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Variational quantum simulation: a case study for understanding warm starts
The barren plateau phenomenon, characterized by loss gradients that vanish exponentially with system size, poses a challenge to scaling variational quantum algorithms.
On fundamental aspects of quantum extreme learning machines
Notably, the expressivity of QELMs is fundamentally limited by the number of Fourier frequencies and the number of observables, while the complexity of the prediction hinges on the reservoir.
Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing
A large amount of effort has recently been put into understanding the barren plateau phenomenon.
A Unified Framework for Trace-induced Quantum Kernels
In this work we combine all trace-induced quantum kernels, including the commonly-used global fidelity and local projected quantum kernels, into a common framework.
Trainability barriers and opportunities in quantum generative modeling
In this work, we investigate the barriers to the trainability of quantum generative models posed by barren plateaus and exponential loss concentration.
Exponential concentration in quantum kernel methods
Lastly, we show that when dealing with classical data, training a parametrized data embedding with a kernel alignment method is also susceptible to exponential concentration.
Subtleties in the trainability of quantum machine learning models
In this work we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models.
Quantum supremacy and quantum phase transitions
Demonstrating the ability of existing quantum platforms to perform certain computational tasks intractable to classical computers represents a cornerstone in quantum computing.
Quantum Physics Disordered Systems and Neural Networks Quantum Gases Statistical Mechanics
