QuCLEAR: Clifford Extraction and Absorption for Quantum Circuit Optimization

2024-08-23

AI Summary

To address the fidelity degradation of quantum circuits on Noisy Intermediate-Scale Quantum (NISQ) devices caused by hardware noise, this paper proposes a novel compilation framework that jointly optimizes the number of two-qubit gates and circuit depth. The core method introduces a pioneering two-stage paradigm—“Clifford Extraction + Clifford Absorption”—which systematically extracts Clifford subcircuits and relocates them toward the circuit’s end, leveraging their algebraic structure and efficient classical simulability for absorption. This is synergistically combined with gate merging, phase propagation, and measurement folding to achieve hardware-efficient circuit compression. Evaluated on representative variational quantum algorithms—including VQE and QAOA—the framework reduces CNOT count by up to 77.7% and entanglement depth by up to 84.1%, outperforming state-of-the-art compilation techniques.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Combinatorial Optimization

Application Category

• 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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Quantum computing carries significant potential for addressing practical problems. However, currently available quantum devices suffer from noisy quantum gates, which degrade the fidelity of executed quantum circuits. Therefore, quantum circuit optimization is crucial for obtaining useful results. In this paper, we present QuCLEAR, a compilation framework designed to optimize quantum circuits. QuCLEAR significantly reduces both the two-qubit gate count and the circuit depth through two novel optimization steps. First, we introduce the concept of Clifford Extraction, which extracts Clifford subcircuits to the end of the circuit while optimizing the gates. Second, since Clifford circuits are classically simulatable, we propose Clifford Absorption, which efficiently processes the extracted Clifford subcircuits classically. We demonstrate our framework on quantum simulation circuits, which have wide-ranging applications in quantum chemistry simulation, many-body physics, and combinatorial optimization problems. Near-term algorithms such as VQE and QAOA also fall within this category. Experimental results across various benchmarks show that QuCLEAR achieves up to a $77.7%$ reduction in CNOT gate count and up to an $84.1%$ reduction in entangling depth compared to state-of-the-art methods.

Problem

• Optimizes quantum circuits for better fidelity
• Reduces two-qubit gate count and circuit depth
• Classically processes Clifford subcircuits efficiently

Innovation

• Clifford Extraction optimization
• Clifford Absorption processing
• reduces CNOT gate count