Scalable parameterized quantum circuits classifier.

As a generalized quantum machine learning model, parameterized quantum circuits (PQC) have been found to perform poorly in terms of classification accuracy and model scalability for multi-category classification tasks. To address this issue, we propose a scalable parameterized quantum circuits classifier (SPQCC), which performs per-channel PQC and combines the measurements as the output of the trainable parameters of the classifier. By minimizing the cross-entropy loss through optimizing the trainable parameters of PQC, SPQCC leads to a fast convergence of the classifier. The parallel execution of identical PQCs on different quantum machines with the same structure and scale reduces the complexity of classifier design. Classification simulations performed on the MNIST Dataset show that the accuracy of our proposed classifier far exceeds that of other quantum classification algorithms, achieving the state-of-the-art simulation result and surpassing/reaching classical classifiers with a considerable number of trainable parameters. Our classifier demonstrates excellent scalability and classification performance.

Recurrent quantum embedding neural network and its application in vulnerability detection.

In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and language. Since NLP usually consumes a lot of computing resources, its combination with quantum computing is becoming a valuable research direction. In this paper, we present a Recurrent Quantum Embedding Neural Network (RQENN) for vulnerability detection. It aims to reduce the memory consumption of classical models for vulnerability detection tasks and improve the performance of quantum natural language processing (QNLP) methods. We show that the performance of RQENN achieves the above goals. Compared with the classic model, the space complexity of each stage of its execution is exponentially reduced, and the number of parameters used and the number of bits consumed are significantly reduced. Compared with other QNLP methods, RQENN uses fewer qubit resources and achieves a 15.7% higher accuracy in vulnerability detection.

A general quantum algorithm for numerical integration.

Quantum algorithms have shown their superiority in many application fields. However, a general quantum algorithm for numerical integration, an indispensable tool for processing sophisticated science and engineering issues, is still missing. Here, we first proposed a quantum integration algorithm suitable for any continuous functions that can be approximated by polynomials. More impressively, the algorithm achieves quantum encoding of any integrable functions through polynomial approximation, then constructs a quantum oracle to mark the number of points in the integration area and finally converts the statistical results into the phase angle in the amplitude of the superposition state. The quantum algorithm introduced in this work exhibits quadratic acceleration over the classical integration algorithms by reducing computational complexity from O(N) to O(√N). Our work addresses the crucial impediments for improving the generality of quantum integration algorithm, which provides a meaningful guidance for expanding the superiority of quantum computing.

Fast reconstruction algorithm based on HMC sampling.

In Noisy Intermediate-Scale Quantum (NISQ) era, the scarcity of qubit resources has prevented many quantum algorithms from being implemented on quantum devices. Circuit cutting technology has greatly alleviated this problem, which allows us to run larger quantum circuits on real quantum machines with currently limited qubit resources at the cost of additional classical overhead. However, the classical overhead of circuit cutting grows exponentially with the number of cuts and qubits, and the excessive postprocessing overhead makes it difficult to apply circuit cutting to large scale circuits. In this paper, we propose a fast reconstruction algorithm based on Hamiltonian Monte Carlo (HMC) sampling, which samples the high probability solutions by Hamiltonian dynamics from state space with dimension growing exponentially with qubit. Our algorithm avoids excessive computation when reconstructing the original circuit probability distribution, and greatly reduces the circuit cutting post-processing overhead. The improvement is crucial for expanding of circuit cutting to a larger scale on NISQ devices.

EGFR-targeted hybrid lipid nanoparticles for chemo-photothermal therapy against colorectal cancer cells.

Antibody-functionalized targeted nanocarriers have shown great-potential for minimizing the chemoresistance and systemic toxicity of cancer chemotherapies. The combination of chemotherapy and photothermal therapy has great potential in improving therapeutic effect. However, cetuximab-modified nanoparticles based lipids for chemo-phototherapy of EGFR overexpressing colorectal carcinoma (CRC) have seldom been investigated. Hence, this study aimed to fabricate cetuximab-conjugated and near infrared (NIR) light-responsive hybrid lipid-polymer nanoparticles (abbreviated as Cet-CINPs) for targeted delivery of irinotecan. Cet-CINPs were prepared with copolymer PLGA and various lipids DSPE-PEG, DSPE-PEG-Mal, lecithin as carriers. Cetuximab was conjugated on the surface of nanoparticles to achieve targeting anti-tumor efficacy. Cet-CINPs were characterized in terms of morphology (spherical), size (119 nm), charge (-27.2 mV), drug entrapment efficiency (43.27 %), and antibody conjugation efficiency (70.87 %). Cet-CINPs showed preferable photothermal response, pH/NIR-triggered drug release behavior, enhanced cellular uptake and ROS level compared with free ICG and CINPs. Meanwhile, in vitro cytotoxicity assay showed that Cet-CINPs with NIR irradiation had a higher cytotoxicity against Lovo cells than non-targeted or non-NIR activated nanoparticles. The IC50 values of Cet-CINPs with NIR irradiation was 22.84 ± 1.11 µM for 24 h and 5.01 ± 1.06 µM for 48 h, respectively. These investigations demonstrate that Cet-CINPs with good tumor-targeting ability and enhanced antitumor activity, are a promising multifunctional nanoplatform for CRC therapy.