Robust implementation of generative modeling with parametrized quantum circuits

Leyton-Ortega, Vicente; Perdomo-Ortiz, Alejandro; Perdómo, Oscar

doi:10.1007/s42484-021-00040-2

Cited by 22 publications

(22 citation statements)

References 15 publications

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“…However, increasing expressibility and entanglement capacity of both models resulted in worse performance in both cases. In this way, our results are consistent with results of Leyton-Ortega et al (2021). This behaviour is thought to be the result of Barren Plateau formation (McClean et al 2018).…”

Section: Resultssupporting

confidence: 93%

“…There are contrasting results in the literature on how expressibility and entanglement capacities affect the training performance. Recently, Hubregtsen et al (2021) showed a positive correlation between expressibility and accuracy, while Leyton-Ortega et al (2021) showed the opposite. They found that more expressive models perform worse and also overfit more.…”

Section: Resultsmentioning

confidence: 99%

“…In order to achieve a good training performance, choosing a good combination of IEC and PQC is essential. Although there are many practical and theoretical work to understand this better (Sim et al 2019;Leyton-Ortega et al 2021;Hubregtsen et al 2021), our current understanding of which combination works for which task is still limited (Schuld et al 2021). We therefore try to cover a range of PQCs and fix the IEC to a specific angle encoding to provide more controlled results.…”

Section: The Hybrid Neural Networkmentioning

confidence: 99%

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Hybrid quantum classical graph neural networks for particle track reconstruction

Tüysüz

Rieger

Novotny³

et al. 2021

Quantum Mach. Intell.

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The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC (HL-LHC). This increase in luminosity will significantly increase the number of particles interacting with the detector. The interaction of particles with a detector is referred to as “hit”. The HL-LHC will yield many more detector hits, which will pose a combinatorial challenge by using reconstruction algorithms to determine particle trajectories from those hits. This work explores the possibility of converting a novel graph neural network model, that can optimally take into account the sparse nature of the tracking detector data and their complex geometry, to a hybrid quantum-classical graph neural network that benefits from using variational quantum layers. We show that this hybrid model can perform similar to the classical approach. Also, we explore parametrized quantum circuits (PQC) with different expressibility and entangling capacities, and compare their training performance in order to quantify the expected benefits. These results can be used to build a future road map to further develop circuit-based hybrid quantum-classical graph neural networks.

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Section: Resultssupporting

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: The Hybrid Neural Networkmentioning

confidence: 99%

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Hybrid quantum classical graph neural networks for particle track reconstruction

Tüysüz

Rieger

Novotny³

et al. 2021

Quantum Mach. Intell.

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“…These models are classically implemented using deep neural network architectures. In recent years, however, hybrid quantum-classical approaches based on parameterised quantum circuits have also gained traction [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Generative Modellingmentioning

confidence: 99%

“…Given their flexibility, VQAs have been proposed for a vast array of applications. Of particular relevance are applications of VQAs to machine learning problems, including classification [5][6][7][8][9][10], data compression [11][12][13], clustering [14], generative modelling [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32] , and inference [33].…”

Section: Introductionmentioning

confidence: 99%

F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits

Leadbeater

Louis

Coyle

et al. 2021

Entropy

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Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit born machine. In particular, we consider training a quantum circuit born machine using f-divergences. We first discuss the adversarial framework for generative modelling, which enables the estimation of any f-divergence in the near term. Based on this capability, we introduce two heuristics which demonstrably improve the training of the born machine. The first is based on f-divergence switching during training. The second introduces locality to the divergence, a strategy which has proved important in similar applications in terms of mitigating barren plateaus. Finally, we discuss the long-term implications of quantum devices for computing f-divergences, including algorithms which provide quadratic speedups to their estimation. In particular, we generalise existing algorithms for estimating the Kullback–Leibler divergence and the total variation distance to obtain a fault-tolerant quantum algorithm for estimating another f-divergence, namely, the Pearson divergence.

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