EPJ Quantum Technology最新文献

Time-bin encoding of quantum information is highly advantageous for long-distance quantum communication protocols over optical fibres due to its inherent robustness in the channel and the possibility of generating high-dimensional quantum states. The most common implementation of time-bin quantum states using unbalanced interferometers presents challenges in terms of stability and flexibility of operation. In particular, a limited number of states can be generated without modifying the optical scheme. Here we present the implementation of a fully controllable arbitrary time-bin quantum state encoder, which is easily scalable to arbitrary dimensions and time-bin separations. The encoder presents high stability and low quantum bit error rate (QBER) for all the tested repetition rates and time-bin separations, without requiring hardware modifications. We further demonstrate phase randomisation and phase encoding without the need for additional resources.

This study presents the design and implementation of a university course for physics teacher training with a focus on quantum technologies and quantum information science. The course aims to equip prospective teachers with the knowledge and skills to teach quantum physics at upper secondary level and provide their students with insights into the professional world of quantum technologies. To this end, the course comprises three parts: a theoretical seminar at university, a didactic internship at quantum technology companies and a transfer phase implementing corresponding teaching units at school. The course was developed and implemented in several cycles within the framework of design-based research. The effect of the course was evaluated by an exploratory interview study. The focus was on its impact on prospective teachers’ attitudes and knowledge concerning quantum technologies, as well as on the implementation of career-oriented teaching units in the classroom. The results show that the internship was considered interesting and motivating by the prospective teachers, who valued the opportunity to gain insights into professional fields. However, transferring the content and experiences from the internship into actual school practice proved to be challenging. This process was perceived as difficult and required extensive support from university supervisors. From the results it can be concluded that the approach achieves its aims of providing prospective teachers with an introduction to quantum technologies and related professions and of stimulating students interest in the subject.

A hybrid optomechanical system incorporating a two-level atom is utilized to realize phonon blockade, with the large-detuned optical mode decoupled from the system via adiabatic approximation. This process establishes an exchange interaction between the two-level atom and the mechanical mode. The findings indicate that both conventional and unconventional phonon blockade mechanisms coexist within the system. By tuning the driving frequency of the two-level atom, the system can be dynamically switched between these two mechanisms. The phonon blockade effect exhibits remarkable robustness against optical dissipation due to the adiabatic elimination of the cavity mode, enabling its realization even in low-Q cavities. In this study, the two-level atom serves not only as a critical component for achieving phonon blockade but also as an additional tool for manipulating the blockade mechanism. Our results provide a promising framework for hybrid optomechanical systems aiming to achieve switchable and robust single-phonon sources.

Efficient adiabatic control schemes, where one steers a quantum system along an adiabatic path ensuring minimal excitations while achieving a desired final state, that enable fast, high-fidelity operations are essential for any practical quantum computation. However, current optimization protocols are not universally tractable due to stringent requirements imposed by the microscopic systems encoding the qubit, including complex energy level structures and unwanted transitions, and generally require a trade-off between speed and fidelity of the operation. Here, we address these challenges by developing a general framework for optimal control based on the quantum metric tensor. This framework allows for fast and high-fidelity adiabatic pulses, even for a dense energy spectrum, based solely on the Hamiltonian of the system instead of the time evolution propagator and independent of the size of the underlying Hilbert space. Furthermore, our framework suppresses diabatic transitions and state-dependent crosstalk effects without the need for additional control fields. As an example, we study the adiabatic charge transfer in a double quantum dot to find optimal control pulses with improved performance. We show that for the geometric protocol, the transfer fidelities are lower bounded (mathcal{F}>99%) for ultrafast (20~mbox{ns}) pulses, regardless of the size of the anti-crossing, while being robust against miscalibration errors and quasistatic noise.

Superconducting quantum computers require microwave control lines running from room temperature to the mixing chamber of a dilution refrigerator. Adding more lines without preliminary thermal modeling to make predictions risks overwhelming the cooling power at each thermal stage. In this paper, we investigate the thermal load of SC-086/50-SCN-CN semi-rigid coaxial cable, which is commonly used for the control and readout lines of a superconducting quantum computer, as we increase the number of lines to a quantum processor. We investigate the makeup of the coaxial cables, verify the materials and dimensions, and experimentally measure the total thermal conductivity of a single cable as a function of the temperature from cryogenic to room temperature values. We also measure the cryogenic DC electrical resistance of the inner conductor as a function of temperature, allowing for the calculation of active thermal loads due to Ohmic heating. Fitting this data produces a numerical thermal conductivity function used to calculate the static heat loads due to thermal transfer within the wires resulting from a temperature gradient. The resistivity data is used to calculate active heat loads, and we use these fits in a cryogenic model of a superconducting quantum processor in a typical Bluefors XLD1000-SL dilution refrigerator, investigating how the thermal load increases with processor sizes ranging from 100 to 225 qubits. We conclude that the theoretical upper limit of the described architecture is approximately 200 qubits. However, including an engineering margin in the cooling power and the available space for microwave readout circuitry at the mixing chamber, the practical limit is approximately 140 qubits.

