Hua Xu

With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest. Many quantum (convolutional) circuit ansaetze are proposed for grayscale images classification tasks with promising empirical results. However, when applying these ansaetze on RGB images, the intra-channel information that is useful for vision tasks is not extracted effectively. In this paper, we propose two types of quantum circuit ansaetze to simulate convolution operations on RGB images, which differ in the way how inter-channel and intra-channel information are extracted. To the best of our knowledge, this is the first work of a quantum convolutional circuit to deal with RGB images effectively, with a higher test accuracy compared to the purely classical CNNs. We also investigate the relationship between the size of quantum circuit ansatz and the learnability of the hybrid quantum-classical convolutional neural network. Through experiments based on CIFAR-10 and MNIST datasets, we demonstrate that a larger size of the quantum circuit ansatz improves predictive performance in multiclass classification tasks, providing useful insights for near term quantum algorithm developments.

Existing and near-term quantum computers can only perform two-qubit gates between physically connected qubits. Research has been done on compilers to rewrite quantum programs to match hardware constraints. However, the quantum processor architecture, in particular the qubit connectivity and topology, still lacks enough discussion, while it potentially has a huge impact on the performance of the quantum algorithms. We perform a quantitative and comprehensive study on the quantum processor performance under different qubit connectivity and topology. We select ten representative design models with different connectivities and topologies from quantum architecture design space and benchmark their performance by running a set of standard quantum algorithms. It is shown that a high-performance architecture almost always comes with a design with a large connectivity, while the topology shows a weak influence on the performance in our experiment. Different quantum algorithms show different dependence on quantum chip connectivity and topologies. This work provides quantum computing researchers with a systematic approach to evaluating their processor design.

A moderate chemical method [i.e., the reaction of diborane (B₂H₆) and a mixture gas of ammonia and nitrogen (NH₃/N₂) over nanoscale iron boride at 1100 °C] was developed to explore the boron nitride (BN) nanostructures. The products were well characterized by high-resolution electron microscopy and energy-dispersive x-ray spectroscopy. Two types of novel core–shell nanocapsules of amorphous boron core encapsulated in crystalline boron nitride shell were obtained. The first one looked like a peanut with an amorphous B core containing a trace of BN crystallites, a transition layer of BN nanofibers and amorphous B, and a thornlike shell of BN nanofibers. The second one looked like a perfect sphere consisting of a pure amorphous B core and a rather smooth crystalline BN shell. These results not only provided us a new chemical method for preparing BN nanostructures but also enriched the important BN nanostructures family. A growth mechanism is also briefly discussed.