论文标题
混合量子编程的有效参数化汇编
Efficient parameterised compilation for hybrid quantum programming
论文作者
论文摘要
近期量子设备具有通过使用混合经典量子算法（如变异量子特征层）来胜过经典计算的潜力。这些迭代算法使用经典优化器来更新参数化的量子电路。每次迭代，电路都在物理量子处理器或量子计算模拟器上执行，并且平均测量结果将传递回经典优化器。当需要许多迭代时，整个量子程序也会重新编译多次。我们已经实施了明确的参数，以防止量子编程框架OpenQL（称为OpenQL_PC）重新编译整个程序，以改善编译，从而改善混合算法的总运行时间。我们将OpenQL中MaxCut算法的汇编和模拟所需的时间与Pyquil和Qiskit中的同一算法进行了比较。使用新参数，对于MaxCut基准，OpenQL中的汇编时间大大减少。使用OpenQL_PC时，混合算法的汇编的速度比使用Pyquil或Qiskit时要快两倍。
Near term quantum devices have the potential to outperform classical computing through the use of hybrid classical-quantum algorithms such as Variational Quantum Eigensolvers. These iterative algorithms use a classical optimiser to update a parameterised quantum circuit. Each iteration, the circuit is executed on a physical quantum processor or quantum computing simulator, and the average measurement result is passed back to the classical optimiser. When many iterations are required, the whole quantum program is also recompiled many times. We have implemented explicit parameters that prevent recompilation of the whole program in quantum programming framework OpenQL, called OpenQL_PC, to improve the compilation and therefore total run-time of hybrid algorithms. We compare the time required for compilation and simulation of the MAXCUT algorithm in OpenQL to the same algorithm in both PyQuil and Qiskit. With the new parameters, compilation time in OpenQL is reduced considerably for the MAXCUT benchmark. When using OpenQL_PC, compilation of hybrid algorithms is up to two times faster than when using PyQuil or Qiskit.
