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International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

CaQR: A Compiler-Assisted Approach for Qubit Reuse through Dynamic Circuit

Fei Hua, Yuwei Jin, Yanhao Chen, Suhas Vittal, Kevin Krsulich, Lev S. Bishop, John Lapeyre, Ali Javadi-Abhari, Eddy Z. Zhang

Published Mar 20, 2023• 27 citations
Publisher

Abstract

Quantum measurement is important to quantum computing as it extracts out the outcome of the circuit at the end of the computation. Previously, all measurements have to be done at the end of the circuit. Otherwise, it will incur significant errors. But it is not the case now. Recently IBM starts supporting dynamic circuit through hardware (instead of software by simulator). With mid-circuit hardware measurement, we can improve circuit efficacy and fidelity from three aspects: (a) reduced qubit usage, (b) reduced swap insertion, and (c) improved fidelity. We demonstrate this using real-world applications Bernstein Verizani on real hardware and show that circuit resource usage can be improved by 60%, and circuit fidelity can be improved by 15%. We design a compiler-assisted tool that can find and exploit the tradeoff between qubit reuse, fidelity, gate count, and circuit duration. We also developed a method for identifying whether qubit reuse will be beneficial for a given application. We evaluated our method on a representative set of important applications. We can reduce resource usage by up to 80% and improve circuit fidelity by up to 20%.

