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QuCLEAR

Ji Liu, Alvin Gonzales, Zain H. Saleem, Paul Hovland

Published Jan 1, 2025• 0 citations
Publisher

Abstract

SF-25-008 This software optimizes quantum circuits using a two-step process. The first step, Clifford Extraction, moves the Clifford gates to the end of the circuit and includes circuit optimizations. The second step, Clifford Absorption, then addresses these extracted gates on a classical computer. By reducing the number of gates that need to run on the quantum device, the software achieves a significantly lower gate count.

DOI
10.11578/dc.20250223.1

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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 wideranging applications in quantum chemistry simulation, manybody physics, and combinatorial optimization problems. Nearterm 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 with state-of-the-art methods.

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arXiv:2303.10788•Proceedings - International Symposium on Computer Architecture/Proceedings•Jun 16, 2023•23 citations
SF-25-008 This software optimizes quantum circuits using a two-step process. The first step, Clifford Extraction, moves the Clifford gates to the end of the circuit and includes circuit optimizations. The second step, Clifford Absorption, then addresses these extracted gates on a classical computer. By reducing the number of gates that need to run on the quantum device, the software achieves a significantly lower gate count.
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 wideranging applications in quantum chemistry simulation, manybody physics, and combinatorial optimization problems. Nearterm 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 with state-of-the-art methods.
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%77.7\%77.7% reduction in CNOT gate count and up to an 84.1%84.1\%84.1% reduction in entangling depth compared to state-of-the-art methods.
Quantum computers are only as powerful as the compilers that optimize the circuits they run. As quantum hardware scales, reducing gate counts and circuit depth becomes critical for practical computation. This work implements a core synthesis algorithm in OCaml that maps arbitrary Cliffords to circuits, a fundamental building block in quantum circuit compilation. The Clifford synthesis implementation is designed around how LambdaPC thinks about quantum computation, through Pauli operators. It frames synthesis as a search problem: given a Clifford tableau, find a path to the identity by applying gates. Working factor by factor, it reduces each Pauli pair (P, Q) to a canonical (Z_i, X_i) form using single-qubit Clifford gates and TQE gates, until the full tableau is decomposed into an executable circuit. The main challenge was working within LambdaPC's typed representation, where n-qubit Paulis are encoded as nested pairs. This required helper functions to extract Pauli data from those structures and pattern-matching logic to find minimal gate sequences, laying the groundwork for end-to-end Clifford compilation in LambdaPC.
Optimal Hadamard gate count for Clifford+T+T+T synthesis of Pauli rotations sequences
The Clifford+T+T+T gate set is commonly used to perform universal quantum computation. In such setup the TTT gate is typically much more expensive to implement in a fault-tolerant way than Clifford gates. To improve the feasibility of fault-tolerant quantum computing it is then crucial to minimize the number of TTT gates. Many algorithms, yielding effective results, have been designed to address this problem. It has been demonstrated that performing a pre-processing step consisting of reducing the number of Hadamard gates in the circuit can help to exploit the full potential of these algorithms and thereby lead to a substantial TTT-count reduction. Moreover, minimizing the number of Hadamard gates also restrains the number of additional qubits and operations resulting from the gadgetization of Hadamard gates, a procedure used by some compilers to further reduce the number of TTT gates. In this work we tackle the Hadamard gate reduction problem, and propose an algorithm for synthesizing a sequence of π/4π/4π/4 Pauli rotations with a minimal number of Hadamard gates. Based on this result, we present an algorithm which optimally minimizes the number of Hadamard gates lying between the first and the last TTT gate of the circuit.
Quantum computing has potential to provide exponential speedups over classical computing for many important applications. However, today's quantum computers are in their early stages, and hardware quality issues hinder the scale of program execution. Benchmarking and simulation of quantum circuits on classical computers is therefore essential to advance the understanding of how quantum computers and programs operate, enabling both algorithm discovery that leads to high-impact quantum computation and engineering improvements that deliver to more powerful quantum systems. Unfortunately, the nature of quantum information causes simulation complexity to scale exponentially with problem size.