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检索条件"主题词=Variational Quantum Algorithms"

共 123 条记录，以下是81-90 订阅

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Metamodel and Formalization to Model, Transform, Deploy, and Execute quantum Workflows 12th

Metamodel and Formalization to Model, Transform, Deploy, and...

引用

13th International Conference on Cloud Computing and Services Science (CLOSER)

作者： Beisel, Martin Barzen, Johanna Bechtold, Marvin Leymann, Frank Truger, Felix Weder, Benjamin Univ Stuttgart Univ Str 38 D-70569 Stuttgart Germany

ISBN: (纸本)9783031681646;9783031681653

quantum algorithms typically comprise classical pre- and post-processing tasks, making quantum applications inherently hybrid. To facilitate the integration and orchestration challenges arising when building hybrid applications, a quantum-specific workflow modeling extension was introduced. However, it lacks support for state-of-the-art techniques that have emerged in recent years, e.g., circuit cutting and warm-starting. As the usage of these complex techniques further complicates integrating quantum circuit executions with classical applications, we introduce new activity types and data objects to facilitate their integration. Moreover, we formalize the quantum workflow modeling extension by presenting a metamodel as well as transformation algorithms, ensuring its compatibility with existing workflow languages. Furthermore, we showcase the practical feasibility of our approach by presenting a system architecture, a prototypical implementation, and a case study.

关键词： quantum computing Modeling extension Model transformation Workflow technology variational quantum algorithms

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Channel Modeling Based On quantum Generative Adversarial Network 14

Channel Modeling Based On Quantum Generative Adversarial Net...

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14th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Gong, Zhairui He, Xinling Wan, Zhifan Li, Zetong Zhang, Xianchao Yu, Xutao Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Peoples R China China Data Ctr Agr Bank Shanghai 200120 Peoples R China Key Lab Med Elect & Digital Hlth Zhejiang Prov Jiaxing 314001 Peoples R China

ISBN: (纸本)9781665450850

Channel modeling is indispensable in a communication system. In this paper, a novel scheme for channel modeling using quantum generative adversarial model was proposed. A quantum generative adversarial network is a generative adversarial model with a quantum circuit as the generative module and a deep neural network as the discriminant module, thereby exploiting the privilege of quantum algorithms in simulating probability distributions to stochastic channel models. Experiments were conducted on IBM QX quantum computing platform. The gradient descent of the cost function and Kullback-Leibler divergence were analyzed. Results verify the feasibility and superiority of the quantum generative adversarial network for channel modeling.

关键词： Generative Adversarial Network Channel Modeling variational quantum algorithms

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Numerical Simulations of Noisy quantum Circuits for Computational Chemistry 3

Numerical Simulations of Noisy Quantum Circuits for Computat...

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3rd IEEE International Conference on quantum Computing and Engineering (QCE)

作者： Gowrishankar, Meenambika Wright, Jerimiah Claudino, Daniel Lotshaw, Phillip Thien Nguyen McCaskey, Alex Humble, Travis Univ Tennessee UTORII Knoxville TN 37996 USA Oak Ridge Natl Lab Quantum Computat Sci Grp Oak Ridge TN USA Quantum Brilliance Ply Ltd Acton Australia Nvidia Corp Santa Clara CA USA Oak Ridge Natl Lab Quantum Sci Ctr Oak Ridge TN USA

ISBN: (数字)9781665491136

ISBN: (纸本)9781665491136

The opportunities afforded by near-term quantum computers to calculate the ground-state properties of small molecules depend on the structure of the computational ansatz as well as the errors induced by device noise. Here we investigate the behavior of these noisy quantum circuits using numerical simulations to estimate the accuracy and fidelity of the prepared quantum states relative to the ground truth obtained by conventional means. We implement several different types of ansatz circuits derived from unitary coupled cluster theory for the purposes of estimating the ground-state energy of sodium hydride using the variational quantum eigensolver algorithm. We show how relative error in the energy and the fidelity scale with the levels of gate-based noise, the internuclear configuration, the ansatz circuit depth, and the parameter optimization methods.

关键词： variational quantum algorithms Noise quantum Chemistry quantum Computing

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Weighted Feedback-Based quantum Algorithm for Excited States Calculation 5

Weighted Feedback-Based Quantum Algorithm for Excited States...

