Advanced quantum technologies最新文献

In article number 2500069, Yong-Hoon Cho, Sven Höfling, Andreas T. Pfenning, and co-workers explore single photons in the telecom C-band emitted from a quantum dot embedded in a circular Bragg grating (bottom).

These photons interfere at a beam splitter (left) via the Hong-Ou-Mandel effect, revealing their indistinguishability.

Rare earth doped crystals show, at low temperatures, extremely narrow optical homogeneous linewidths, as well as long spin coherence lifetimes, a unique combination in the solid state. This makes these materials attractive for optical quantum technologies like quantum communication and processing. Most of the results in this field have so far used bulk crystals because of their exceptional spectroscopic properties. Crystalline thin films can combine these properties with the possibilities offered by integration in photonic circuits in terms of compactness, stability, energy efficiency, and scalability.

In this review, recent results on different platforms containing rare earth ions and targeting quantum technologies, including lithium niobate and silicon films, and oxide films deposited on Si, are summarized. Current approaches for obtaining thin films and devices are described, together with RE spectroscopic properties and applications to quantum technologies. The opportunities and challenges offered or faced by the different platforms are also discussed.

Dipole–dipole interaction (DDI) possesses characteristics different from the conventional isotropic s-wave interaction in Bose-Einstein condensates (BECs). The interplay of DDI with spin-orbit coupling (SOC) and rotation may induce novel quantum properties. This work systematically analyzes the effects of the DDI, Weyl-like SOC, rotation and trap anharmonicity in the ground state of two-component BECs. The interplay of these factors leads to a kaleidoscope of quantum states, emerging in quantum defects, droplets, wheel and ring forms of density distributions, accompanied with transitions of topology of density, and manifesting a critical behavior around a revealed density-collapse point. A bunch of exotic spin topological structures are shown, including centric vortex surrounded by layers of spin flows, compound topological structure of edge defect, and various coexistence states of skyrmions with different topological charges. In particular, quarter skyrmions and other possible fractional skyrmions are found. Rashba-type SOC and Weyl-like SOC are also compared. The study implies that one can manipulate both the density topology and the spin topological structure via these tunable parameters in BECs. The abundant variations of the topological structures and particularly the revealed critical behavior may provide quantum resources for potential applications in quantum metrology.

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.