Systematic Literature Review: Quantum Machine Learning and its applications

García, David Peral; Cruz-Benito, Juan; García‐Peñalvo, Francisco José

doi:10.48550/arxiv.2201.04093

Cited by 19 publications

(18 citation statements)

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“…The QC is a future device that should enable the exponential speedup for certain kinds of combinatorial computation. The use of quantum computing for ML has attracted significant attention recently, and quantum algorithms oriented to the general-purpose ML have been extensively studied, emerging as a subfield referred to as quantum machine learning (QML) [29][30][31][32][33][34][35][36] . There are numerous earlier developments of QML that underlie this work.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Encoding of Molecular Wavefunction for Quantum Computing

Hagai

Sugiyama

Tsuda

et al. 2023

Preprint

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Artificial neural networks (ANNs) for material modeling have received significant interest. We recently reported an adaptation of ANNs based on Boltzmann machine (BM) architectures to an ansatz of the multiconfigurational many-electron wavefunction, designated neural-network quantum state (NQS), for quantum chemistry calculations [Yang et al., J. Chem. Theory Comput., 2020, 16, 3513--3529]. Here, this study presents its extended formalism to a quantum algorithm that enables the preparation of the NQS through quantum gates. The descriptors of the ANN model, which are chosen as the occupancies of electronic configurations, are quantum-mechanically represented by qubits. Our algorithm may thus bring potential advantages over classical sampling-based computation employed in the previous studies. The NQS can be accurately formed using quantum-native procedures. Still, the training of the model in terms of energy minimization is efficiently performed on a classical computer; thus, our approach is a class of variational quantum eigensolver. The BM models are related to the Gibbs distribution, and our preparation procedures exploit techniques of quantum phase estimation but with no Hamiltonian evolution. The proposed algorithm is assessed by implementing it on a quantum computer simulator. Illustrative molecular calculations at the complete-active-space configuration interaction level of theory are displayed, confirming consistency with the accuracy of our previous classical approaches.

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Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Encoding of Molecular Wavefunction for Quantum Computing

Hagai

Sugiyama

Tsuda

et al. 2023

Preprint

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“…Until nowadays, the size of quantum devices is limited, and the computation is not entirely fault-tolerant, i.e., an efficient quantum error correction is not available yet. But very recently, the availability of Noisy Intermediate Scale Quantum (NISQ) devices allows testing the performances of both quantum and hybrid algorithms to explore new computational paradigms that find various applications in fields such as chemistry, biology [13][14][15] , and artificial intelligence 16 . However, a detailed study of the performances on PO of various NISQ devices is still missing.…”

Section: Introductionmentioning

confidence: 99%

Best practices for portfolio optimization by quantum computing, experimented on real quantum devices

Buonaiuto¹,

Gargiulo²,

Pietro³

et al. 2023

Preprint

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In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.

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“…These are required for calculating intensities in various areas of spectroscopy [6][7][8][9][10][11] and for response functions in scattering experiments and condensed matter [12][13][14][15]. Additionally, as vector-matrix-vector products a t A b (or a t A a) are often relevant to classical linear algebra problems, transition probability subroutines may be useful in quantum linear algebra [16][17][18][19][20][21], including for classical partial differential equations [22][23][24], finance [25,26], and quantum machine learning [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Improved resource-tunable near-term quantum algorithms for transition probabilities, with applications in physics and variational quantum linear algebra

Sawaya¹,

Huh²

2022

Preprint

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Systematic Literature Review: Quantum Machine Learning and its applications

Cited by 19 publications

References 0 publications

Artificial Neural Network Encoding of Molecular Wavefunction for Quantum Computing

Artificial Neural Network Encoding of Molecular Wavefunction for Quantum Computing

Best practices for portfolio optimization by quantum computing, experimented on real quantum devices

Improved resource-tunable near-term quantum algorithms for transition probabilities, with applications in physics and variational quantum linear algebra

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