Advanced quantum technologies最新文献

The entropy source of a quantum random number generator (QRNG) is theoretically unpredictable, but in practice, imperfections in local oscillators and measuring devices introduce classical noise that inevitably contaminates the unpredictability of the quantum entropy source. This makes it a challenge for traditional methods to accurately estimate the min-entropy of QRNG, which in turn poses potential threats to the practical security of such systems. To address this issue, a novel min-entropy predictor based on deep neural networks is proposed to solve the min-entropy estimation problem for continuous-variable QRNG (CV-QRNG). First, the process of random number generation is systematically analyzed in CV-QRNG and the deviation between theoretical and practical randomness is discussed. Next, deep neural networks are used to construct a comprehensive min-entropy estimation strategy. Finally, extensive entropy estimation tests are conducted on various types of real-world quantum random number data. Experimental results show that the proposed predictor provides higher accuracy and reliability in min-entropy estimation for CV-QRNG, while also improving execution efficiency. In conclusion, the proposed predictor offers a simple and effective method for min-entropy estimation in CV-QRNG.

The Quantum Private Set Intersection (QPSI) protocol, combining quantum technology, enables efficient extraction of the public elements of private sets while preserving the privacy of all parties. As known, existing solutions are mostly designed for two-party scenarios or typically rely on an honest or semi-honest third party to perform the private set intersection. In fact, establishing such third party may bring risks of single-point failures in real scenarios. To address these issues, a Quantum Multi-Party Private Set Intersection (QMPSI) protocol without third party is proposed for the first time. In this protocol, the quantum state phase encoding is applied to compute the intersection of participants' private set. According to this analysis, it can be seen that the presented protocol conducts a comprehensive correctness and security analysis, and simulates the core processes using IBM Qiskit to verify its feasibility. The proposed approach successfully solves the private set intersection problem while serving as a fundamental module for solving other multi-party collaborative computation scenarios.

Classification using variational quantum circuits is a promising frontier in quantum machine learning. Quantum supervised learning (QSL) applied to classical data using variational quantum circuits involves embedding the data into a quantum Hilbert space and optimizing the circuit parameters to train the measurement process. In this context, the efficacy of QSL is inherently influenced by the selection of quantum embedding. In this study, a classical-quantum hybrid approach is introduced for optimizing quantum embedding beyond the limitations of the standard circuit model of quantum computation (i.e., completely positive and trace-preserving maps) for general multi-channel data. The performance of various models is benchmarked in this framework using the CIFAR-10 and Tiny ImageNet datasets and provide theoretical analyses that guide model design and optimization.

The development of quantum technology has opened up exciting opportunities to revolutionize computing and communication, timing and navigation systems, enable noninvasive imaging of the human body, and probe fundamental physics with unprecedented precision. Alongside these advancements has come an increase in experimental complexity and a correspondingly greater dependence on compact, efficient and reliable hardware. The drive to move quantum technologies from laboratory prototypes to portable, real-world instruments has incentivized miniaturization of experimental systems relating to a strong demand for smaller, more robust, and less power-hungry quantum hardware and for increasingly specialized and intricate components. Additive manufacturing, already heralded as game-changing for many manufacturing sectors, is especially well-suited to this task owing to the comparatively large amount of design freedom it enables and its ability to produce intricate 3D forms and specialized components. Herein we review work conducted to date on the application of additive manufacturing to quantum technologies, discuss the current state of the art in additive manufacturing in optics, optomechanics, magnetic components and vacuum equipment, and consider pathways for future advancement. We also give an overview of the research and application areas most likely to be impacted by the deployment of additive manufacturing techniques within the quantum technology sector.

The high-dimensional Toffoli gate functions in a broader Hilbert space, which empowers it to convey and handle greater amounts of information through parallel quantum pathways. In this study, a scheme of 3-qudit $��4�\times �4�\times �4�$-D Toffoli gate in the QD cavity-coupling system with high fidelity is proposed. By modulating Purcell factor, the balanced condition $��r_0�=�-�r�$ is obtained so that the error induced by whether there is interaction between the QDs and cavities or not can be neglected. The scheme features a module circuit design, which not only ensures higher construction feasibility but also helps save resources. Meanwhile, it possesses technical scalability, enabling effective promotion to the application scenarios of n-qudit systems, which reduces the steps required to manipulate quantum states and makes it simpler to construct quantum computing circuits.

