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A Novel Noise-Aware Classical Optimizer for variational quantum algorithms

INFORMS JOURNAL ON COMPUTING 2025年第1期37卷 1-196, C2页

作者： Larson, Jeffrey Menickelly, Matt Shi, Jiahao Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Univ Michigan Ind & Operat Engn Dept Ann Arbor MI 48109 USA

A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quantum algorithms will, for the foreseeable future, necessarily be run on noisy devices with limited fidelities. Thus, the evaluation of an objective function (e.g., the guiding function in the quantum approximate optimization algorithm (QAOA) or the expectation of the electronic Hamiltonian in variational quantum eigensolver (VQE)) required by a classical optimizer is subject not only to stochastic error from estimating an expected value but also to error resulting from intermittent hardware noise. Model-based derivative-free optimization methods have emerged as popular choices of a classical optimizer in the noisy VQA setting, based on empirical studies. However, these optimization methods were not explicitly designed with the consideration of noise. In this work we adapt recent developments from the "noise-aware numerical optimization" literature to these commonly used derivative-free model-based methods. We introduce the key defining characteristics of these novel noise-aware derivative-free model-based methods that separate them from standard model-based methods. We study an implementation of such noise-aware derivative-free model-based methods and compare its performance on demonstrative VQA simulations to classical solvers packaged in scikit-quant.

关键词： variational quantum algorithms classical optimizers optimization methods derivative-free optimization noisy optimization

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Verifiable cloud-based variational quantum algorithms

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OPTICS COMMUNICATIONS 2025年 578卷

作者： Yang, Junhong Wang, Banghai Quan, Junyu Li, Qin Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China Xiangtan Univ Sch Math & Computat Sci Xiangtan 411105 Peoples R China Xiangtan Univ Sch Comp Sci Xiangtan 411105 Peoples R China

variational quantum algorithms (VQAs) demonstrate potential advantages on noisy intermediate-scale quantum (NISQ) devices, particularly in applications for quantum machine learning (QML). However, due to the high cost and limited availability of quantum resources, delegating VQAs through cloud networks offers a practical solution for clients with limited quantum capabilities. Recently, Shingu et al. proposed a variational secure cloud quantum computing protocol that employs ancilla-driven quantum computation (ADQC) to facilitate cloud-based VQAs while minimizing quantum resource consumption. Nonetheless, their protocol lacks verifiability, rendering it susceptible to potential malicious interference by the server or unintended deviations that could result in corrupted output states. Furthermore, channel loss necessitates frequent re- delegation, complicating the verification process as the size of the delegated variational circuit increases. This paper introduces a novel protocol that addresses these challenges by incorporating both verifiability and high tolerance to channel loss in cloud-based VQAs. Our approach emphasizes the critical integration of ADQC with robust security measures to ensure computational integrity, even in less controlled cloud networks.

关键词： variational quantum algorithms Cloud networks Ancilla-driven quantum computation Verifiability

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variational quantum algorithms: fundamental concepts, applications and challenges

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quantum INFORMATION PROCESSING 2024年第6期23卷 224-224页

作者： Qi, Han Xiao, Sihui Liu, Zhuo Gong, Changqing Gani, Abdullah Shenyang Aerosp Univ Sch Comp Sci Shenyang 110000 Peoples R China Jalan Univ Fac Comp Sci & Informat Technol Univ Malaya Kuala Lumpur 50603 Malaysia

quantum computing is a new discipline combining quantum mechanics and computer science, which is expected to solve technical problems that are difficult for classical computers to solve efficiently. At present, quantum algorithms and hardware continue to develop at a high speed, but due to the serious constraints of quantum devices, such as the limited numbers of qubits and circuit depth, the fault-tolerant quantum computing will not be available in the near future. variational quantum algorithms(VQAs) using classical optimizers to train parameterized quantum circuits have emerged as the main strategy to address these constraints. However, VQAs still have many challenges, such as trainability, hardware noise, expressibility and entangling capability. The fundamental concepts and applications of VQAs are reviewed. Then, strategies are introduced to overcome the challenges of VQAs and the importance of further researching VQAs is highlighted.

关键词： quantum computing Noisy-intermediate scale quantum variational quantum algorithms quantum machine learning

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variational quantum algorithms via Measurement-Induced Passive Steering 5

Variational Quantum Algorithms via Measurement-Induced Passi...