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2024 International Conference on quantum Computing and Engineering

作者： Rahman, Salahuddin Abdul Karabacak, Ozkan Wisniewski, Rafal Aalborg Univ Sect Automat & Control Dept Elect Syst Aalborg Denmark Kadir Has Univ Dept Mechatron Engn Istanbul Turkiye

ISBN: (纸本)9798331541378

Drawing inspiration from the Lyapunov control technique for quantum systems, feedback-based quantum algorithms have been proposed for calculating the ground states of Hamiltonians. In this work, we consider extending these algorithms to tackle calculating excited states. Inspired by the weighted subspace-search variational quantum eigensolver algorithm, we propose a novel weighted feedback-based quantum algorithm for excited state calculation. We show that depending on how we design the weights and the feedback law, we can prepare the pth excited state or lowest energy states up to the pth excited state. Through an application in quantum chemistry, we show the effectiveness of the proposed algorithm, evaluating its efficacy via numerical simulations.

关键词： Excited States FALQON NISQ devices quantum Lyapunov Control variational quantum algorithms WFQAE

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Comprehensive Library of variational LSE Solvers 5

Comprehensive Library of Variational LSE Solvers

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2024 International Conference on quantum Computing and Engineering

作者： Meyer, Nico Roehn, Martin Murauer, Jakob Plinge, Axel Mutschler, Christopher Scherer, Daniel D. Fraunhofer Inst Integrated Circuits IIS Fraunhofer IIS Nurnberg Germany

ISBN: (纸本)9798331541378

Linear systems of equations can be found in various mathematical domains, as well as in the field of machine learning. By employing noisy intermediate-scale quantum devices, variational solvers promise to accelerate finding solutions for large systems. Although there is a wealth of theoretical research on these algorithms, only fragmentary implementations exist. To fill this gap, we have developed the variational-lse-solver framework, which realizes existing approaches in literature, and introduces several enhancements. The user-friendly interface is designed for researchers that work at the abstraction level of identifying and developing end-to-end applications.

关键词： Terms quantum computing variational quantum algorithms quantum machine learning quantum software library quantum linear algebra linear system of equations

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Hybrid quantum-Classical Autoencoders for End-to-End Radio Communication 7

Hybrid Quantum-Classical Autoencoders for End-to-End Radio C...

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IEEE/ACM 7th Symposium on Edge Computing (SEC)

作者： Tabi, Zsolt Bako, Bence Nagy, Daniel T. R. Vaderna, Peter Kallus, Zsofia Haga, Peter Zimboras, Zoltan Eotvos Lorand Univ Budapest Hungary Wigner Res Ctr Phys Budapest Hungary Ericsson Res Budapest Hungary

ISBN: (纸本)9781665486118

quantum neural networks are emerging as potential candidates to leverage noisy quantum processing units for applications. Here we introduce hybrid quantum-classical autoencoders for end-to-end radio communication. In the physical layer of classical wireless systems, we study the performance of simulated architectures for standard encoded radio signals over a noisy channel. We implement a hybrid model, where a quantum decoder in the receiver works with a classical encoder in the transmitter part. Besides learning a latent space representation of the input symbols with good robustness against signal degradation, a generalized data re-uploading scheme for the qubit-based circuits allows to meet inference-time constraints of the application.

关键词： variational quantum algorithms quantum machine learning quantum autoencoder radio communication

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Entanglement-enhanced quantum Reinforcement Learning: an Application using Single-Photons 5

Entanglement-enhanced Quantum Reinforcement Learning: an App...

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2024 International Conference on quantum Computing and Engineering

作者： Gaspar, Joaquim M. Bergerault, Alexandre Apostolou, Vassilis Ricou, Arno Quandela F-91300 Massy France Inst Polytech Paris ENSTA Paris F-91120 Palaiseau France

ISBN: (纸本)9798331541378

quantum Reinforcement Learning (QRL) is an emerging field with many novel approaches suitable for noisy intermediate-scale quantum (NISQ) devices being proposed recently. Projective Simulation (PS) is a classical machine learning method based on random walks in a Directed Acyclic Graph (DAG), and is mainly used to train agents in a Reinforcement Learning setup. This graph corresponds to the Episodic Compositional Memory (ECM) of the agent. For the quantum version of PS we use quantum walks of single photons in quantum optical circuits, which go through tunable components (beamsplitters and phase shifters). We call this specific model quantum Optical Projective Simulation (QOPS). In this work, we propose a general framework to translate any 2-layer ECM to a quantum optical circuit and an algorithm for solving quantum strategic games using QOPS. Moreover, we introduce QOPS with an entangled state as input. The application that we are using to evaluate the performance of the framework is the Prisoner's Dilemma. We executed the implementation using Altair's noisy simulator, designed to emulate a single photon-based quantum processor by Quandela. The algorithm provided promising results even in the presence of noise, which makes it a strong candidate for runs on single-photon -based quantum processors.

