EPJ Quantum Technology最新文献

Quantum technologies and computing are an emerging area which offers a new paradigm to solve complex problems using the principles of quantum mechanics, where classical computing faces limits. Due to the advantages of quantum computers, today, there are several industries focusing on different aspects of quantum technologies based on their physics to explore the most efficient and useful platform for implementing applications. Since the scope of the quantum companies is diverse, it is important to understand the education, skills, and qualifications required for different job roles, as this will aid global educational institutions in constructing concentrated disciplines in this field. This paper provides a detailed critical analysis of different job descriptions for education, skills and qualifications. Most of the qubit modalities, such as superconducting, semiconducting, topological, nitrogen-vacancy centres, ion-traps, neutral atoms, and photonics, have been covered. Additionally, quantum software domains such as quantum machine learning, cryptography and error corrections have been discussed with fields such as quantum sensors and metrology. Finally, based on the patterns, recommendations are given to enable better preparation of skills and infrastructure for educational institutes and individuals who would like to pursue a career in the field of quantum technologies.

Quantum key distribution (QKD) promises theoretically secure communication. However, it encounters challenges in implementation security and performance due to inevitable device imperfections. Since the proposal of measurement-device-independent (MDI) QKD, the critical step toward practical security is to secure QKD with imperfect sources. The source imperfections manifest as state-preparation uncertainty (SPU) in various aspects, e.g., encoding uncertainty, intensity fluctuation, and imperfect vacuum states. Here, we perform an MDI-QKD experiment and achieve both high practical security and superior performance. We address the general form of SPU and guarantee a tight estimation of the secret key rate based on the operator dominance method. We achieve secure key distribution over 303.37 km, which not only represents the farthest distance in experiments involving SPU but also considers the most SPU scenarios. Our experimental results represent a significant step toward promoting practical and secure quantum communication.

Superconducting quantum computing has emerged as a leading platform in the pursuit of practical quantum computers, driven by rapid advances from industry, academia, and government initiatives. This review examines the state of superconducting quantum technology, with emphasis on qubit design, processor architecture, scalability, and supporting quantum software. We compare the hardware strategies and performance milestones of key players—including IBM Quantum, Google Quantum AI, Rigetti Computing, Intel Quantum, QuTech, and Oxford Quantum Circuits—highlighting innovations in qubit coherence, control, and system integration. Landmark demonstrations such as quantum supremacy experiments are discussed alongside progress toward real-world applications in the noisy intermediate-scale quantum (NISQ) era. Beyond hardware, attention is given to the broader software and service ecosystem, including quantum programming frameworks, operating environments, and cloud-accessible platforms such as Amazon Braket, Azure Quantum, and OriginQ Cloud, which enable remote access and algorithm development. Persistent challenges in superconducting quantum computing—such as error correction, system stability, and large-scale integration—are assessed in light of emerging approaches aimed at fault-tolerant quantum computing. As the field moves from the NISQ era toward fault-tolerant quantum computing, we capture the defining hardware achievements and characteristics of current superconducting processors, while examining the ongoing efforts and challenges in overcoming NISQ-era limitations. These developments offer critical insights into the path toward scalable quantum systems and their transformative impact on future technologies, while also underscoring the strategic and societal considerations that require balancing innovation with responsible oversight and thoughtful governance.

We present a design for a multi-channel optically pumped zero-field magnetometer utilizing a 200-μm-thick Rubidium vapor cell. The vapor cell and its housing are designed to reduce the minimal distance between a magnetic sample and the sensing volume to about 1 mm, to optimize the effective spatial resolution. The thin vapor cell, filled with 2 atm of nitrogen as a buffer gas reduces the volume across which the magnetic field is averaged. The vapor cell is fully illuminated by a single laser beam, and the transmitted light is imaged onto a 4 x 4 photodiode array, allowing for simultaneous measurement of a magnetic field distribution with up to 16 channels. The performance of the magnetometer is studied for all channels. It is shown that the sensor can operate in the spin-exchange relaxation-free regime with a projected photon-shot noise limited noise floor of about 1 pT/Hz^1/2 for a sensitive voxel size of approximately 600 μm x 600 μm x 200 μm.

The bin packing problem (BPP), a classical NP-hard combinatorial optimization challenge, has emerged as a promising application for quantum computing. In this work, we tackle the one-dimensional BPP (1dBPP) using a digitized counterdiabatic quantum approximate optimization algorithm (DC-QAOA) that incorporates counterdiabatic (CD) driving to achieve a 40% higher feasibility ratio than standard QAOA, while reducing quantum resource requirements. We investigate three ansatz schemes -DC-QAOA, CD-inspired ansatz, and CD-mixer ansatz - each integrating CD terms with distinct combinations of cost and mixer Hamiltonians, resulting in different DC-QAOA variants. Numerical simulations demonstrate that these DC-QAOA variants maintain solution accuracy with less than 5% variance across varying iteration numbers, circuit depths, and Hamiltonian step sizes. Moreover, they require approximately 7 to 8 times fewer measurements to achieve comparable precision under the same parameter variations. Experimental validation on a 10-item 1dBPP instance using IBM quantum computers shows the CD-mixer ansatz achieves five times more feasibility solutions and greater robustness against NISQ noise. Collectively, these results establish DC-QAOA as a resource-efficient framework for combinatorial optimization on near-term quantum devices.

