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.

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.

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.

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.

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.