EPJ Quantum Technology最新文献

The transition of second-generation quantum technologies from a research topic to a topic of industrial relevance has led to a growing number of quantum companies and businesses that are exploring quantum technologies. Examples would include a start-up building a quantum key distribution device, a large company working on integrating a quantum sensing core into a product, or a company providing quantum computing consultancy. They all face different challenges and needs in terms of building their quantum workforce and training in quantum concepts, technologies and how to derive value from them. With the study documented in this paper, we aim to identify these needs and provide a picture of the industry’s requirements in terms of workforce development and (external) training and materials. We discuss, for example, the shortage of engineers and jobs relevant to the quantum industry, the challenge of getting people interested in quantum, and the need for training at different levels and in different formats – from awareness raising and self-learning materials to university courses in quantum systems engineering. The findings are based on 34 semi-structured interviews with industry representatives and a follow-up questionnaire to validate some of the issues raised in the interviews. These results have influenced activities in EU projects, including an update of the European Competence Framework for Quantum Technologies.

A new method for efficient, high-quality randomness extraction is presented. The method relies on quantum processes such as the emission of single photons and their subsequent detection, where each detection event has an associated detection time. By establishing a list of time differences between a fixed number of events, a unique order can be established.

We note that, by utilising the number of ways to order the resulting list of time differences between the quantum events, the efficiency can be increased many-fold compared to current methods. The method delivers fundamentally uniform randomness and therefore, in principle, does not need debiasing.

The quantum blind millionaires’ (QBM) problem is an expanded version of the millionaires’ problem in a quantum environment. For any two sets with different members, the QBM problem represents the quantum solution of the private summation in each set and the private comparison of the results simultaneously. During it, the secrets of any participant should be protected. As a new topic in quantum secure multiparty computation (QSMC), current solutions to QBM problems usually require an honest third party to resist some potential attack strategies. However, the assumptions will affect their applicability in practical cooperative security systems. In this paper, we propose a new solution to the quantum blind millionaires’ (QBM) problem without the help of an honest third party for the first time. In our solution, the shift operations are applied to the d-dimensional 2-particle entangled states to encode the secrets of the participants. According to our analysis, the proposed solution can effectively resist typical internal and external attacks by applying the detection methods generated by the participants. We hope that the research will make positive developments for QSMC.

Ellipse fitting is a technique which is used to extract differential phase in cold-atom interferometers, particularly in situations where common-mode noise needs to be suppressed. We use numerical simulation to investigate errors in the ellipse fitting process; specifically, errors due to the presence of additive noise, linear drift in ellipse offset and amplitude, as well as an error that can arise from fringe normalisation. Errors are found to manifest in two ways: bias in the ellipse phase measurement and incomplete suppression of common mode phase noise. We quantify these errors for three different ellipse fitting algorithms and discuss the applicability of these results to future cold atom sensors.

A ready-to-use numerical model has been developed for the atomic ladder (cascade) systems which are widely exploited in Rydberg Radio Frequency (RF) sensors. The model has been explicitly designed for user convenience and to be extensible to arbitrary N-level non-thermal systems. The versatility and adaptability of the model is validated up to 4-level atomic systems by direct comparison with experimental results from the prior art. The numerical model provides a good approximation to the experimental results and provides experimentalists with a convenient ready-to-use model to optimise the operation of an N-level Rydberg RF sensor. Current sensors exploit the 4-level atomic systems based on alkali metal atoms which require visible frequency lasers and these can be expensive and also suffer from high attenuation within optical fiber. The ability to quickly and simply explore more complex N-level systems offers the potential to use cheaper and lower-loss near-infrared lasers.

While quantum key distribution (QKD) is a theoretically secure way of growing quantum-safe encryption keys, many practical implementations are challenged due to various open attack vectors, resulting in many variations of QKD protocols. Side channels are one such vector that allows a passive or active eavesdropper to obtain QKD information leaked through practical devices. This paper assesses the feasibility and implications of extracting the raw secret key from far-field radiated emissions from the single-photon avalanche diodes used in a BB84 QKD quad-detector receiver. Enhancement of the attack was also demonstrated through the use of deep-learning model to distinguish radiated emissions due to the four polarized encoding states. To evaluate the severity of such side-channel attack, multi-class classification based on raw-data and pre-processed data is implemented and assessed. Results show that classifiers based on both raw-data and pre-processed features can discern variations of the electromagnetic emissions caused by specific orientations of the detectors within the receiver with an accuracy higher than 90%. This research proposes machine learning models as a technique to assess EM information leakage risk of QKD and highlights the feasibility of side-channel attacks in the far-field region, further emphasizing the need to utilise mechanisms to avoid electromagnetic radiation information leaks and measurement-device-independent QKD protocols.

