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

2025-12-12

AI Summary

Variational quantum algorithms (VQAs) incur prohibitive sampling overhead (excessive shots) in multi-task, high-precision scenarios. Method: This paper proposes a tree-structured collaborative execution framework—the first to model cross-task quantum circuit execution similarity as a tree—and enables dynamic reuse of already-executed circuits via joint initial execution and on-demand branching. A lightweight wrapper integrates transparently into existing VQA workflows without modifying algorithms or hardware interfaces. Key techniques include quantum circuit equivalence analysis, adaptive branch decision-making, runtime instrumentation, and sample reweighting. Results: Evaluated on scientific computing and combinatorial optimization benchmarks, the framework reduces shot counts by 25.9× on average and over 100× for large-scale problems, with zero accuracy loss. Crucially, speedup scales favorably with increasing problem size and precision requirements.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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 $25.9 imes$ on average and over $100 imes$ for large-scale problems at the same target accuracy. The benefits grow further with increasing problem size and precision requirements.

Problem

• Reduces execution shots in variational quantum algorithms
• Exploits task similarities to lower VQA costs
• Enables scalable quantum computing for complex applications

Innovation

• Tree-structured execution framework for VQAs
• Joint task execution with progressive branching
• Shot count reduction up to 100x