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arXiv:2301.12358 · Physical Review A

Unified multivariate trace estimation and quantum error mitigation

Jin‐Min Liang, Qiao-Qiao Lv, Zhi‐Xi Wang, Shao-Ming Fei

Published Jan 10, 2023• 9 citations
arXivHTMLPDF

Abstract

Calculating the trace of the product of $m\phantom{\rule{4pt}{0ex}}n$-qubit density matrices (multivariate trace) is a crucial subroutine in quantum error mitigation and information measure estimation. We propose a unified multivariate trace estimation (UMT) which conceptually unifies the previous qubit-optimal and depth-optimal approaches with tunable quantum circuit depth and number of qubits. The constructed circuits have $\ensuremath{\lceil}(m\ensuremath{-}1)/s\ensuremath{\rceil}$ or $n\ensuremath{\lceil}(m\ensuremath{-}1)/s\ensuremath{\rceil}$ depth corresponding to $(s+m)n$ or $s+mn$ qubits for $s\ensuremath{\in}{1,\ensuremath{\cdots},\ensuremath{\lfloor}m/2\ensuremath{\rfloor}}$, respectively. Such flexible circuit structures enable people to choose suitable circuits for different hardware devices. We apply UMT to virtual distillation to achieve exponential error suppression and design a family of concrete circuits to calculate the trace of the product of eight and nine $n$-qubit density matrices. A numerical example shows that the additional circuits still mitigate the noise expectation value under the global depolarizing channel.