Ion traps are a promising architecture to host a future quantum computer. Several challenges, such as signal-routing, power dissipation, and fabrication quality, need to be overcome to scale ion trap devices to hundreds of ions. Currently, ion traps are often fabricated on silicon substrates which result in high power dissipation. Substrates that lead to lower power dissipation are preferred. In this work, a multi-metal layer ion trap is presented on a fused silica substrate that is fabricated and tested in an industrial facility. Its design and material-stack are tailored to minimize power dissipation. Furthermore, the integrated temperature sensors are characterized and functionality down to 10 K is verified. Moreover, an automated wafer test is demonstrated to validate each trap chip prior to its integration into experimental setups. Subsequently, electric field noise and electric stray fields are characterized using a single trapped-ion as a probe, showing an improvement in trap performance over similar trap designs realized on silicon substrates.

In this paper, the effect of multi-photon subtraction operations in a feedback-assisted interferometer can enhance measurement precision for single-parameter and two-parameter estimation is analyzed under both ideal and photon-loss conditions. The effects of the feedback strength $�R$, the optical parametric amplifier's gain $�g$, the coherent state amplitude $�\alpha$, and the order of multi-photon subtraction on system performance are examined. It is demonstrated that an optimal feedback strength $�R_{�o�p�t�}$ exists in both conditions. Selecting a suitable $�R$ can significantly boost the system's robustness to photon loss, and markedly improve measurement precision. And the photon subtraction operations within a feedback-assisted interferometer can further enhance measurement precision effectively. Additionally, it is found that increasing intramode correlations while decreasing intermode correlations can improve estimation accuracy. This work investigates a new method through the synergistic integration of feedback topology and non-Gaussian operations into a multiparameter estimation system, along with their systematic study under both ideal and loss conditions. The findings may contribute to improving quantum-enhanced measurements and hold promise for high-precision quantum sensing research.

Layout optimization problems involve finding the optimal arrangement of elements in order to maximize efficiency. For instance, the wind farm layout optimization (WFLO) problem consists of the best turbine placement to maximize energy production while minimizing wake losses. As its nonlinear and combinatorial nature makes it challenging for traditional optimization methods, alternative approaches such as quantum annealing and quantum-classical hybrid methods offer a promising alternative for tackling such complex problems. Here, WFLO is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem using the Jensen wake model. A quantum annealer is compared, the Gurobi solver, and the Quantum Approximate Optimization Algorithm (QAOA). The quantum annealer provides solutions one order of magnitude faster than Gurobi with at most 3% lower power output, making it suitable for rapid suboptimal approximations. These findings highlight the trade-off between the quality of the solution and the computational time and demonstrate how quantum methods, especially when combined with classical solvers, can contribute to efficient renewable energy optimization.

A scheme is proposed to achieve ground state cooling in a dual-cavity optomechanical system. By solving the optimal coupling conditions in both red-detuned and blue-detuned regimes, it is demonstrated that ground state cooling is achievable over a wide range of parameters. The quantum interference effect induced by the cavity-cavity coupling plays a crucial role in suppressing the heating process and enhancing the cooling process, enabling ground state cooling of the mechanical resonator even in the blue-detuned regime. Furthermore, the SSH model is studied with next-nearest-neighbor (NNN) coupling and it is found that the NNN coupling not only induces the edge state but also enables ground state cooling of the mechanical resonator. The scheme provides new insights into the connection between optomechanical systems and topological models, paving the way for further exploration of their synergistic effects.

Achieving high-fidelity quantum teleportation requires the distribution of maximally entangled states through noiseless channels, a condition rarely met in practical implementations. In realistic environments, decoherence processes, such as amplitude damping, severely degrade entanglement quality. In this study, a universal strategy is proposed for protecting entanglement against amplitude damping noise by introducing escort qubits—ancillary qubits specifically designed to assist in the distribution process. This method involves CNOT operations and measurement of the escort qubits. Modeling the amplitude damping channel with a Lindblad master equation, it is showed analytically that the method achieves unit fidelity under ideal operations, independent of the damping rate, with a certain success probability. This method is applied to teleportation with Bell and W states, and compare its performance with weak measurement and environment-assisted measurement protocols. This approach offers superior fidelity, experimental feasibility, and independence from noise parameters, as confirmed through simulation and Qiskit implementation.