Recent progress and perspectives on quantum computing for finance

Tang, Yehui; Yan, Junchi; Hu, Guoqiang; Zhang, Baohua; Zhou, Jie

doi:10.1007/s11761-022-00351-7

Cited by 4 publications

(6 citation statements)

References 21 publications

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“…Moreover, the partial differential equation (PDE) governing the system evolution from the initial to the final Hamiltonian operators (3) together with the initial and final restrictions in time (( 7) and ( 8)), they serve as beforehand information to train the network. These inductive biases are helped by the physical insights provided regarding the loss function for the collocation points, by means of the recovery of adiabatic theory (11), the minimization of the action (10) and the decomposition of the gauge potential (13).…”

Section: Discussionmentioning

confidence: 99%

“…Ensuring optimal operator A CD (t) within specific conditions, including local development, robustness, availability, and experimental accessibility, hinges on minimizing physical action through (9), as evidenced by research studies such as [44]. This minimization aligns with defining the term related to physical loss as written in (10). Additionally, the temporal rate of change of the scheduling function, dλ/dt, dictates the influence of the non-adiabatic components on the system evolution under the total Hamiltonian operator.…”

Section: Methodsmentioning

confidence: 99%

“…We compute dλ/dt using automatic differentiation to obtain the Hamiltonian operator H(t). We adhere to initial and final temporal constraints (7) and (8) and calculate the loss via the underlying PDEs (14) incorporating physical constraints (10), (11) and (13), adhering to the Principle of Least Action, adiabaticity, and the operator decomposition. We compute the final loss metric L (15) and update trainable variables through backpropagation to minimize the loss during optimization.…”

Section: As Both H Initial Andmentioning

confidence: 99%

“…Quantum computing, fueled by remarkable achievements in applied quantum machine learning, quantum simulation, and circuit/system optimization, has become a vibrant field of research [1][2][3][4][5][6][7][8]. Of particular interest are optimization problems, prevalent in various sectors like medicine, economics, logistics, and more [9][10][11][12][13][14]. Quantum computing has emerged as a promising alternative to classical approaches due to its potential for increased efficiency and speed.…”

Section: Introductionmentioning

confidence: 99%

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Physics-informed neural networks for an optimal counterdiabatic quantum computation

Ferrer-Sánchez,

Flores-Garrigos,

Hernani-Morales

et al. 2024

Mach. Learn.: Sci. Technol.

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A novel methodology that leverages Physics-Informed Neural Networks to optimize quantum circuits in systems with NQ qubits by addressing the counterdiabatic protocol is introduced. The primary purpose is to employ physics-inspired deep learning techniques for accurately modeling the time evolution of various physical observables within quantum systems. To achieve this, we integrate essential physical information into an underlying neural network to effectively tackle the problem. Specifically, the imposition of the solution to meet the principle of least action, along with the hermiticity condition on all physical observables, among others, ensuring the acquisition of appropriate counterdiabatic terms based on underlying physics. This approach provides a reliable alternative to previous methodologies relying on classical numerical approximations, eliminating their inherent constraints. The proposed method offers a versatile framework for optimizing physical observables relevant to the problem, such as the scheduling function, gauge potential, temporal evolution of energy levels, among others. This methodology has been successfully applied to 2-qubit representing H2 molecule using the STO-3G basis, demonstrating the derivation of a desirable decomposition for non-adiabatic terms through a linear combination of Pauli operators. This attribute confers significant advantages for practical implementation within quantum computing algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: As Both H Initial Andmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Physics-informed neural networks for an optimal counterdiabatic quantum computation

Ferrer-Sánchez,

Flores-Garrigos,

Hernani-Morales

et al. 2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…[6] 2023 presented ideas from Quantum physics to help and create a machine learning application using Quantum computing specifically for optimizing the computational complexity of massive data sets calculations [42]. Quantum Big Bang, a creature of Quantum computers, its assessment, and development are well explained by the author [1], they also discussed Quantum parallelism with its superposition and linearity concept that is helpful for infinite no. of inputs.DiVincenzo criteria are used in Quantum computing technologies, complemented with its specific physical layer feature [43].…”

Section: Twin Support Vector Machines Based On Fruit Fly Optimization...mentioning

confidence: 99%

Quantum Image processing algorithms comparison using datasets of machine learning based applications

SINGH,

Indu,

majumdar

2024

Preprint

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This article analyzes and compares various Quantum learning algorithms on big data. The main contribution of this article is to provide a new machine-learning approach using Quantum computing for big data analysis with features of robust, novel, and effective Quantum machine images and transit mode. Currently, there is no efficient method for doing Quantum image recognition or classification due to the lack of an effective Quantum feature extraction technique. This work proposes a global Quantum feature extraction technique based on Schmidt decomposition for the first time. Additionally, a new version of the Quantum learning algorithm is shown, which uses the features' Hamming distance to classify images. With the help of algorithm analysis and experimental findings from the benchmark database Caltech 101, a successful method for large-scale image classification is developed and put forth in the context of big data. The proposed model yields an with an average accuracy of 98\% with the proposed enhanced Quantum classifier, QeSVM classifier, swarm particle optimizer with Twin wave SVM, QPSO-TWSVM, and other Q-CNN models on different Big Data sets.

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Modern finance through quantum computing—A systematic literature review

Bunescu,

Vârtei

2024

PLoS ONE

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Human intellectual restlessness originates from the need for knowledge of the modern world. The financial world is struggling to prototype accurate and fast data at low risk. The quantum approach to finance can support this desire. The goal of this paper is to provide a comprehensive review of the literature on how quantum computing can be used in finance. This research aims to expose an architecture of the state of the art in quantum finance. In terms of methodology, the PSALSAR framework was used to conduct this systematic literature review. The selection procedure followed the PRISMA guidelines and was applied in two databases (Web of Science and Scopus) without time limit. In total, 94 out of 1646 articles were included for data extraction and assessment of content evaluation covering the period 2001–2023. The current review of quantum finance literature is structured around the following themes: journals, research methods, tested data series, research topics in quantum finance, and future research directions. Within the financial sector, quantum computing is used in three main areas: simulation, optimization, and machine learning. These areas are supported by algorithms that have been created in recent years. Finally, we propose to highlight the benefits and the applications of quantum finance and to stimulate the interest in the future prospects of the debates.

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Recent progress and perspectives on quantum computing for finance

Cited by 4 publications

References 21 publications

Physics-informed neural networks for an optimal counterdiabatic quantum computation

Physics-informed neural networks for an optimal counterdiabatic quantum computation

Quantum Image processing algorithms comparison using datasets of machine learning based applications

Modern finance through quantum computing—A systematic literature review

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