arXiv subjects

Ruyu Yang

Publications and source records attributed to Ruyu Yang.

11 recordsLinked to original sources

arXiv2026

Quantum Error Suppression via Symmetry-Averaged Virtual Distillation

Reliable quantum simulation is limited by both algorithmic approximations and hardware noise, which usually coexist in the output of near-term and early fault-tolerant quantum devices. Existing error-suppression strategies often target these error sources separately. Here, we introduce symmetry-averaged virtual distillation (SAVD), an error-suppression protocol that applies symmetry averaging before virtual distillation and treats both imperfections at the density-matrix level. The protocol constructs a symmetry-averaged output ensemble from symmetry-labeled implementations, leaving the symmetry-invariant target contribution unchanged while averaging residual components over symmetry-related branches. Virtual distillation (VD) is then applied to this averaged ensemble, rather than to the raw output state, to amplify its dominant eigencomponent. We analyze the resulting spectral suppression mechanism and identify the role of symmetry averaging as a state-preconditioning layer for VD. Numerical demonstrations on an isotropic Heisenberg chain show improved accuracy in the presence of both coherent algorithmic errors and hardware noise. Our results provide a general symmetry-based architecture for enhancing quantum simulations.

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arXiv2025

Practical Homodyne Shadow Estimation

Shadow estimation provides an efficient framework for estimating observable expectation values using randomized measurements. While originally developed for discrete-variable systems, its recent extensions to continuous-variable (CV) quantum systems face practical limitations due to idealized assumptions of continuous phase modulation and infinite measurement resolution. In this work, we develop a practical shadow estimation protocol for CV systems using discretized homodyne detection with a finite number of phase settings and quadrature bins. We construct an unbiased estimator for the quantum state and establish both sufficient conditions and necessary conditions for informational completeness within a truncated Fock space up to $n_{\mathrm{max}}$ photons. We further provide a comprehensive variance analysis, showing that the shadow norm scales as $\mathcal{O}(n_{\mathrm{max}}^4)$, improving upon previous $\mathcal{O}(n_{\mathrm{max}}^{13/3})$ bounds. Our work bridges the gap between theoretical shadow estimation and experimental implementations, enabling robust and scalable quantum state characterization in realistic CV systems.

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arXiv2025

Classical post-processing approach for quantum amplitude estimation

We propose an approach for quantum amplitude estimation (QAE) designed to enhance computational efficiency while minimizing the reliance on quantum resources. Our method leverages quantum computers to generate a sequence of signals, from which the quantum amplitude is inferred through classical post-processing techniques. Unlike traditional methods that use quantum phase estimation (QPE), which requires numerous controlled unitary operations and the quantum Fourier transform, our method avoids these complex and resource-demanding steps. By integrating quantum computing with classical post-processing techniques, our method significantly reduces the need for quantum gates and qubits, thus optimizing the utilization of quantum hardware. We present numerical simulations to validate the effectiveness of our method and provide a comprehensive analysis of its computational complexity and error. This hybrid strategy not only improves the practicality of QAE but also broadens its applicability in quantum computing.

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arXiv2024

Expressibility of linear combination of ansatz circuits

Variational Quantum Eigensolver is considered promising for medium-scale noisy quantum computers. Expressibility is an important metric for measuring the capability of a variational quantum Ansatz circuit. A commonly used method to increase expressibility is to increase the circuit depth. However, increasing the circuit depth also introduces more noise. We propose to use a linear combination of ansatzes to improve the expressibility of variational circuits, thus avoiding the increase of circuit depth. Concurrently, we introduce a novel measurement strategy that circumvents the necessity for the Hadamard test, thereby significantly diminishing the reliance on two-qubit gates, which are presently the predominant contributors to quantum noise. We also provide a corresponding gradient calculation method, which makes it convenient to update the parameters. Compared with the method of increasing the circuit depth, our method of improving expressibility is more practical. Numerical simulations demonstrate the effectiveness of our method.

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arXiv2024

Error mitigated shadow estimation based on virtual distillation

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.