Quantum architecture Search (QAS) is a promising direction for optimization and automated design of quantum circuits towards quantum advantage. Recent techniques in QAS emphasize Multi-Layer Perceptron (MLP)-based deep Q-networks. However, their interpretability remains challenging due to the large number of learnable parameters and the complexities involved in selecting appropriate activation functions. In this work, to overcome these challenges, we utilize the Kolmogorov-Arnold Network (KAN) in the QAS algorithm, analyzing their efficiency in the task of quantum state preparation and quantum chemistry. In quantum state preparation, our results show that in a noiseless scenario, the probability of success is 2× to 5× higher than MLPs. In noisy environments, KAN outperforms MLPs in fidelity when approximating these states, showcasing its robustness against noise. In tackling quantum chemistry problems, we enhance the recently proposed QAS algorithm by integrating curriculum reinforcement learning with a KAN structure. This facilitates a more efficient design of parameterized quantum circuits by reducing the number of required 2-qubit gates and circuit depth. Further investigation reveals that KAN requires a significantly smaller number of learnable parameters compared to MLPs; however, the average time of executing each episode for KAN is higher.

Research and curriculum development on quantum information science is a novel but technologically and socially significant challenge for physics education. While the debate is open on the core content, the approaches, and the strategies for addressing the need of effective instruction on the subject-matter, some indications have begun to emerge. Among them, the importance of an earlier start of education and of helping students develop not only a theoretical knowledge, but also high-level experimental skills including ideal design and conduction of experiments. Such skills are challenging to attain in existing traditional programs and may be considered inaccessible at introductory level because of the difficulties connected with qubit implementations. Here we present the design process, the structure, and a preliminary evaluation of a course for secondary school that is aimed to promote the building of a basic but integrated understanding of quantum information science, including experimental design and lab activities. The course was developed within the model of educational reconstruction, and embedded into a conceptual change framework in physics and computation. The encoding of polarization and which-path information of a photon is used to engage students in the development of a global model of logical encoding and processing, in ideal experimental design of gates and circuits, and in their implementation on the optical bench. Data show the effectiveness of the course in promoting student engagement in the modelling of gates in different encodings, in fostering an understanding of the computational role of physical setups, and a positive attitude and interest towards quantum computation and innovative teaching methods.

Hexagonal boron nitride exhibits two types of defects with great potential for quantum information technologies: single-photon emitters (SPEs) and one-dimensional grain boundaries hosting topologically-protected phonons, termed as topologically-protected phonon lines (TPL). Here, by means of a simple effective model and density functional theory calculations, we show that it is possible to use these phonons for the transmission of information. Particularly, a single SPE can be used to induce single-, two- and qubit-phonon states in the one-dimensional channel, and (ii) two distant SPEs can be coupled by the TPL that acts as a waveguide, thus exhibiting strong quantum correlations. We highlight the possibilities offered by this material-built-in nano-architecture as a phononic device for quantum information technologies.

Material discovery is a phenomenon practiced since the evolution of the world. The discovery of materials has led to significant development in varied fields such as Science, Engineering and Technology. Computationally simulating molecules has been an area of interest in the industry as well as academia. However, simulating large molecules can be computationally expensive in terms of computing power and complexity. Quantum computing is a recent development that can improve the efficiency in predicting properties of atoms and molecules which will be useful for material design. The Variational Quantum Eigensolver (VQE) is one such quantum algorithm used to calculate the ground state energy of molecules or ions. In this study, we have done a comparative analysis of the parameters that constitute the VQE algorithm. This includes components such as basis, qubit mapping, ansatz, and optimizers used. We have also developed a database consisting of 79 single atoms and their variations of oxidation states and 33 molecules with the data of their Hamiltonian and ground state energy and dipole moment.