The efficient optimization of variational quantum algorithms (VQAs) is critical for their successful application in quantum computing. The Quantum Natural Gradient (QNG) method, which leverages the geometry of quantum state space, has demonstrated improved convergence compared to standard gradient descent (Stokes et al. in Quantum 4:269, 2020). In this work, we introduce the Modified Conjugate Quantum Natural Gradient (CQNG), an optimization algorithm that integrates QNG with principles from the nonlinear conjugate-gradient method. Unlike QNG, which employs a fixed learning rate, CQNG dynamically adjusts its hyperparameters at each step, enhancing both efficiency and flexibility. Numerical simulations show that CQNG achieves faster convergence and reduces quantum-resource requirements compared to QNG across various optimization scenarios, even when strict conjugacy conditions are not fully satisfied—hence the term “Modified Conjugate.” These results highlight CQNG as a promising optimization technique for improving the performance of VQAs.

Frequency-modulation schemes offer an alternative to standard Rabi pulses for realizing robust quantum operations. In this work, we investigate short-duration population transfer between the ground and first excited states of a ladder-type qutrit, with the goal of minimizing leakage into the second excited state. Our multiobjective approach seeks to reduce the maximum transient second-state population and maximize detuning robustness. Inspired by two-state models—such as the Allen-Eberly and Hioe-Carroll models—we extend these concepts to our system, exploring a range of pulse families, including those with super-Gaussian envelopes and polynomial detuning functions. We identify Pareto fronts for pulse models constructed from one of two envelope functions paired with one of four detuning functions. We then analyze how each Pareto-optimal pulse parameter influences the two Pareto objectives as well as amplitude robustness.

Quantum Physics is a cornerstone of modern science and technology, yet a comprehensive approach to integrating it into school curricula and communicating its foundations to policymakers, industrial stakeholders, and the general public has yet to be established. In this paper, we discuss the rationale for introducing entanglement and Bell’s Inequalities (BI) to a non-expert audience, and how these topics have been presented in the exhibition “Dire l’indicibile” (“Speaking the unspeakable”), as a part of the Italian Quantum Weeks project. Our approach meets the challenge of simplifying quantum concepts without sacrificing their core meaning, specifically avoiding the risks of oversimplification and inaccuracy. Through interactive activities, including a card game demonstration and the staging of CHSH experiments, participants explore the fundamental differences between classical and quantum probabilistic predictions. They gain insights into the significance of BI verification experiments and the implications of the 2022 Nobel Prize in Physics. Preliminary results from both informal and formal assessment sessions are encouraging, suggesting the effectiveness of this approach.

Protein-ligand binding affinity is critical in drug discovery, but experimentally determining it is time-consuming and expensive. Artificial intelligence (AI) has been used to predict binding affinity, significantly accelerating this process. However, the high-performance requirements and vast datasets involved in affinity prediction demand increasingly large AI models, requiring substantial computational resources and training time. Quantum machine learning has emerged as a promising solution to these challenges. In particular, hybrid quantum-classical models can reduce the number of parameters while maintaining or improving performance compared to classical counterparts. Despite these advantages, challenges persist: why hybrid quantum models achieve these benefits, whether quantum neural networks (QNNs) can replace classical neural networks, and whether such models are feasible on noisy intermediate-scale quantum (NISQ) devices. This study addresses these challenges by proposing a hybrid quantum neural network (HQNN) that empirically demonstrates the capability to approximate non-linear functions in the latent feature space derived from classical embedding. The primary goal of this study is to achieve a parameter-efficient model in binding affinity prediction while ensuring feasibility on NISQ devices. Numerical results indicate that HQNN achieves comparable or superior performance and parameter efficiency compared to classical neural networks, underscoring its potential as a viable replacement. This study highlights the potential of hybrid QML in computational drug discovery, offering insights into its applicability and advantages in addressing the computational challenges of protein-ligand binding affinity prediction.

Polynomial multiplication is a fundamental operation in various fields of science and engineering. This paper proposes a quantum algorithm for polynomial multiplication that achieves improved efficiency over classical approaches. The core innovation is the use of a quantum Fourier transform with digital encoding. The practical utility and versatility of this algorithm are highlighted through its application to several related computational problems, including string matching, Toeplitz matrix-vector multiplication, and matrix decomposition algorithm. Furthermore, an enhanced version of the quantum polynomial multiplication algorithm is introduced, offering improvements in both execution process and time complexity.