EPJ Quantum Technology最新文献

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.

The practical deployment of Continuous Variables Quantum Key Distribution (CV-QKD) systems benefits from existing optical fiber telecommunication infrastructures. However, optical fibers introduce random variations in the state of polarization, which degrades the system’s performance. We consider a CV-QKD system featuring a polarization diversity heterodyne receiver and the constant modulus algorithm (CMA) to compensate for the polarization drifts in the quantum channel. Our setup can effectively realign Alice’s quantum signal with Bob’s local oscillator for polarization drift variances below 10⁻¹⁰. This value is compatible with most experimental implementations, allowing for accurate estimation of the channel transmission and excess noise parameters. Our results establish operational limits for passive polarization drift compensation using a polarization diversity receiver combined with digital CMA, validating its use to compensate for the polarization drift in real-world implementations approximating the ideal scenario of no polarization drift, for polarization drift variances below 10⁻¹⁰. This enables long-term stability in CV-QKD systems, eliminating the need for active polarization controllers and manual adjustments.

Design space exploration (DSE) plays an important role in optimising quantum circuit execution by systematically evaluating different configurations of compilation strategies and hardware settings. In this paper, we conduct a comprehensive investigation into the impact of various layout methods, qubit routing techniques, and optimisation levels, as well as device-specific properties such as different variants and strengths of noise and imperfections, the topological structure of qubits, connectivity densities, and back-end sizes. By spanning through these dimensions, we aim to understand the interplay between compilation choices and hardware characteristics. A key question driving our exploration is whether the optimal selection of device parameters, mapping techniques, comprising of initial layout strategies and routing heuristics can mitigate device induced errors beyond standard error mitigation approaches. Our results show that carefully selecting software strategies (e.g., mapping and routing algorithms) and tailoring hardware characteristics (such as minimising noise and leveraging topology and connectivity density) significantly improve the fidelity of circuit execution outcomes, and thus the expected correctness or success probability of the computational result. We provide estimates based on key metrics such as circuit depth, gate count and expected fidelity. Our results highlight the importance of hardware–software co-design, particularly as quantum systems scale to larger dimensions, and along the way towards fully error corrected quantum systems: Our study is based on computationally noisy simulations, but considers various implementations of quantum error correction (QEC) using the same approach as for other algorithms. The observed sensitivity of circuit fidelity to noise and connectivity suggests that co-design principles will be equally critical when integrating QEC in future systems. Our exploration provides practical guidelines for co-optimising physical mapping, qubit routing, and hardware configurations in realistic quantum computing scenarios.

Quantum Generative Adversarial Networks (QGANs), as a rising paradigm in Quantum Machine Learning, have shown promising potential in image generation and processing. However, their output quality remains suboptimal, and existing research is largely limited to small-scale, proof-of-concept studies. In this work, we propose a hybrid quantum-classical GAN architecture, where the generator integrates parameterized quantum circuits (PQCs) and classical neural networks. This integration significantly enhances the visual quality of generated images. Our model leverages the complementary strengths of quantum and classical components and outperforms existing methods (Tsang et al. in IEEE Trans. Quantum Eng. 4:1–19, 2023; Gulrajani et al. in Proceedings of the 31st international conference on neural information processing systems. NIPS’17, Red Hook, pp. 5769–5779, 2017), particularly in terms of image fidelity. Experiments conducted on the MNIST family of datasets show that our hybrid approach achieves a 20.26% average reduction in Fréchet Inception Distance. Furthermore, it improves the Structural Similarity Index Measure, Cosine Similarity, and Peak Signal-to-Noise Ratio by 26.04%, 2.22%, and 7.62%, respectively. These results highlight the effectiveness of combining quantum computing with machine learning, and underscore the potential of hybrid quantum-classical models in advancing generative tasks.

Tian et al. (EPJ Quantum Technol 11(1):35, 2024) proposed three semi-quantum summation protocols that enable resource-limited participants to securely compute the sum of their private inputs with assistance from a semi-honest third party (TP). In these protocols, participants do not need quantum measurement devices to achieve secure computation. Although the protocols have been analyzed against common attacks to demonstrate their robustness, this study identifies two significant issues in Tian et al.’s work: a security loophole and a design flaw. The security loophole permits a malicious participant to steal another participant’s private input without detection, while the design flaw prevents participants from obtaining the correct summation result when the number of participants is odd. To address these issues, this study proposes improved semi-quantum summation protocols under the same assumptions and environment.