Dead Gate Elimination

2025-04-17

AI Summary

Hybrid quantum algorithms suffer from redundant quantum gate executions—specifically, gates that do not influence final classical outcomes yet are fully executed. Method: This paper introduces dead-gate identification and elimination, formally defining “dead gates” and proving their safe removability. Leveraging data-flow analysis and measurement contribution modeling, our approach supports dynamic derivation of measurement dependencies and is compatible with practical algorithms including VQE and QPE. By jointly analyzing quantum circuit structure and classical post-processing logic, it achieves circuit simplification without altering critical measurement distributions. Contribution/Results: Experiments on VQE, QPE, and random hybrid circuits demonstrate substantial reduction in non-contributing gates. Optimization gain scales linearly with the proportion of non-contributing measurements, significantly improving quantum resource utilization efficiency.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Hybrid quantum algorithms combine the strengths of quantum and classical computing. Many quantum algorithms, such as the variational quantum eigensolver (VQE), leverage this synergy. However, quantum circuits are executed in full, even when only subsets of measurement outcomes contribute to subsequent classical computations. In this manuscript, we propose a novel circuit optimization technique that identifies and removes dead gates. We prove that the removal of dead gates has no influence on the probability distribution of the measurement outcomes that contribute to the subsequent calculation result. We implemented and evaluated our optimization on a VQE instance, a quantum phase estimation (QPE) instance, and hybrid programs embedded with random circuits of varying circuit width, confirming its capability to remove a non-trivial number of dead gates in real-world algorithms. The effect of our optimization scales up as more measurement outcomes are identified as non-contributory, resulting in a proportionally greater reduction of dead gates.

Problem

• Eliminate dead gates in quantum circuits
• Optimize hybrid quantum-classical algorithms
• Reduce unnecessary quantum computations

Innovation

• Identifies and removes dead gates
• Proves no impact on outcome distribution
• Scales with non-contributory measurements