DOI
10.1145/3582016.3582030

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Quantum measurement is important to quantum computing as it extracts out the outcome of the circuit at the end of the computation. Previously, all measurements have to be done at the end of the circuit. Otherwise, it will incur significant errors. But it is not the case now. Recently IBM starts supporting dynamic circuit through hardware (instead of software by simulator). With mid-circuit hardware measurement, we can improve circuit efficacy and fidelity from three aspects: (a) reduced qubit usage, (b) reduced swap insertion, and (c) improved fidelity. We demonstrate this using real-world applications Bernstein Verizani on real hardware and show that circuit resource usage can be improved by 60%, and circuit fidelity can be improved by 15%. We design a compiler-assisted tool that can find and exploit the tradeoff between qubit reuse, fidelity, gate count, and circuit duration. We also developed a method for identifying whether qubit reuse will be beneficial for a given application. We evaluated our method on a representative set of important applications. We can reduce resource usage by up to 80% and improve circuit fidelity by up to 20%.
Quantum measurement is important to quantum computing as it extracts the outcome of the circuit at the end of the computation. Previously, all measurements have to be done at the end of the circuit. Otherwise, it will incur significant errors. But it is not the case now. Recently IBM started supporting dynamic circuits through hardware (instead of software by simulator). With mid-circuit hardware measurement, we can improve circuit efficacy and fidelity from three aspects: (a) reduced qubit usage, (b) reduced swap insertion, and (c) improved fidelity. We demonstrate this using real-world applications Bernstein Verizani on real hardware and show that circuit resource usage can be improved by 60\%, and circuit fidelity can be improved by 15\%. We design a compiler-assisted tool that can find and exploit the tradeoff between qubit reuse, fidelity, gate count, and circuit duration. We also developed a method for identifying whether qubit reuse will be beneficial for a given application. We evaluated our method on a representative set of essential applications. We can reduce resource usage by up to 80\% and circuit fidelity by up to 20\%.
The practical applications of quantum computing is currently limited by the small number of available qubits. Recent advances in quantum hardware have introduced midcircuit measurements and resets, enabling the reuse of measured qubits and thus reducing the qubit requirements for executing quantum algorithms. In this work, we present a systematic study of dynamic quantum circuit compilation, a process that transforms static quantum circuits into their dynamic equivalents with fewer qubits through qubit reuse. We establish the first graph-based framework for optimizing qubit-reuse compilation. In particular, we characterize the task of finding the optimal compilation strategy for maximizing qubit reuse using binary integer programming and provide efficient heuristic algorithms for devising general compilation strategies. We conduct a thorough analysis of quantum circuits with practical relevance and offer their optimal qubit-reuse compilation strategies. We also perform a comparative analysis against state-of-the-art approaches, demonstrating the superior performance of our methods in both structured and random quantum circuits. Our framework lays a rigorous foundation for understanding dynamic quantum circuit compilation via qubit reuse, holding significant promise for the practical implementation of large-scale quantum algorithms on quantum computers with limited resources.
Recent advances in quantum computing introduce the ability to reuse qubits through mid-circuit measurements, thereby enhancing the efficiency of quantum devices with limited computational resources.However, identifying optimal reuse opportunities in quantum circuits remains challenging due to the intricate dependencies between quantum gates.Existing frameworks address this by either directly searching for reuse opportunities or converting circuits into directed acyclic graphs (DAGs).Unfortunately, these frameworks may require exponential search complexity or may not always ensure optimal results due to their non-deterministic property.To overcome these challenges, we propose QR-Map (Qubit Reuse Map), a map-based framework that abstracts computational dependencies for efficient qubit reuse.By extracting and aligning two-qubit gates, QR-Map facilitates dependency detection and ensures qubit savings without incurring excessive idle time.This approach achieves an optimal balance between gate serialization depth and crosstalk reduction.Evaluations with various quantum circuit benchmarks demonstrate that quantum circuits optimized with QR-Map achieve average reductions of 20% in qubit usage, 25% in circuit depth, and 22% in SWAP insertions compared to those optimized with the state-of-the-art framework.
Quantum circuit compilation is a critical step in realizing quantum algorithms on real quantum hardware. Traditional compilation methods often employ static optimization strategies, neglecting the unique constraints and characteristics of the target quantum device. This paper introduces a novel adaptive quantum circuit compilation framework designed to dynamically adjust circuit designs based on hardware-specific information, aiming to minimize gate count and, consequently, error rates. The core of the framework lies in integrating a hardware simulator directly within the compilation process. This allows the compiler to evaluate the performance of various circuit designs—considering factors like qubit connectivity, gate fidelity, and decoherence models—before committing to a final layout. The proposed approach represents a shift from static to adaptive optimization, providing a more nuanced and effective strategy for translating quantum algorithms into executable code on diverse quantum hardware platforms. This research demonstrates a significant potential improvement in quantum circuit efficiency and fidelity, addressing a key bottleneck in the advancement of quantum computing.
Dynamic quantum circuits integrate mid-circuit measurements and feed-forward operations to enable real-time classical processing and conditional quantum logic. These capabilities are central to key quantum protocols such as quantum error correction, and have recently demonstrated significant potential for reducing quantum resources, including circuit depth and gate count, across a range of applications. However, executing dynamic circuits on real quantum hardware introduces a critical trade-off: while resource requirements decrease, circuit fidelity degrades due to high error rates of mid-circuit measurements, as well as the decoherence errors accumulated during the extended idle periods introduced by both mid-circuit measurements and feed-forward operations. In this paper, we systematically investigate the impact of standard error mitigation techniques on dynamic circuit applications pertaining to Hamiltonian simulation and ground state estimation of physically relevant systems like the Heisenberg model. We explore dynamical decoupling (DD) as a strategy to suppress decoherence and crosstalk errors during idle windows introduced by mid-circuit measurements and feed-forward delays, and also examine error mitigation via zero-noise extrapolation (ZNE). Through experiments conducted on IBM quantum hardware, we benchmark effective combinations of these strategies that maximize the practical benefits of dynamic quantum circuits in these applications. We demonstrate that a combination of DD and ZNE is effective in mitigating the errors introduced during mid-circuit measurements and feed-forward operations, as well as the errors arising from faulty measurements. This approach yields a energy gap improvement of at least 60% in ground state estimation and reduces observed error of time-evolved states by up to 99% for the Ising model and up to 20% for the Heisenberg model.
Recently developed quantum computers support mid-circuit measurements, allowing qubit measurement, resetting, and reuse during the execution of quantum circuits. This capability opens new opportunities for optimizing quantum circuit compilation, particularly in the context of Noisy Intermediate-Scale Quantum devices. This paper explores the role of machine learning in integrating mid-circuit measurements into the compilation process, focusing on strategies to minimize errors. Specifically, we propose a reinforcement learning method to enable physically-aware qubit reuse and a machine learning method to support physically-aware synthesis-based qubit reuse. We conduct an extensive experimental evaluation to demonstrate the effectiveness of our methods using various quantum algorithms executed on an IBM quantum computer.