DOI
10.1103/physreva.107.012606

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Calculating the trace of the product of mnm\phantom{\rule{4pt}{0ex}}nmn-qubit density matrices (multivariate trace) is a crucial subroutine in quantum error mitigation and information measure estimation. We propose a unified multivariate trace estimation (UMT) which conceptually unifies the previous qubit-optimal and depth-optimal approaches with tunable quantum circuit depth and number of qubits. The constructed circuits have \ensuremath⌈(m\ensuremath−1)/s\ensuremath⌉\ensuremath{\lceil}(m\ensuremath{-}1)/s\ensuremath{\rceil}\ensuremath⌈(m\ensuremath−1)/s\ensuremath⌉ or n\ensuremath⌈(m\ensuremath−1)/s\ensuremath⌉n\ensuremath{\lceil}(m\ensuremath{-}1)/s\ensuremath{\rceil}n\ensuremath⌈(m\ensuremath−1)/s\ensuremath⌉ depth corresponding to (s+m)n(s+m)n(s+m)n or s+mns+mns+mn qubits for s\ensuremath∈1,\ensuremath⋯,\ensuremath⌊m/2\ensuremath⌋s\ensuremath{\in}{1,\ensuremath{\cdots},\ensuremath{\lfloor}m/2\ensuremath{\rfloor}}s\ensuremath∈1,\ensuremath⋯,\ensuremath⌊m/2\ensuremath⌋, respectively. Such flexible circuit structures enable people to choose suitable circuits for different hardware devices. We apply UMT to virtual distillation to achieve exponential error suppression and design a family of concrete circuits to calculate the trace of the product of eight and nine nnn-qubit density matrices. A numerical example shows that the additional circuits still mitigate the noise expectation value under the global depolarizing channel.
There is a folkloric belief that a depth- Θ ( m ) quantum circuit is needed to estimate the trace of the product of m density matrices (i.e., a multivariate trace), a subroutine crucial to applications in condensed matter and quantum information science. We prove that this belief is overly conservative by constructing a constant quantum-depth circuit for the task, inspired by the method of Shor error correction. Furthermore, our circuit demands only local gates in a two dimensional circuit – we show how to implement it in a highly parallelized way on an architecture similar to that of Google's S y c a m o r e processor. With these features, our algorithm brings the central task of multivariate trace estimation closer to the capabilities of near-term quantum processors. We instantiate the latter application with a theorem on estimating nonlinear functions of quantum states with "well-behaved" polynomial approximations.
We present a unified approach to analyzing the cost of various quantum error mitigation methods on the basis of quantum estimation theory. By analyzing the quantum Fisher information matrix of a virtual quantum circuit that effectively represents the operations of quantum error mitigation methods, we derive for a generic layered quantum circuit under a wide class of Markovian noise that, unbiased estimation of an observable encounters an exponential growth with the circuit depth in the lower bound on the measurement cost. Under the global depolarizing noise, we in particular find that the bound can be asymptotically saturated by merely rescaling the measurement results. Moreover, we prove for random circuits with local noise that the cost grows exponentially also with the qubit count. Our numerical simulations support the observation that, even if the circuit has only linear connectivity, such as the brick-wall structure, each noise channel converges to the global depolarizing channel with its strength growing exponentially with the qubit count. This not only implies the exponential growth of cost both with the depth and qubit count, but also validates the rescaling technique for sufficiently deep quantum circuits. Our results contribute to the understanding of the physical limitations of quantum error mitigation and offer a new criterion for evaluating the performance of quantum error mitigation techniques.
In the era of noisy intermediate-scale quantum devices, the number of controllable hardware qubits is insufficient to implement quantum error correction.As an alternative, quantum error mitigation (QEM) can suppress errors in measurement results via repeated experiments and postprocessing of data.Typical techniques for error mitigation, e.g., the quasiprobability decomposition method, ignore correlated errors between different gates.Here, we introduce a QEM method based on the matrix product operator (MPO) representation of a quantum circuit that can characterize the noise channel with polynomial complexity.Our technique is demonstrated on a depth = 20 fully parallel quantum circuit of up to N q = 20 qubits undergoing local and global noise.The circuit error is reduced by a several-times factor with only a small bond dimension D = 1 for the noise channel.The MPO representation increases the accuracy of modeling noise without consuming more experimental resources, which improves the QEM performance and broadens its scope of application.Our method is hopeful of being applied to circuits in higher dimensions with more qubits and deeper depth.
Shadow estimation is a method for deducing numerous properties of an unknown quantum state through a limited set of measurements, which suffers from noises in quantum devices. In this paper, we introduce an error-mitigated shadow estimation approach based on virtual distillation, tailored for applications in near-term quantum devices. Our methodology leverages the qubit reset technique, thereby reducing the associated qubit overhead. Crucially, our approach ensures that the required qubit resources remain independent of the desired accuracy and avoid an exponential measurement overhead, marking a substantial advancement in practical applications. Furthermore, our technique accommodates a mixed Clifford and Pauli-type shadow, which can result in a reduction in the number of required measurements across various scenarios. We also study the trade-off between circuit depth and measurement overhead quantitatively. Through numerical simulations, we substantiate the efficacy of our error mitigation method, establishing its utility in enhancing the robustness of shadow estimations on near-term quantum devices.
Digital quantum simulation of many-body dynamics faces a tension between algorithmic Trotter error and physical noise that accumulates with circuit depth. Typical higher-order product formulas mitigate the algorithmic error at the cost of deeper circuits. Our benchmark shows that, within the limited physical error budget, the feasible advantage is confined to absolute errors well below the 10−210^{-2}10−2 scale, which vanishes under noise levels of current quantum hardware. In sharp contrast to the previous Trotter error mitigation methods, we introduce a constant-depth multi-product formula (cd-MPF) that suppresses the Trotter error by combining multiple circuits at fixed circuit depth. We identify an auxiliary parameter ααα, which reshapes the Trotter error terms while leaving the target evolution invariant. The classical linear combination of the measured expectation values cancels the leading (Δt)2(Δt)^{2}(Δt)2 algorithmic contribution and steepens the Trotter-error scaling with the two-qubit circuit depth ddd from d−2d^{-2}d−2 to d−4d^{-4}d−4. Combined with physical-noise mitigation, our method can serve as a key ingredient for realizing long-time quantum dynamics simulation on near-term hardware.
Abstract. In this paper, we present a quantum algorithm for approximating multivariate traces, i.e., the traces of matrix products. Our research is motivated by the extensive utility of multivariate traces in elucidating spectral characteristics of matrices, as well as by recent advancements in leveraging quantum computing for faster numerical linear algebra. Central to our approach is a direct translation of a multivariate trace formula into a quantum circuit, achieved through a sequence of low-level circuit construction operations. To facilitate this translation, we introduce quantum matrix states linear algebra (qMSLA), a framework tailored for the efficient generation of state preparation circuits via primitive matrix algebra operations. Our algorithm relies on sets of state preparation circuits for input matrices as its primary inputs and yields two state preparation circuits encoding the multivariate trace as output. These circuits are constructed utilizing qMSLA operations, which enact the aforementioned multivariate trace formula. We emphasize that our algorithm’s inputs consist solely of state preparation circuits, eschewing harder to synthesize constructs such as block encodings. Furthermore, our approach operates independently of the availability of specialized hardware like QRAM, underscoring its versatility and practicality.