activity
20182023
most citedQuantum machine learning beyond kernel methods

212 citations · 370 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI2023

Meaningful Causal Aggregation and Paradoxical Confounding

Yuchen Zhu, Kailash Budhathoki, Jonas Kuebler +1

In aggregated variables the impact of interventions is typically ill-defined because different micro-realizations of the same macro-intervention can result in different changes of…

quant-ph2023

Multi-Armed Bandits and Quantum Channel Oracles

Simon Buchholz, Jonas M. Kübler, Bernhard Schölkopf

Multi-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and…

quant-ph2021★ 212 cited

Quantum machine learning beyond kernel methods

Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup +3

Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of q…

quant-ph2021★ 6 cited

The Inductive Bias of Quantum Kernels

Jonas M. Kübler, Simon Buchholz, Bernhard Schölkopf

It has been hypothesized that quantum computers may lend themselves well to applications in machine learning. In the present work, we analyze function classes defined via quantum k…

math.ST2020★ 6 cited

Kernel Conditional Moment Test via Maximum Moment Restriction

Krikamol Muandet, Wittawat Jitkrittum, Jonas Kübler

We propose a new family of specification tests called kernel conditional moment (KCM) tests. Our tests are built on a novel representation of conditional moment restrictions in a r…

quant-ph2019★ 123 cited

An Adaptive Optimizer for Measurement-Frugal Variational Algorithms

Jonas M. Kübler, Andrew Arrasmith, Lukasz Cincio +1

Variational hybrid quantum-classical algorithms (VHQCAs) have the potential to be useful in the era of near-term quantum computing. However, recently there has been concern regardi…

quant-ph2019★ 13 cited

Quantum Mean Embedding of Probability Distributions

Jonas M. Kübler, Krikamol Muandet, Bernhard Schölkopf

The kernel mean embedding of probability distributions is commonly used in machine learning as an injective mapping from distributions to functions in an infinite dimensional Hilbe…

quant-ph2018★ 10 cited

Two-qubit causal structures and the geometry of positive qubit-maps

Jonas Kübler, Daniel Braun

We study quantum causal inference in a set-up proposed by Ried et al. [Nat. Phys. 11, 414 (2015)] in which a common-cause scenario can be mixed with a cause-effect scenario, and fo…