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arXiv2024

Quantum-enhanced Green's function Monte Carlo for excited states of nuclear shell model

We present a hybrid quantum-classical Green's function Monte Carlo (GFMC) algorithm for estimating the excited states of the nuclear shell model. The conventional GFMC method, widely used to find the ground state of a quantum many-body system, is plagued by the sign problem, which leads to an exponentially increasing variance with the growth of system size and evolution time. This issue is typically mitigated by applying classical constraints but at the cost of introducing bias. Our approach uses quantum subspace diagonalization (QSD) on a quantum computer to prepare a quantum trial state, replacing the classical trial state in the GFMC process. We also incorporated a modified classical shadow technique in the implementation of QSD to optimize quantum resource utilization. Besides, we extend our hybrid GFMC algorithm to find the excited states of a given quantum system. Numerical results suggest our method largely enhances accuracy in determining excited state energies, offering an improvement over the conventional method.

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arXiv2023

General noise-resilient quantum amplitude estimation

Quantum advantage requires overcoming noise-induced degradation of quantum systems. Conventional methods for reducing noise such as error mitigation face scalability issues in deep circuits. Specifically, noise hampers the extraction of amplitude and observable information from quantum systems. In this work, we present a novel algorithm that enhances the estimation of amplitude and observable under noise. Remarkably, our algorithm exhibits robustness against noise that varies across different depths of the quantum circuits. We assess the accuracy of amplitude and observable using numerical analysis and theoretically analyze the impact of gate-dependent noise on the results. This algorithm is a potential candidate for noise-resilient approaches that have high computational accuracy.

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arXiv2023

Scalable Algorithms for Power Function Calculations of quantum states in NISQ Era

This article focuses on the development of scalable and quantum bit-efficient algorithms for computing power functions of random quantum states. Two algorithms, based on Hadamard testing and Gate Set Tomography, are proposed. We provide a comparative analysis of their computational outcomes, accompanied by a meticulous evaluation of inherent errors in the gate set tomography approach. The second algorithm exhibits a significant reduction in the utilization of two-qubit gates compared to the first. As an illustration, we apply both methods to compute the Von Neumann entropy of randomly generated quantum states.

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arXiv2023

Shadow-based quantum subspace algorithm for the nuclear shell model

In recent years, researchers have been exploring the applications of noisy intermediate-scale quantum (NISQ) computation in various fields. One important area in which quantum computation can outperform classical computers is the ground state problem of a many-body system, e.g., the nucleus. However, using a quantum computer in the NISQ era to solve a meaningful-scale system remains a challenge. To calculate the ground energy of nuclear systems, we propose a new algorithm that combines classical shadow and subspace diagonalization techniques. Our subspace is composed of matrices, with the basis of the subspace being the classical shadow of the quantum state. We test our algorithm on nuclei described by Cohen-Kurath shell model and USD shell model. We find that the accuracy of the results improves as the number of shots increases, following the Heisenberg scaling.

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arXiv2022

Unsupervised machine learning for physical concepts

In recent years, machine learning methods have been used to assist scientists in scientific research. Human scientific theories are based on a series of concepts. How machine learns the concepts from experimental data will be an important first step. We propose a hybrid method to extract interpretable physical concepts through unsupervised machine learning. This method consists of two stages. At first, we need to find the Betti numbers of experimental data. Secondly, given the Betti numbers, we use a variational autoencoder network to extract meaningful physical variables. We test our protocol on toy models and show how it works.

cs.LG↗

arXiv2020

Perturbative tomography of small errors in quantum gates

We propose an efficient protocol to fully reconstruct a set of high-fidelity quantum gates. Usually, the efficiency of reconstructing high-fidelity quantum gates is limited by the sampling noise. Our protocol is based on a perturbative approach and has two stages. In the first stage, the initial part of noisy quantum gates is reconstructed by measuring traces of maps, and the trace can be measured by amplifying the noise in a way similar to randomised benchmarking and quantum spectral tomography. In the second stage, by amplifying the non-unital part using the unital part, we can efficiently reconstruct the non-unital part. We show that the number of measurements needed in our protocol scales logarithmically with the error rate of gates.

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