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Found 13 papers, 1 papers with code
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.
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.
The power and limitations of learning quantum dynamics incoherently
Quantum process learning is emerging as an important tool to study quantum systems.
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.
Dynamical simulation via quantum machine learning with provable generalization
Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated.
Out-of-distribution generalization for learning quantum dynamics
However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution.
The quantum low-rank approximation problem
We consider a quantum version of the famous low-rank approximation problem.
Equivalence of quantum barren plateaus to cost concentration and narrow gorges
Optimizing parameterized quantum circuits (PQCs) is the leading approach to make use of near-term quantum computers.
Long-time simulations with high fidelity on quantum hardware
Specifically, we simulate an XY-model spin chain on the Rigetti and IBM quantum computers, maintaining a fidelity of at least 0. 9 for over 600 time steps.
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Parameterized quantum circuits serve as ans\"{a}tze for solving variational problems and provide a flexible paradigm for programming near-term quantum computers.
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer's ability to learn a unitary process (the quantum analog of a function) with quantum training data.
