BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms

Kulshrestha, Ankit; Safro, Ilya

doi:10.48550/arxiv.2204.13751

Cited by 3 publications

(3 citation statements)

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“…The VQE method is known for the presence of the Barren plateaus problem [12] which appears during the optimization part. But, in examined data because of several smaller clusters, Barren plateaus problem has not been observed.…”

Section: Remarkmentioning

confidence: 99%

Variational Quantum Eigensolver for Classification in Credit Sales Risk

Wiśniewska¹,

Sawerwain²

2023

Preprint

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In this work, a classification task is realized by the Variational Quantum Eigensolver (VQE) approach. A decision situation concerns issuing commodities in the process of credit sales -at some point a client's history may be analyzed by debt department's employee to check if the goods issuing should not be stopped due to the risk of fraud (bivalent classification). As in the classical machine learning, the data should be normalized. We describe the two-step process of data normalization to write each observation as a quantum state. The VQE approach allows training the parameters of a quantum circuit (so-called ansatz) to output patternstates for each class. Observations are categorized to classes with the use of the SWAP-test. The obtained solution is compact and requires only logarithmically increasing number of qubits (due to the exponential capacity of quantum registers) -we also present alternative classical solutions which, in fact, are quite complex. All calculations, plots, and comparisons were implemented and conduced in Python language environment. Source codes of the simulations for individual examples of quantum classification can be found in the source code repository. The version of a simulator directly used in the article is in the Zenodo version of the source code.

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Section: Remarkmentioning

confidence: 99%

Variational Quantum Eigensolver for Classification in Credit Sales Risk

Wiśniewska¹,

Sawerwain²

2023

Preprint

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“…As discussed in the Refs. [15][16][17][18][19][20][21][22][23], better parameters initialization methods can help VQA reach better performance. In this section, we investigate the plain training where the initial parameters of 10% two-qubit gates are sampled uniformly from [0, 2π] and the remaining initial parameters are set to 0.…”

Section: Comparsion With the Initialization Strategymentioning

confidence: 99%

“…Even the gradient-free optimization approaches are also suppressed by the barren plateaus [14]. In order to mitigate the barren plateaus and achieve better performance from VQA, a series of strategies have been proposed, including parameters initialization methods [15][16][17][18][19][20][21][22][23], local objective function [24,25] and special quantum circuit ansatz [26][27][28][29][30][31].…”

mentioning

confidence: 99%

Training variational quantum algorithms with random gate activation

Liu¹,

Zhang²,

Jian³

et al. 2023

Preprint

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Variational quantum algorithms (VQAs) hold great potentials for near-term applications and are promising to achieve quantum advantage on practical tasks. However, VQAs suffer from severe barren plateau problem as well as have a large probability of being trapped in local minima. In this Letter, we propose a novel training algorithm with random quantum gate activation for VQAs to efficiently address these two issues. This new algorithm processes effectively much fewer training parameters than the conventional plain optimization strategy, which efficiently mitigates barren plateaus with the same expressive capability. Additionally, by randomly adding two-qubit gates to the circuit ansatz, the optimization trajectories can escape from local minima and reach the global minimum more frequently due to more sources of randomness. In real quantum experiments, the new training algorithm can also reduce the quantum computational resources required and be more quantum noise resilient. We apply our training algorithm to solve variational quantum simulation problems for ground states and present convincing results that showcase the advantages of our novel strategy where better performance is achieved by the combination of mitigating barren plateaus, escaping from local minima, and reducing the effect of quantum noises. We further propose that the entanglement phase transition could be one underlying reason why our RA training is so effective.

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A quantum information theoretic view on a deep quantum neural network

Hiesmayr

2024

International Workshop on Machine Learning and Quantum Computing Applications in Medicine and Physics: Wmlq2022

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BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms

Cited by 3 publications

References 0 publications

Variational Quantum Eigensolver for Classification in Credit Sales Risk

Variational Quantum Eigensolver for Classification in Credit Sales Risk

Training variational quantum algorithms with random gate activation

A quantum information theoretic view on a deep quantum neural network

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