Search Results for author:
Found 13 papers, 0 papers with code
Hamiltonian Property Testing
Our work initiates the study of property testing for quantum Hamiltonians, demonstrating that a broad class of Hamiltonian properties is efficiently testable even with limited quantum capabilities, and positioning Hamiltonian testing as an independent area of research alongside Hamiltonian learning.
Learning quantum states and unitaries of bounded gate complexity
While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers.
Classical Verification of Quantum Learning
Finally, we showcase two general scenarios in learning and verification in which quantum mixture-of-superpositions examples do not lead to sample complexity improvements over classical data.
The power and limitations of learning quantum dynamics incoherently
Quantum process learning is emerging as an important tool to study quantum systems.
Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrix
We show that a quantum memory allows to efficiently solve the following tasks: (a) learning the Pauli transfer matrix of an arbitrary $\mathcal{N}$, (b) predicting expectation values of bounded Pauli-sparse observables measured on the output of an arbitrary $\mathcal{N}$ upon input of a Pauli-sparse state, and (c) predicting expectation values of arbitrary bounded observables measured on the output of an unknown $\mathcal{N}$ with sparse Pauli transfer matrix upon input of an arbitrary state.
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.
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.
Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i. e., generalizing).
Encoding-dependent generalization bounds for parametrized quantum circuits
However, none of these generalization bounds depend explicitly on how the classical input data is encoded into the PQC.
From Undecidability of Non-Triviality and Finiteness to Undecidability of Learnability
Our work shows that undecidability appears in the theoretical foundations of artificial intelligence: There is no one-size-fits-all algorithm for deciding whether a machine learning model can be successful.
Binary Classification with Classical Instances and Quantum Labels
In particular, we see that the sample complexity is the same as in the classical binary classification task w. r. t.
Pseudo-dimension of quantum circuits
We characterize the expressive power of quantum circuits with the pseudo-dimension, a measure of complexity for probabilistic concept classes.
Quantum Learning Boolean Linear Functions w.r.t. Product Distributions
With our analysis we contribute to a more quantitative understanding of the power and limitations of quantum training data for learning classical functions.
