
PBScalculus: A Graphical Language for QuantumControlled Computations
We introduce the PBScalculus to represent and reason on quantum computa...
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Diagrammatic Differentiation for Quantum Machine Learning
We introduce diagrammatic differentiation for tensor calculus by general...
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Barren plateaus in quantum neural network training landscapes
Many experimental proposals for noisy intermediate scale quantum devices...
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Classifying Complexity with the ZXCalculus: Jones Polynomials and Potts Partition Functions
The ZXcalculus is a graphical language which allows for reasoning about...
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Quantum computing and the brain: quantum nets, dessins d'enfants and neural networks
In this paper, we will discuss a formal link between neural networks and...
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A SuperpositionBased Calculus for Quantum Diagrammatic Reasoning and Beyond
We introduce a class of rooted graphs which allows one to encode various...
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Statistical Tests and Confidential Intervals as Thresholds for Quantum Neural Networks
Some basic quantum neural networks were analyzed and constructed in the ...
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Analyzing the barren plateau phenomenon in training quantum neural network with the ZXcalculus
In this paper, we propose a general scheme to analyze the barren plateau phenomenon in training quantum neural networks with the ZXcalculus. More precisely, we extend the barren plateaus theorem from unitary 2design circuits to any parameterized quantum circuits under certain reasonable assumptions. The main technical contribution of this paper is representing certain integrations as ZXdiagrams and computing them with the ZXcalculus. The method is used to analyze four concrete quantum neural networks with different structures. It is shown that, for the hardware efficient ansatz and the MPSinspired ansatz, there exist barren plateaus, while for the QCNN and the tree tensor network ansatz, there exists no barren plateau.
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