Optimization of Hybrid Quantum-Classical Algorithms

TL;DR

This work addresses the optimization of real-time hybrid quantum–classical programs by introducing seven optimization routines and three metrics tailored to Quil-enabled architectures. The authors develop a hybrid analysis framework (control-flow and data-dependence) and define metrics—$Wall Time$, $QIN$, and $QCT$—to quantify performance, then demonstrate improvements on several real-time algorithms. Their results show modest gains for some protocols and no gains for others, highlighting both potential and limitations of current approaches. By laying foundational metrics and a hybrid optimization toolkit, the paper paves the way for more sophisticated optimizers that enable efficient collaboration between classical CPUs and quantum processors.

Abstract

Quantum computers do not run in isolation; rather, they are embedded in quantum-classical hybrid architectures. In these setups, a quantum processing unit communicates with a classical device in near-real time. To enable efficient hybrid computations, it is mandatory to optimize quantum-classical hybrid code. To the best of our knowledge, no previous work on the optimization of hybrid code nor on metrics for which to optimize such code exists. In this work, we take a step towards optimization of hybrid programs by introducing seven optimization routines and three metrics to evaluate the effectiveness of the optimization. We implement these routines for the hybrid quantum language Quil and show that our optimizations improve programs according to our metrics. This lays the foundation for new kinds of hybrid optimizers that enable real-time collaboration between quantum and classical devices.

Figures (5)

• Figure 1: The refined HQCC model (from fu2021quingo). Dotted lines indicate slow communication (longer than the coherence time of the qubits), the continuous line indicates fast communication (shorter than qubit coherence time).
• Figure 2: Creating basic blocks in the CFG from alternating quantum and classical instructions.
• Figure 3: An example for multiple DDGs originating from one Quil code. The code can be found in \ref{['lst:exampleDDGCode']}.
• Figure 4: An example of a Quil execution that has a wall time of $7$.
• Figure 5: An example of determining $\delta t_{between}$, $n_{q\_before}$ and $n_{q\_after}$ from Quil code. The QCT is the sum of these three values.