485 citations · 717 across the 22 of their papers we have counts for
23 papers
Anti-symmetrization is cheap and useful
Adam Bene Watts, John Bostanci, Matthias C. Caro +3
We consider the computational task of anti-symmetrization of an unknown mixed quantum state: Given iid copies of a rank- qudit state $ρ=\sum_{i=1}^r λ_i |φ_i\rangle\!\langleφ_i|…
Sample-optimal learning of stabilizer states
Rebecca Chang, Matthias C. Caro, Martin Larocca +1
It is well-known that learning a pure -qubit stabilizer state both requires, and can be accomplished with, access to a number of copies of linear in .…
Parameterised graph theory for tensor networks: entanglement rerouting, structural simplification, and agnostic tomography
Matthias C. Caro, Natalie McHugh, Sergii Strelchuk
Parameterised graph theory studies how the complexity of graph-theoretic problems depends on structural parameters of the input graph. This perspective has proved useful in analysi…
Cloning is as Hard as Learning for Stabilizer States
Nikhil Bansal, Matthias C. Caro, Gaurav Mahajan
The impossibility of simultaneously cloning non-orthogonal states lies at the foundations of quantum theory. Even when allowing for approximation errors, cloning an arbitrary unkno…
Certifying and learning local quantum Hamiltonians
Andreas Bluhm, Matthias C. Caro, Francisco Escudero Gutiérrez +4
In this work, we study the problems of certifying and learning quantum -local Hamiltonians, for a constant . Our main contributions are as follows: - Certification of Hamilto…
A PAC-Bayesian approach to generalization for quantum models
Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert +3
Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacit…
Interactive proofs for verifying (quantum) learning and testing
Matthias C. Caro, Jens Eisert, Marcel Hinsche +3
We consider the problem of testing and learning from data in the presence of resource constraints, such as limited memory or weak data access, which place limitations on the effici…
Learning quantum states and unitaries of bounded gate complexity
Haimeng Zhao, Laura Lewis, Ishaan Kannan +3
While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers. Given that states and unitaries appearing in Nature are of…
Learning and simulating bosonic systems via finite-energy locality
Tim Möbus, Andreas Bluhm, Matthias C. Caro +2
Bosonic devices promise applications in simulation, sensing and quantum error correction, but infinite-dimensional local Hilbert spaces obstruct the locality tools that make qubit…
Classical Verification of Quantum Learning
Matthias C. Caro, Marcel Hinsche, Marios Ioannou +2
Quantum data access and quantum processing can make certain classically intractable learning tasks feasible. However, quantum capabilities will only be available to a select few in…
The power and limitations of learning quantum dynamics incoherently
Sofiene Jerbi, Joe Gibbs, Manuel S. Rudolph +4
Quantum process learning is emerging as an important tool to study quantum systems. While studied extensively in coherent frameworks, where the target and model system can share qu…
Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrix
Matthias C. Caro
Learning about physical systems from quantum-enhanced experiments, relying on a quantum memory and quantum processing, can outperform learning from experiments in which only classi…
Dynamical simulation via quantum machine learning with provable generalization
Joe Gibbs, Zoë Holmes, Matthias C. Caro +5
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 e…
Out-of-distribution generalization for learning quantum dynamics
Matthias C. Caro, Hsin-Yuan Huang, Nicholas Ezzell +5
Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution gen…
On the generators of quantum dynamical semigroups with invariant subalgebras
Markus Hasenöhrl, Matthias C. Caro
The problem of characterizing GKLS-generators and CP-maps with an invariant appeared in different guises in the literature. We prove two unifying results which hold even for weakly…
Generalization in quantum machine learning from few training data
Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo +4
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a test…
Quantum and classical dynamical semigroups of superchannels and semicausal channels
Markus Hasenöhrl, Matthias C. Caro
Quantum devices are subject to natural decay. We propose to study these decay processes as the Markovian evolution of quantum channels, which leads us to dynamical semigroups of su…
Encoding-dependent generalization bounds for parametrized quantum circuits
Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer +2
A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical…
From Undecidability of Non-Triviality and Finiteness to Undecidability of Learnability
Matthias C. Caro
Machine learning researchers and practitioners steadily enlarge the multitude of successful learning models. They achieve this through in-depth theoretical analyses and experientia…
Necessary Criteria for Markovian Divisibility of Linear Maps
Matthias C. Caro, Benedikt Graswald
Characterizing those quantum channels that correspond to Markovian time evolutions is an open problem in quantum information theory, even different notions of quantum Markovianity…
Binary Classification with Classical Instances and Quantum Labels
Matthias C. Caro
In classical statistical learning theory, one of the most well studied problems is that of binary classification. The information-theoretic sample complexity of this task is tightl…
Pseudo-dimension of quantum circuits
Matthias C. Caro, Ishaun Datta
We characterize the expressive power of quantum circuits with the pseudo-dimension, a measure of complexity for probabilistic concept classes. We prove pseudo-dimension bounds on t…
Quantum Learning Boolean Linear Functions w.r.t. Product Distributions
Matthias C. Caro
The problem of learning Boolean linear functions from quantum examples w.r.t. the uniform distribution can be solved on a quantum computer using the Bernstein-Vazirani algorithm. A…