Browsing by Author "Tang, Ho Lun"

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    Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer
    Zhu, Linghua; Tang, Ho Lun; Barron, George S.; Calderon-Vargas, F. A.; Mayhall, Nicholas J.; Barnes, Edwin Fleming; Economou, Sophia E. (American Physical Society, 2022-07-11)
    The quantum approximate optimization algorithm (QAOA) is a hybrid variational quantum-classical algorithm that solves combinatorial optimization problems. While there is evidence suggesting that the fixed form of the standard QAOA Ansatz is not optimal, there is no systematic approach for finding better Ansatze. We address this problem by developing an iterative version of QAOA that is problem tailored, and which can also be adapted to specific hardware constraints. We simulate the algorithm on a class of Max-Cut graph problems and show that it converges much faster than the standard QAOA, while simultaneously reducing the required number of CNOT gates and optimization parameters. We provide evidence that this speedup is connected to the concept of shortcuts to adiabaticity.
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    Designing Adaptive Ansätze in Quantum Simulation and Geometric Entangling Gates
    Tang, Ho Lun (Virginia Tech, 2024-06-25)
    Quantum Computation has attracted massive interest because of the recent technological advancement in both hardware and software suggesting the potential of quantum advantage. On the software side, hybrid classical-quantum algorithms are extensively studied as they can be implemented on the current noisy intermediate-scale quantum devices. On the hardware side, researchers are striving for faster and more noise-robustness quantum operations to achieve higher quantum processing power. The dissertation presents two topics in the above-mentioned aspects. The first one is constructing adaptive ans"atze for variational quantum eigensolver, one of the most promising hybrid algorithms. We present how to compress different required quantum resources by designing different ans"atze. The second topic is about designing fast entangling gates with a geometric approach. We show that the geometric approach can improve the existing numerical methods by locating the good initial guesses.