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2024 International Conference on quantum Computing and Engineering

作者： Sanjaya, Sahan Volya, Daniel Mishra, Prabhat Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9798331541378

variational quantum algorithms (VQA) combine the advantages of classical and near-term quantum computation for solving problems on today's noisy quantum devices. variational quantum Eigensolver (VQE) is one of the widely used VQAs, which aims to find the approximate ground state energy of a given Hamiltonian. While traditional VQE implementation is promising for ansatz-based trial state preparation, it requires an initial fiducial state or a reference state, which can be infeasible for large quantum systems. In this paper, we propose a novel approach for trial state preparation in VQE algorithms. This method leverages passive steering, a circuit-based approach with repeated measurements, eliminating the need for an initial fiducial state or a reference state. Experimental results demonstrate that passive steering-based state preparation provides improved accuracy and scalability of VQE compared to traditional ansatz-based solutions. Our proposed solution can also be effectively combined with the existing ansatz-based methods, where passive steering prepares the reference state while ansatz prepares the trial state, facilitating a robust and scalable state preparation for variational quantum algorithms.

关键词： quantum Computing variational quantum algorithms variational quantum Eigensolver Ansatz State Preparation Passive Steering

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variational quantum algorithms in Anomaly Detection, Fraud Indicator Identification, Credit Scoring, and Stock Price Prediction 9th

Variational Quantum Algorithms in Anomaly Detection, Fraud I...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Thi, Hiep L. Thanh Nguyen Thu Dau Mot Univ Fac Educ Binh Duong 75000 Vietnam Duy Tan Univ Inst Res & Dev Da Nang Vietnam

ISBN: (纸本)9789819733019;9789819733026

The quantum processors' performance is predicted to surpass the application of variational quantum algorithms in finance has proven to be instrumental in addressing crucial challenges. From enhancing security through anomaly detection and fraud indicator identification to optimizing credit scoring and improving stock price prediction, VQAs demonstrate their versatility and potential to revolutionize the financial industry's analytical capabilities. As quantum computing continues to advance, the integration of VQAs is expected to play an increasingly pivotal role in shaping the future of financial technology. In this paper, we review variational quantum algorithms in anomaly detection and fraud indicator systems, credit scoring, and stock price prediction.

关键词： quantum computing quantum machine learning variational quantum algorithms

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variational quantum algorithms in Finance 9th

Variational Quantum Algorithms in Finance

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9th International Congress on Information and Communication Technology (ICICT)

作者： Cong, Thanh N. N. Thi, Hiep. L. Duy Tan Univ Inst Res & Dev Da Nang City 550000 Vietnam Thu Dau Mot Univ Fac Educ Thu Dau Mot City 75000 Binh Duong Vietnam

ISBN: (纸本)9789819732982;9789819732999

The quantum processors' performance is predicted to surpass the traditional systems during this decade in computational performance and capabilities. This disruptive technology can significantly impact many industrial sectors in the long term. Other than communication and mathematics, we expect the finance sector to be one of the first to receive the prosperity of this new cutting-edge technology. This paper provides a review of the current progress on quantum algorithms for financial applications, specifically focusing on the use cases that can be addressed through machine learning.

关键词： quantum computing quantum machine learning variational quantum algorithms

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Near-term quantum computing techniques: variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation

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Science China(Physics,Mechanics & Astronomy) 2023年第5期66卷 23-72页

作者： He-Liang Huang Xiao-Yue Xu Chu Guo Guojing Tian Shi-Jie Wei Xiaoming Sun Wan-Su Bao Gui-Lu Long Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou 450000China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Beijing Academy of Quantum Information Sciences Beijing 100193China Department of Physics Tsinghua UniversityBeijing 100084China

quantum computing is a game-changing technology for global academia,research centers and industries including computational science,mathematics,finance,pharmaceutical,materials science,chemistry and *** it has seen a major boost in the last decade,we are still a long way from reaching the maturity of a full-fledged quantum *** said,we will be in the noisy-intermediate scale quantum(NISQ)era for a long time,working on dozens or even thousands of qubits quantum computing *** outstanding challenge,then,is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum *** address this challenge,several near-term quantum computing techniques,including variational quantum algorithms,error mitigation,quantum circuit compilation and benchmarking protocols,have been proposed to characterize and mitigate errors,and to implement algorithms with a certain resistance to noise,so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful ***,the development of near-term quantum devices is inseparable from the efficient classical sim-ulation,which plays a vital role in quantum algorithm design and verification,error-tolerant verification and other *** review will provide a thorough introduction of these near-term quantum computing techniques,report on their progress,and finally discuss the future prospect of these techniques,which we hope will motivate researchers to undertake additional studies in this field.

