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arXiv:2210.08862 · Physical Review A

Quantum annealing with error mitigation

Yuta Shingu, Tetsuro Nikuni, Shiro Kawabata, Yuichiro Matsuzaki

Published Apr 2, 2024• 9 citations
arXivHTMLPDF

Abstract

Quantum annealing (QA) is one of the methods to prepare a ground state of a problem Hamiltonian. In the absence of noise, QA can accurately estimate the ground-state energy if the adiabatic condition is satisfied. However, in practice, systems are known to suffer from decoherence. Meanwhile, considerable research effort has been devoted to noisy intermediate-scale quantum (NISQ) computation. For practical NISQ computation, many error-mitigation (EM) methods have been proposed to mitigate the effects of noise. This paper proposes a QA strategy combined with an EM method, namely, dual-state purification, to suppress the effects of decoherence. Our protocol consists of four parts: the conventional dynamics, single-qubit projective measurements, the Hamiltonian dynamics corresponding to an inverse map of the first dynamics, and postprocessing of the measurement results. Importantly, our protocol works without two-qubit gates that require pulse operations; hence, it is suitable for devices designed for practical QA. In addition, we present numerical calculations to show that our protocol is more accurate than the conventional QA in estimating the ground-state energy under decoherence.

DOI
10.1103/physreva.109.042606

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Quantum annealing (QA) is one of the methods to prepare a ground state of a problem Hamiltonian. In the absence of noise, QA can accurately estimate the ground-state energy if the adiabatic condition is satisfied. However, in practice, systems are known to suffer from decoherence. Meanwhile, considerable research effort has been devoted to noisy intermediate-scale quantum (NISQ) computation. For practical NISQ computation, many error-mitigation (EM) methods have been proposed to mitigate the effects of noise. This paper proposes a QA strategy combined with an EM method, namely, dual-state purification, to suppress the effects of decoherence. Our protocol consists of four parts: the conventional dynamics, single-qubit projective measurements, the Hamiltonian dynamics corresponding to an inverse map of the first dynamics, and postprocessing of the measurement results. Importantly, our protocol works without two-qubit gates that require pulse operations; hence, it is suitable for devices designed for practical QA. In addition, we present numerical calculations to show that our protocol is more accurate than the conventional QA in estimating the ground-state energy under decoherence.
Quantum annealing (QA) is an efficient method for finding the ground-state energy of the problem Hamiltonian. However, in practical implementation, the system suffers from decoherence. On the other hand, recently, ``Localized virtual purification" (LVP) was proposed to suppress decoherence in the context of noisy intermediate-scale quantum (NISQ) devices. Suppose observables have spatially local support in the lattice. In that case, the requirement for LVP is to calculate the expectation value with a reduced density matrix on a portion of the total system. In this work, we propose a method to mitigate decoherence errors in QA using LVP. The key idea is to use the so-called classical shadow method to construct the reduced density matrix. Thanks to the CS, unlike the previous schemes to mitigate decoherence error for QA, we do not need either two-qubit gates or mid-circuit measurements, which means that our method is hardware-efficient.
Quantum chemistry calculations are important applications of quantum annealing. For practical applications in quantum chemistry, it is essential to estimate a ground state energy of the Hamiltonian with chemical accuracy. However, there are no known methods to guarantee the accuracy of the estimation of the energy calculated by quantum annealing. Here, we propose a way to improve the accuracy of the estimate of the ground state energy by combining quantum annealing with classical computation. In our scheme, before running the QA, we need a pre-estimation of the energies of the ground state and first excited state with some error bars (corresponding to possible estimation error) by performing classical computation with some approximations. We show that, if an expectation value and variance of the energy of the state after the QA are smaller than certain threshold values (that we can calculate from the pre-estimation), the QA provides us with a better estimate of the ground state energy than that of the pre-estimation. Since the expectation value and variance of the energy can be experimentally measurable by the QA, our results pave the way for accurate estimation of the ground state energy with the QA.
Quantum error mitigation is essential for many computing tasks on the noisy quantum computer with a limited number of qubits. In this paper, we propose a practical protocol of error mitigation by virtually purifying the quantum state without qubit overhead or requiring only one ancilla qubit. In dual-state purification, we effectively generate a purified state with increased fidelity using the erroneous state and its dual state, respectively, prepared with the noisy quantum circuit and the dual map of its inverse circuit. Combined with tomography purification, we can make sure that the final estimate of an observable is obtained from a pure state. While the absolute error increases, the numerical result, based on a depolarizing model, suggests that our protocol reduces the error compared to the case without the protocol by a rescaling factor decreasing with the qubit number and circuit depth; i.e., the relative performance of purification is better for larger circuits. On a cloud quantum computer, we successfully demonstrate the reduced error with a quantum variational eigensolver circuit.
Quantum error mitigation (QEM) is vital for noisy intermediate-scale quantum (NISQ) devices. While most conventional QEM schemes assume discrete gate-based circuits with noise appearing either before or after each gate, the assumptions are inappropriate for describing realistic noise that may have strong gate dependence and complicated nonlocal effects, and general computing models such as analog quantum simulators. To address these challenges, we first extend the scenario, where each computation process, being either digital or analog, is described by a continuous time evolution. For noise from imperfections of the engineered Hamiltonian or additional noise operators, we show it can be effectively suppressed by a stochastic QEM method. Since our method assumes only accurate single qubit controls, it is applicable to all digital quantum computers and various analog simulators. Meanwhile, errors in the mitigation procedure can be suppressed by leveraging the Richardson extrapolation method. As we numerically test our method with various Hamiltonians under energy relaxation and dephasing noise and digital quantum circuits with additional two-qubit crosstalk, we show an improvement of simulation accuracy by 2 orders. We assess the resource cost of our scheme and conclude the feasibility of accurate quantum computing with NISQ devices.
Abstract Quantum annealers like those manufactured by D-Wave Systems are designed to find high quality solutions to optimization problems that are typically hard for classical computers. They utilize quantum effects like tunneling to evolve toward low-energy states representing solutions to optimization problems. However, their analog nature and limited control functionalities present challenges to correcting or mitigating hardware errors. As quantum computing advances towards applications, effective error suppression is an important research goal. We propose a new approach called replication based mitigation (RBM) based on parallel quantum annealing (QA). In RBM, physical qubits representing the same logical qubit are dispersed across different copies of the problem embedded in the hardware. This mitigates hardware biases, is compatible with limited qubit connectivity in current annealers, and is well-suited for currently available noisy intermediate-scale quantum annealers. Our experimental analysis shows that RBM provides solution quality on par with previous methods while being more flexible and compatible with a wider range of hardware connectivity patterns. In comparisons against standard QA without error mitigation on larger problem instances that could not be handled by previous methods, RBM consistently gets better energies and ground state probabilities across parameterized problem sets.
Quantum annealers like those from D-Wave Systems implement adiabatic quantum computing to solve optimization problems, but their analog nature and limited control functionalities present challenges to correcting or mitigating errors. As quantum computing advances towards applications, effective error suppression is an important research goal. We propose a new approach called replication based mitigation (RBM) based on parallel quantum annealing. In RBM, physical qubits representing the same logical qubit are dispersed across different copies of the problem embedded in the hardware. This mitigates hardware biases, is compatible with limited qubit connectivity in current annealers, and is suited for available noisy intermediate-scale quantum (NISQ) annealers. Our experimental analysis shows that RBM provides solution quality on par with previous methods while being compatible with a much wider range of hardware connectivity patterns. In comparisons against standard quantum annealing without error mitigation, RBM consistently improves the energies and ground state probabilities across parameterized problem sets.