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arXiv:2211.01925

Exploiting Qubit Reuse through Mid-circuit Measurement and Reset

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

Published Nov 3, 2022• 12 citations
arXivHTMLPDF

Abstract

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\%.

DOI
10.48550/arxiv.2211.01925

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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%.
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%.
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.
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.
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.
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 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.