11 papers
Provable learning separation for predicting time-evolution of quantum many-body systems
Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert +2
Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantu…
Optimal algorithmic complexity of inference in quantum kernel methods
Elies Gil-Fuster, Seongwook Shin, Sofiene Jerbi +2
Quantum kernel methods are among the leading candidates for achieving quantum advantage in supervised learning. A key bottleneck is the cost of inference: evaluating a trained mode…
Computational relative entropy
Johannes Jakob Meyer, Asad Raza, Jacopo Rizzo +3
Our capacity to process information depends on the computational power at our disposal. Information theory captures our ability to distinguish states or communicate messages when i…
The computational two-way quantum capacity
Johannes Jakob Meyer, Jacopo Rizzo, Asad Raza +3
Quantum channel capacities are fundamental to quantum information theory. Their definition, however, does not limit the computational resources of sender and receiver. In this work…
Efficient distributed inner product estimation via Pauli sampling
Marcel Hinsche, Marios Ioannou, Sofiene Jerbi +3
Cross-platform verification is the task of comparing the output states produced by different physical platforms using solely local quantum operations and classical communication. W…
Potential and limitations of random Fourier features for dequantizing quantum machine learning
Ryan Sweke, Erik Recio-Armengol, Sofiene Jerbi +4
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning…