TreeVQA: A Tree-Structured Execution Framework for Shot Reduction in Variational Quantum Algorithms

TL;DR

TreeVQA introduces a tree-structured execution framework that significantly reduces the number of shots needed in Variational Quantum Algorithms by exploiting similarities across tasks, leading to substantial cost savings.

Contribution

It proposes a novel tree-based execution framework for VQAs that adaptively branches to minimize redundant quantum executions, improving efficiency over traditional methods.

Findings

Average shot reduction of 25.9x across benchmarks

Over 100x shot reduction for large-scale problems

Benefits increase with problem size and precision

Abstract

Variational Quantum Algorithms (VQAs) are promising for near- and intermediate-term quantum computing, but their execution cost is substantial. Each task requires many iterations and numerous circuits per iteration, and real-world applications often involve multiple tasks, scaling with the precision needed to explore the application's energy landscape. This demands an enormous number of execution shots, making practical use prohibitively expensive. We observe that VQA costs can be significantly reduced by exploiting execution similarities across an application's tasks. Based on this insight, we propose TreeVQA, a tree-based execution framework that begins by executing tasks jointly and progressively branches only as their quantum executions diverge. Implemented as a VQA wrapper, TreeVQA integrates with typical VQA applications. Evaluations on scientific and combinatorial benchmarks show shot count reductions of $25.9\times$ on average and over $100\times$ for large-scale problems at the same target accuracy. The benefits grow further with increasing problem size and precision requirements.