Guoqiang Shu

Limited by the decoherence of qubits as well as the errors of quantum gates, near-term superconducting quantum computers can only run low-depth quantum circuits to achieve acceptable fidelity. One possible way to overcome these limitations is to construct quantum circuits with additional high-fidelity expressive multi-qubit gates. Recently, a new three-qubit gate, denoted as Controlled-CPHASE-SWAP (CCZS), has been implemented through simultaneous Controlled-Z (CZ) gates. The CCZS gate takes less time than a single CZ gate and can be implemented at the coherence limit. However, how to use the CCZS gate in quantum circuit synthesis remains unexplored. In this paper, we construct the quantum fan-out/parity gates, the controlled-phase gate and the locally fully connected CZ gates with the CCZS gate, respectively. Furthermore, applications of the CCZS gate in quantum error correction, quantum Fourier transform and quantum approximate optimization algorithm are also proposed. We evaluate the performance of the CCZS gate in quantum circuit synthesis through simulation and explore its potential advantages over CZ gates.

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.

Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.