This study proposes the first multiparty-to-multiparty mediated quantum secret sharing (M2M-MQSS) protocol within a restricted quantum environment. Unlike existing fully quantum secret sharing (QSS) protocols, this protocol allows protocol participants with limited quantum capabilities—including (1) measuring a single qubit in the Z-basis and (2) performing a single-qubit unitary operation, Hadamard operation—to participate, significantly reducing implementation costs. By employing one-way qubit transmission, the proposed MMQSS protocol not only simplifies the quantum communication process but also effectively defends against quantum Trojan horse attacks. The correctness and security analyses demonstrate that the proposed M2M-MQSS protocol is robust against various well-known attack strategies. Simulation experiments confirm the feasibility of the protocol for various numbers of participants. It maintains high levels of efficiency and security even as the number of participants increases. Moreover, compared with existing protocols, the proposed M2M-MQSS protocol lowers the barrier to practical quantum communication deployment by reducing the quantum resources required for protocol participants.

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm that shows promise in efficiently solving the Max-Cut problem, a representative example of combinatorial optimization. However, its effectiveness heavily depends on the parameter optimization pipeline, where the parameter initialization strategy is nontrivial due to the non-convex and complex optimization landscapes characterized by issues with low-quality local minima. Recent inspiration comes from the diffusion of classical neural network parameters, which has demonstrated that neural network training can benefit from generating good initial parameters through diffusion models. However, whether the diffusion model can enhance the parameter optimization and performance of QAOA by generating well-performing initial parameters is still an open topic. Therefore, in this work, we formulate the problem of finding good initial parameters as a generative task and propose the initial parameter generation scheme through dataset-conditioned pre-trained parameter sampling. Concretely, the generative machine learning model, specifically the denoising diffusion probabilistic model (DDPM), is trained to learn the distribution of pre-trained parameters conditioned on the graph dataset. Intuitively, the proposed framework aims to effectively distill knowledge from pre-trained parameters to generate well-performing initial parameters for QAOA. To benchmark our framework, we adopt trotterized quantum annealing (TQA)-based and graph neural network (GNN) prediction-based initialization protocols as baselines. Through numerical experiments on Max-Cut problem instances of various sizes, we show that conditional DDPM can consistently generate high-quality initial parameters, improve convergence to the approximation ratio, and exhibit greater robustness against local minima over baselines. Additionally, the experimental results also indicate that the conditional DDPM trained on small problem instances can be extrapolated to larger ones, thus demonstrating the extrapolation capacity of our framework in terms of the qubit number.

Leveraging the properties of quantum entanglement and squeezing, quantum clock synchronization offers significant advantages in improving precision and security. For scalable quantum clock synchronization networks, developing an accurate time deviation analysis model is essential to characterize long-term timing stability and enable reliable deployment in real-world systems. This paper proposes a synchronization stability analysis model that establishes the theoretically achievable time deviation based on the Cramér-Rao lower bound. We experimentally validate this model using a round-trip quantum clock synchronization protocol over 50 km of fiber, employing an integrated silicon-photonic chip that generates frequency-entangled photon pairs via four-wave mixing. Results show a synchronization accuracy of 15.08 ps and a time deviation of 901 fs at an averaging time of 10,240 seconds, while our model analysis shows a standard deviation of 12.21 ps. This work provides a fundamental framework for building robust, large-scale quantum networks.

The growing public fascination with quantum technologies has inadvertently fueled the rise of pseudoscientific claims, particularly the misuse of quantum terminology in fields such as alternative medicine. This phenomenon poses a challenge for physics education, where the distinction between legitimate science and pseudoscience is essential. This paper examines how pre-service physics teachers (N = 28) respond to pseudoscientific uses of quantum terminology, particularly in the context of quantum healing. Therefore, the participants were asked to evaluate a pseudoscientific text about quantum healing in a classroom-like vignette, responding as if they were addressing a student. Their responses were analyzed using qualitative content analysis to categorize the types of reasoning used. Most participants were successful in identifying scientific inaccuracies and misuse of technical terms, although only a proportion applied broader Nature of Science (NOS)-related critiques. The findings suggest that although pre-service teachers are adept at identifying pseudoscientific claims, more emphasis on the principles of NOS could improve their ability to make comprehensive judgements.

High-dimensional (HD) quantum systems are capable of processing more complex information and performing a wider array of quantum operations, in contrast to low-dimensional (LD) quantum systems, thereby improving the speed and fault tolerance of quantum computing. In the study, we propose a deterministic qudit-encoded (4times 4times 4)-dimensional (64D) controlled-controlled-SUM (CCSUM) gate based on weak Kerr effect. The 64D CCSUM gate leverages the polarization and spatial degrees of freedom (DoFs) of three photons to encode 4D control and target qudits, thereby optimizing quantum resources and reducing costs. Moreover, the HD CCSUM gate functionality is implemented in a deterministic way by employing the X-Homodyne detector to measure coherent states, combined with related classical feed-forward operations. Through detailed analyses, the proposed HD CCSUM gate, under current technological conditions, exhibits robust fidelity and feasibility, offering a promising path toward the realization of HD quantum computing.