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Found 6 papers, 2 papers with code
Arbitrary Polynomial Separations in Trainable Quantum Machine Learning
Recent theoretical results in quantum machine learning have demonstrated a general trade-off between the expressive power of quantum neural networks (QNNs) and their trainability; as a corollary of these results, practical exponential separations in expressive power over classical machine learning models are believed to be infeasible as such QNNs take a time to train that is exponential in the model size.
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.
Enhancing Generative Models via Quantum Correlations
Generative modeling using samples drawn from the probability distribution constitutes a powerful approach for unsupervised machine learning.
Coreset Clustering on Small Quantum Computers
However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms.
Near-Term Quantum-Classical Associative Adversarial Networks
We introduce a new hybrid quantum-classical adversarial machine learning architecture called a quantum-classical associative adversarial network (QAAN).
Variational Quantum Factoring
In this work, we revisit the problem of factoring, developing an alternative to Shor's algorithm, which employs established techniques to map the factoring problem to the ground state of an Ising Hamiltonian.
Quantum Physics
