Quantum secure direct communication (QSDC) is an important branch of quantum communication that is capable of directly transmitting secret messages over a quantum channel. It may be viewed as a concrete realization of Wyner’s wiretap channel theory, which ensures the reliable and secure communication of information in the presence of noise and eavesdropping. Hence it is a fully-fledged quantum-communications protocol, which does not require a separate secret key negotiation phase. By contrast, its quantum key distribution (QKD) counterpart represents a secret key-negotiation protocol, which has to be followed up by a separate classical communication session. The essential difference between these two modes of quantum communication lies in the employment of a block-based data transmission technique, proposed by Long and Liu in 2000. However, the original block-based data transmission requires quantum memory, which is not widely available at the time of writing. Recently, this difficulty has been overcome by using classical coding theory, which has been successfully applied to the single-qubit DL04 QSDC. Here we will present a single-photon-memory QSDC protocol based on entangled pairs of photons. We commence by comparing QSDC to QKD, followed by an example of the single-photon QSDC and single-photon QKD protocol. Then we continue by modifying the so-called two-step QSDC protocol designed for deterministic QKD by reducing the number of qubits in a block into a single one, in which Alice prepares Einstein-Podolsky-Rosen (EPR) photon pairs and partitions them into two parts: the so-called pioneer qubit and the follow-up qubit. The pioneer photon is transferred first to Bob, while the follow-up photon is used either for performing encoding or for eavesdropping detection. Bob extracts the candidate key by combining the two particles of the EPR pair to perform Bell-basis measurement. Then the protocol is transformed into a single-photon-memory QSDC using coding theory. Our theoretical analysis shows that the resultant protocol is robust to individual attacks. Additionally, a high communication efficiency is achieved.

This paper presented ultra-wideband (UWB) bandpass filters with single or multi notched band(s) for the purpose of "DAA: detect and avoid", to avoid the interference between the UWB radio system and the existing radio system, The filter is based on a novel UWB bandpass filter which consists a broadside-coupled microstrip-coplanar waveguide structure and exhibited its excellent ultra-wideband property, proposed by authors recently. The single and multi notched bands operation was implemented by integrating stub(s) in the broadside-coupled conductors. The resonance of each stub introduces a narrow rejection band in the UWB passband which then results in a single or multi notched band(s). Such UWB bandpass filter with notched band is useful and required in practical systems in order to avoid the interference between the UWB radio system and existing radio systems. The notched band can be easily designed to some specific frequency band(s) by tuning the length of the stub(s). Simulation, design, fabrication and measurement of the UWB bandpass filters with notched bands have been carried out mainly following FCC's spectrum mask. The proposed filters demonstrated both excellent ultra-wide bandwidth (from 2.8 GHz to 10.2 GHz) and rejection performance (> 50 dB at central frequencies for a single notched band, or 21 dB and 27 dB for triple bands), as well as good out-band performance better than the requirement of FCC's mask

The gradient descent method is central to numerical optimization and is the key ingredient in many machine learning algorithms. It promises to find a local minimum of a function by iteratively moving along the direction of the steepest descent. Since for high-dimensional problems the required computational resources can be prohibitive, it is desirable to investigate quantum versions of the gradient descent, such as the recently proposed (Rebentrost et al.¹). Here, we develop this protocol and implement it on a quantum processor with limited resources. A prototypical experiment is shown with a four-qubit nuclear magnetic resonance quantum processor, which demonstrates the iterative optimization process. Experimentally, the final point converged to the local minimum with a fidelity >94%, quantified via full-state tomography. Moreover, our method can be employed to a multidimensional scaling problem, showing the potential to outperform its classical counterparts. Considering the ongoing efforts in quantum information and data science, our work may provide a faster approach to solving high-dimensional optimization problems and a subroutine for future practical quantum computers.

This paper presented a dual-band ultra-wideband (UWB) bandpass filter. The filter is based on a novel UWB bandpass filter structure, proposed by authors recently, which consists of a broadside-coupled microstrip-coplanar waveguide and exhibited its excellent ultra-wideband property. The dual-band operation was implemented by integrating a stub in the broadside-coupled conductors. The resonance of the stub introduces a narrow rejection band in the UWB passband which then results in a dual-band filtering. Such a dual-band UWB bandpass filter is strongly required in practical systems in order to avoid the interference between the UWB radio system and existing radio systems. The rejection band can be easily designed to some specific frequency band(s) by tuning the length of the stub. Simulation, design, fabrication and measurement of dual-band UWB bandpass filters have been carried out mainly following FCC's spectrum mask. The proposed filters demonstrated both excellent ultra-wide bandwidth (from 2.8 GHz to 10.2 GHz) mid rejection performance (> 50 dB at rejection frequencies), as well as good out-band performance better than the requirement of FCC's mask

Gradient-based algorithms provide the fundamental tools for the optimizations and are the essential components of many modern technologies. Theoretically, the extreme points of a cost function can be found by iterative descent(ascent) of the variable along the direction of the gradient. However, the time complexity to calculate a gradient is at a level of $O(poly\left(d\right))$ for the $d$-dimension problems. For processing the high-dimension case, especially modern machine-learning engineering with the number of optimized parameters being in billions, quantum techniques are introduced. Via the dressed amplitude encoding and nonunitary module, a gradient-based quantum optimization algorithm for general polynomials is proposed, aiming at solving the fast-convergence problems with both time and memory complexity in $O(poly(logd))$. Furthermore, numerical simulations are carried out to inspect the performance of the newly born protocol by considering the noises or perturbations during the initialization, operations, and truncations. For the potential values in high-dimension optimizations, this gradient-based quantum optimization algorithm is supposed to facilitate the polynomial optimizations, being a subroutine for future practical quantum computers.