关键词： quantum computing noisy-intermediate scale quantum variational quantum algorithms error mitigation circuit com-pilation benchmarking protocols classical simulation

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Simulating thermodynamic properties of dinuclear metal complexes using variational quantum algorithms

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PHYSICA SCRIPTA 2024年第9期99卷 095131-095131页

作者： Silva, Ana Clara das Neves Galvao, Lucas Queiroz Cruz, Clebson Univ Fed Oeste Bahia Ctr Ciencias Exatas & Tecnol Grp Informacao Quant & Fis Estat Campus Reitor Edgard SantosRua Bertioga892Morad BR-47810059 Barreiras BA Brazil LAQCC Latin Amer Quantum Comp Ctr SENAI CIMATEC Salvador BA Brazil Ctr Competencia Embrapii Cimatec SENAI CIMATEC QuIIN Quantum Ind Innovat Av Orlando Gomes 1845 BR-41850010 Salvador BA Brazil

In this paper, we investigate the use of variational quantum algorithms for simulating the thermodynamic properties of dinuclear metal complexes. Our study highlights the potential of quantum computing to transform advanced simulations and provide insights into the physical behavior of quantum systems. The results demonstrate the effectiveness of variational quantum algorithms in simulating thermal states and exploring the thermodynamic properties of low-dimensional molecular magnetic systems. The findings from this research contribute to broadening our understanding of quantum systems and pave the way for future advancements in materials science through quantum computing.

关键词： variational quantum algorithms metal complexes thermodynamic properties

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variational quantum algorithms for trace distance and fidelity estimation

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quantum SCIENCE AND TECHNOLOGY 2022年第1期7卷

作者： Chen, Ranyiliu Song, Zhixin Zhao, Xuanqiang Wang, Xin Baidu Res Inst Quantum Comp Beijing 100193 Peoples R China

Estimating the difference between quantum data is crucial in quantum computing. However, as typical characterizations of quantum data similarity, the trace distance and quantum fidelity are believed to be exponentially-hard to evaluate in general. In this work, we introduce hybrid quantum-classical algorithms for these two distance measures on near-term quantum devices where no assumption of input state is required. First, we introduce the variational trace distance estimation (VTDE) algorithm. We in particular provide the technique to extract the desired spectrum information of any Hermitian matrix by local measurement. A novel variational algorithm for trace distance estimation is then derived from this technique, with the assistance of a single ancillary qubit. Notably, VTDE could avoid the barren plateau issue with logarithmic depth circuits due to a local cost function. Second, we introduce the variational fidelity estimation algorithm. We combine Uhlmann's theorem and the freedom in purification to translate the estimation task into an optimization problem over a unitary on an ancillary system with fixed purified inputs. We then provide a purification subroutine to complete the translation. Both algorithms are verified by numerical simulations and experimental implementations, exhibiting high accuracy for randomly generated mixed states.

关键词： fidelity near-term quantum computing variational quantum algorithms trace distance quantum machine learning

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A semi-agnostic ansatz with variable structure for variational quantum algorithms

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quantum MACHINE INTELLIGENCE 2023年第2期5卷 1-22页

作者： Bilkis, M. Cerezo, M. Verdon, Guillaume Coles, Patrick J. Cincio, Lukasz Univ Autonoma Barcelona Dept Fis Fis Teor Informac & Fenomens Quant ES-08193 Bellaterra Barcelona Spain Los Alamos Natl Lab Theoret Div Los Alamos NM 87545 USA Los Alamos Natl Lab Ctr Nonlinear Studies Los Alamos NM USA Sandbox Alphabet Mountain View CA USA Univ Waterloo Inst Quantum Comp Waterloo ON Canada

quantum machine learning-and specifically variational quantum algorithms (VQAs)-offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.

关键词： quantum machine learning variational quantum algorithms quantum circuit discovery

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