. The hybrid model’s performance is compared with the classical model counterpart to investigate the quantum circuit’s effectivity as a layer in a DL model. The model has been implemented using 2-6 features through Principle Component Analysis (PCA) method. HQNN’s quantum circuit is a combination of Pennylane’s embedding (Angle Embedding (AE) and Instantaneous Quantum Polynomial (IQP) Embedding) and layer circuits (Basic Entangler Layers (BEL), Random Layers (RL), and Strongly Entangling Layers (SEL)), along with the classical layers. Results show that the proposed model achieved high performance, with a maximum accuracy up to 96.03% using 4 features with the combination of AE and SEL, superior to the DL model. Future research could explore the scalability and diverse applications of HQNN, as well as its potential to address practical ML challenges.

As a promising model in Quantum Machine Learning (QML), Quantum Generative Adversarial Networks (QGANs) are rapidly advancing, offering applications in image processing and generation. However, another emerging paradigm represents an image as a Quantum Implicit Neural Representation (QINR). In this work, we propose a novel architectural technique for building QINR-based QGAN to enhance the quality of images generated by QGANs. Additionally, we integrate classical techniques, such as Gradient Penalty and Wasserstein distance, to train QINR-QGAN. In image generation tasks, we demonstrated that QINR-QGAN can achieve performance comparable to state-of-the-art (SOTA) models while significantly reducing the number of trainable quantum parameters. Specifically, QINR-QGAN reduced the trainable quantum parameters by nearly 10 times compared to PQWGAN (Tsang et al. in IEEE Trans. Quantum Eng. 4:1–19, 2023) and Quantum AnoGAN (Herr et al. Quantum Sci. Technol. 6(4): 045004, 2021), demonstrating its superior efficiency in parameter optimization without sacrificing performance. Furthermore, we conducted experiments on the CelebA dataset to tackle a more complex task and generate larger images ((78times 64)). The results indicate that our model is capable of successfully completing the face generation task.

In this article, we investigate a growing trend in the worldwide Quantum Technology (QT) education landscape, that of the development of master’s programs, intended to provide graduates with the knowledge and skills to take a job in the quantum industry, while serving a much shorter timeline than a doctoral degree. Through a global survey, we identified 86 master’s programs, with substantial growth since 2021. Over time master’s have become increasingly interdisciplinary, organised by multiple faculties or through joint degree programs, and offer more hands-on experiences such as internships in companies. Information from program organisers and websites suggests that the intended career destinations of their graduates are a diverse range of industries, and therefore master’s programs may be a boon to the industrialisation of quantum technologies. Finally, we identify a range of national efforts to grow the quantum workforce of many countries, “quantum program enhancements”, which augment the content of existing study programs with quantum content. This may further contribute to the growth and viability of master’s programs as a route to the quantum industry.

With the increasing industrial relevance of quantum technologies (QTs), a new quantum workforce with special qualification will be needed. Building this workforce requires educational efforts, ranging from short term training to degree programs. In order to plan, map and compare such efforts, personal qualifications or job requirements, standardization is necessary. The European Competence Framework for Quantum Technologies (CFQT) provides a common language for QT education. The 2024 update to version 2.5 extends it by the new proficiency triangle and qualification profiles: The proficiency triangle proposes six proficiency levels for three proficiency areas, specifying knowledge and skills for each level. Nine qualification profiles show prototypical qualifications or job roles relevant to the quantum industry, with the required proficiency, examples, and suggestions. This is an important step towards the standardization of QT education. The CFQT update is based on the results of an analysis of 34 interviews on industry needs. The initial findings from the interviews were complemented by iterative refinement and expert consultation.

We propose a scheme to achieve nonreciprocal mechanical squeezing in a hybrid Kerr-modified cavity magnomechanical system, where the magnon mode is driven by two-tone microwave fields. The nonreciprocity originates from the magnon Kerr effect. Strong mechanical squeezing beyond the 3 dB limit can be nonreciprocally generated by adjusting the magnon frequency detuning, effective magnomechanical coupling strength, as well as the damping of the oscillator and the dissipation of the cavity. Importantly, the proposed scheme is robust against environmental thermal noise and system dissipation, ensuring its feasibility under current experimental conditions. This work may pave the way for the development of nonreciprocal quantum devices, such as isolators and circulators. Furthermore, the ability to achieve such robust mechanical squeezing has significant implications for advancing quantum precision measurements in metrology and sensing, offering new opportunities for exploring quantum-enhanced technologies.

