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arXiv:2511.22712

Qubit Reuse Beyond Reorder and Reset: Optimizing Quantum Circuits by Fully Utilizing the Potential of Dynamic Circuits

Damian Rovara, Lukas Burgholzer, Robert Wille

quant-phSubmitted Nov 27, 2025• Announced Dec 1, 2025• 0 citations
v1 · Nov 27, 2025v2 · Dec 9, 2025
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Abstract

Qubit reuse offers a promising way to reduce the hardware demands of quantum circuits, but current approaches are largely restricted to reordering measurements and applying qubit resets. In this work, we present an approach to further optimize quantum circuits by fully utilizing the potential of dynamic quantum circuits-more precisely by moving measurements and introducing dynamic circuit primitives such as classically controlled gates in a way that forges entirely new pathways for qubit reuse. This significantly reduces the number of required qubits for a variety of circuits, creating new opportunities for running complex circuits on near-term devices with limited qubit counts. We show that the proposed approach drastically outperforms existing methods, reducing qubit requirements where previous approaches are unable to do so for popular quantum circuits such as Quantum Phase Estimation (QPE), Quantum Fourier Transform~(QFT), and Variational Quantum Eigensolver (VQE) ansätze, as well as leading to improvements of up to 95% for sparse random circuits.

Comments
7 pages, 3 figures, 3 tables

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Qubit reuse offers a promising way to reduce the hardware demands of quantum circuits, but current approaches are largely restricted to reordering measurements and applying qubit resets. In this work, we present an approach to further optimize quantum circuits by fully utilizing the potential of dynamic quantum circuits-more precisely by moving measurements and introducing dynamic circuit primitives such as classically controlled gates in a way that forges entirely new pathways for qubit reuse. This significantly reduces the number of required qubits for a variety of circuits, creating new opportunities for running complex circuits on near-term devices with limited qubit counts. We show that the proposed approach drastically outperforms existing methods, reducing qubit requirements where previous approaches are unable to do so for popular quantum circuits such as Quantum Phase Estimation (QPE), Quantum Fourier Transform~(QFT), and Variational Quantum Eigensolver (VQE) ansätze, as well as leading to improvements of up to 95% for sparse random circuits.
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.
Qubit capacity is a common bottleneck in quantum computers, limiting the ability to run complex algorithms and slowing the advancements in quantum computing applications. Reusing qubits by resetting them after measuring and repurposing them for subsequent gate operations offers an effective strategy to reduce the overall qubit requirements. Based on Satisfiability Modulo Theories (SMT) and Integer Linear Programming (ILP), we present an optimal algorithm that maximizes qubit reuse while optionally constraining the circuit depth to a specified upper bound. Our approach considers all feasible circuit rearrangements to enhance optimality, including gate reordering and eager measurement. We further propose a divide-and-conquer approach to scale for large circuits by relaxing the optimality guarantee. Experimental results show that our exact method substantially decreases both the qubit count and the circuit depth compared to the current state of the art, while the divide-and-conquer approach effectively addresses scalability issues with maintained solution quality.
Near-term quantum computations are limited by high error rates, the scarcity of qubits and low qubit connectivity. Increasing support for mid-circuit measurements and qubit reset in near-term quantum computers enables qubit reuse that may yield quantum computations with fewer qubits and lower errors. In this work, we introduce a formal model for qubit reuse optimization that delivers provably optimal solutions with respect to quantum circuit depth, number of qubits, or number of swap gates for the first time. This is in contrast to related work where qubit reuse is used heuristically or optimally but without consideration of the mapping effort. We further investigate reset errors on near-term quantum computers by performing reset error characterization experiments. Using the hereby obtained reset error characterization and calibration data of a near-term quantum computer, we then determine a qubit assignment that is optimal with respect to a given cost function. We define this cost function to include gate errors and decoherence as well as the individual reset error of each qubit. We found the reset fidelity to be state-dependent and to range, depending on the reset qubit, from 67.5% to 100% in a near-term quantum computer. We demonstrate the applicability of the developed method to a number of quantum circuits and show improvements in the number of qubits and swap gate insertions, estimated success probability, and Hellinger fidelity of the investigated quantum circuits.
Quantum circuit optimization - the process of transforming a quantum circuit into an equivalent one with reduced time and space requirements - is crucial for maximizing the utility of current and near-future quantum devices. While most automated optimization techniques focus on transforming circuits into equivalent ones that implement the same unitary, we show that substantial new opportunities for resource reduction can be achieved by (1) allowing approximate local transformations and (2) employing mixed quantum channels to approximate pure circuits. Our novel automated protocol for approximate circuit rewriting is a refined evolution of automated optimization techniques based on the ZX-calculus, where we add a greedy strategy that selectively replaces ZX-diagrams with small phase angles with stochastic mixtures of the identity and carefully chosen over-rotations, which are designed to reduce the overall gate count in expectation while staying within a strict error budget. This approach yields modest two-qubit gate count reduction in random quantum circuits, and achieves a substantial reduction in structured circuits such as the quantum Fourier transform. Fundamentally, our protocol converts experimental noise due to gate applications into deliberately engineered random noise, outperforming many other approximation methods on average. These results highlight the potential of mixed-channel approximations to enhance future quantum circuit performance, suggesting new directions for resource-aware automated quantum compilation beyond pure unitary channels.
Abstract Due to constraints in current quantum hardware manufacturing, the number of available qubits remains limited. This limitation poses significant challenges to the feasibility of quantum computation. Qubit reuse, supported by the hardware-enabled dynamic quantum circuit, has emerged as a promising method for optimizing the width of quantum circuits. However, enabling width optimization through qubit reuse for circuits that lack qubit reuse opportunities remains a challenging problem. This paper aims to address this challenge by introducing RaQCT (Reuseaimed Quantum Circuit Transformation), an algorithm for converting non-reusable qubit pairs into reusable ones. Moreover, this paper proposes a more general method for identifying reusable qubits based on the perspective of the execution sequence of the quantum gates. Based on the proposed identification method of reusable qubit pairs and RaQCT, this paper proposes width optimization strategies based on a tree structure. Compared with state-of-the-art qubit reuse methods, our proposed methods enable further circuit width optimization, which proves effective. Furthermore, the comprehensive set of methods proposed in this paper for handling qubit reuse does not require specific differentiation of circuit types. Meanwhile, it is applicable to both static and dynamic circuits, thereby also demonstrating generality.
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\%.