关键词： quantum reinforcement learning quantum machine learning variational quantum algorithms photonic quantum computing projective simulation quantum games

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LAWS: Look Around and Warm-Start Natural Gradient Descent for quantum Neural Networks

LAWS: Look Around and Warm-Start Natural Gradient Descent fo...

引用

2nd IEEE International Conference on quantum Software (IEEE QSW)

作者： Tao, Zeyi Wu, Jindi Xia, Qi Li, Qun William & Mary Dept Comp Sci Williamsburg VA 23185 USA

ISBN: (纸本)9798350304794

variational quantum algorithms (VQAs) have recently received much attention due to their promising performance in Noisy Intermediate-Scale quantum computers (NISQ). However, VQAs run on parameterized quantum circuits (PQC) with randomly initialized parameters are characterized by barren plateaus (BP) where the gradient vanishes exponentially in the number of qubits. In this paper, we proposed a Look Around Warm-Start (LAWS) quantum natural gradient (QNG) algorithm to mitigate the widespread existing BP issues. LAWS is a combinatorial optimization strategy taking advantage of model parameter initialization and fast convergence of QNG. LAWS repeatedly reinitializes parameter search space for the next iteration parameter update. The reinitialized parameter search space is carefully chosen by sampling the gradient close to the current optimal. Moreover, we present a unified framework (WS-SGD) for integrating parameter initialization techniques into the optimizer. We provide the convergence proof of the proposed framework for both convex and non-convex objective functions based on Polyak-Lojasiewicz (PL) condition. Our experiment results show that the proposed algorithm could mitigate the BP and have better generalization ability in quantum classification problems.

关键词： variational quantum algorithms Natural Gradient Descent

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A Novel Stochastic LSTM Model Inspired by quantum Machine Learning 24

A Novel Stochastic LSTM Model Inspired by Quantum Machine Le...

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24th International Symposium on Quality Electronic Design (ISQED)

作者： Lindsay, Joseph Zand, Ramtin Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

ISBN: (纸本)9798350334753

Works in quantum machine learning (QML) over the past few years indicate that QML algorithms can function just as well as their classical counterparts, and even outperform them in some cases. Among the corpus of recent work, many current QML models take advantage of variational quantum algorithm (VQA) circuits, given that their scale is typically small enough to be compatible with NISQ devices and the method of automatic differentiation for optimizing circuit parameters is familiar to machine learning (ML). While the results bare interesting promise for an era when quantum machines are more readily accessible, if one can achieve similar results through non-quantum methods then there may be a more near-term advantage available to practitioners. To this end, the nature of this work is to investigate the utilization of stochastic methods inspired by a variational quantum version of the long short-term memory (LSTM) model in attempt to approach the reported successes in performance and rapid convergence. By analyzing the performance of classical, stochastic, and quantum methods, this work aims to elucidate if it is possible to achieve some of QML's major reported benefits on classical machines by incorporating aspects of its stochasticity.

关键词： Machine learning stochasticity quantum computing variational quantum algorithms LSTM

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VQA-Classification-Algorithm-Oriented Performance Benchmarks for quantum Computing 8

VQA-Classification-Algorithm-Oriented Performance Benchmarks...

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8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

作者： Zhang, Meng Wang, Jing Yao, Fei Lai, Junsen China Acad Informat Commun Technol CAICT MIIT Res Inst Telecommun Transmiss RITT Beijing Peoples R China

ISBN: (纸本)9781665455336

quantum computing, taking advantage of its parallel computing, is expected to provide exponential acceleration on some difficult problems. It is one of the important directions for the leapfrog development of computing power in the future. However, due to the limitation of the number of quantum bits, coherence time, fidelity and other factors, the computing power of quantum computers has not been fully utilized at the current stage. In recent years, researchers have proposed a variety of quantum computing performance benchmarks to evaluate and research the performance of quantum computers from the bit, circuit, system, application and other levels. However, the existing performance benchmarks cannot directly evaluate the ability of quantum computers to solve specific problems. Considering the above problems, this paper proposes a VQA-classification-algorithm-oriented performance benchmarks for quantum computing, which takes the classification effect under different data scales as one of the evaluation indicators. This benchmark can comprehensively evaluate the ability of quantum computers to solve data classification problems from the three dimensions of scale, speed, and quality. The benchmark was used to evaluate the superconducting quantum computer and simulator of the IBM and Huawei quantum platform, and its effect was verified experimentally.

关键词： quantum computing variational quantum algorithms performance benchmarks data classification

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