The laser’s frequency noise is crucial to the sensitivity of quantum sensors. Two commonly used methods to suppress the laser’s frequency noise are locking the laser to an atomic transition by the lock-in technique or to an ultra-low thermal expansion (ULE) glass cavity by the PDH technique. The former cannot suppress rapidly changing frequency noise and hardly meets the needs; the latter has powerful performance but a heightened cost. The lack of high-performance and low-cost laser noise suppression methods dramatically limits the practical application of quantum sensors. This work demonstrates that, in many quantum sensing applications such as the Rydberg atomic superheterodyne receiver, by cascade locking the laser to both the atomic transition and a low-cost (LC) cavity, the same performance as locking to the ULE cavity can be achieved. This work is significant in promoting the practical application of quantum sensors.

By designing two-step transmissions, this paper presents two quantum dialogue (QD) protocols that can resist different types of collective noise in the quantum channel. The message carrier of the proposed scheme utilizes decoherence-free subspaces to remain invariant under the impact of collective noise. We employ combinations of these quantum states to form decoy photon pairs, ensuring secure transmission and preventing message distortion. Based on the principle that a single photon in an EPR pair reveals no information about its actual state, an EPR pair requires only one photon for protection during transmission. This property effectively reduces the number of decoy photons needed to ensure the security of quantum transmission, which can also be applied to the logical EPR pair consisting of logical qubits. A quantum logic circuit is also designed to demonstrate the practical implementation of shuffling the logical qubits within each logical EPR pair. Therefore, the proposed two-step QD protocols require only half as many decoy photons to achieve the same security level as other state-of-the-art QD schemes. The significant reduction in the utilization of decoy photons improves the qubit efficiency of the proposed QD protocols compared to other existing works in the field. Additionally, the security analysis of the proposed QD schemes ensures the absence of information leakage and resistance to common quantum attacks.

The growth of quantum technologies is attracting the interest of many students eager to learn concepts such as quantum entanglement or quantum superposition. However, the non-intuitive nature of these concepts poses a challenge to understanding them. Here, we present an entangled photon system which can perform a Bell test, i.e. the CHSH inequality, and can obtain the complete tomography of the two-photon state. The proposed setup is versatile, cost-effective and allows for multiple classroom operating modes. We present two variants, both facilitating the measurement of Bell inequalities and quantum state tomography. Experimental results showcase successful manipulation of the quantum state of the photons, achieving high-fidelity entangled states and significant violations of Bell’s inequalities. Our setup’s simplicity and affordability enhances accessibility for less specialized laboratories, allowing students to familiarize themselves with quantum physics concepts.

Recent research has demonstrated the potential of quantum algorithms to exploit vulnerabilities in various popular constructions, such as certain block ciphers like Feistel, Even-Mansour, and multiple MACs, within the superposition query model. In this study, we delve into the security of block ciphers against quantum threats, particularly investigating their susceptibility to cryptanalysis techniques, notably exploring quantum adaptations of differential cryptanalysis. Initially, we introduce a BV-based quantum algorithm for identifying linear structures with a complexity of (O(n)), where n denotes the number of bits in the function. Subsequently, we illustrate the application of this algorithm in devising quantum differential cryptanalysis techniques, including quantum differential cryptanalysis, quantum small probability differential cryptanalysis, and quantum impossible differential cryptanalysis, demonstrating polynomial acceleration compared to prior approaches. By treating the encryption function as a unified entity, our algorithm circumvents the traditional challenge of extending differential paths in differential cryptanalysis.

Coupled spins form composite quantum systems which play an important role in many quantum technology applications, with an essential task often being the efficient generation of entanglement between two constituent qubits. The simplest such system is a pair of spins-(1/2) coupled with Ising interaction, and in previous works various quantum control methods such as adiabatic processes, shortcuts to adiabaticity and optimal control have been employed to quickly generate there one of the maximally entangled Bell states. In this study, we use machine learning and optimization methods to produce maximally entangled states in minimum time, with the Rabi frequency and the detuning used as bounded control functions. We do not target a specific maximally entangled state, like the preceding studies, but rather find the controls which maximize the concurrence, leading thus automatically the system to the closest such state in shorter time. By increasing the bounds of the control functions we observe that the corresponding optimally selected maximally entangled state also changes and the necessary time to reach it is reduced. The present work demonstrates also that machine learning and optimization offer efficient and flexible techniques for the fast generation of entanglement in coupled spin systems, and we plan to extent it to systems involving more spins, for example spin chains.