747 citations · 1.9k across the 19 of their papers we have counts for
19 papers
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…
Query complexities of quantum channel discrimination and estimation: A unified approach
Zixin Huang, Johannes Jakob Meyer, Theshani Nuradha +1
The goal of quantum channel discrimination and estimation is to determine the identity of an unknown channel from a discrete or continuous set, respectively. The query complexity o…
How hard is it to verify a classical shadow?
Georgios Karaiskos, Dorian Rudolph, Johannes Jakob Meyer +2
Classical shadows are succinct classical representations of quantum states which allow one to encode a set of properties P of a quantum state rho, while only requiring measurements…
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…
Tomography of parametrized quantum states
Franz J. Schreiber, Jens Eisert, Johannes Jakob Meyer
Characterizing quantum systems is a fundamental task that enables the development of quantum technologies. Various approaches, ranging from full tomography to instances of classica…
Single-shot quantum machine learning
Erik Recio-Armengol, Jens Eisert, Johannes Jakob Meyer
Quantum machine learning aims to improve learning methods through the use of quantum computers. If it is to ever realize its potential, many obstacles need to be overcome. A partic…
Variational quantum algorithm for enhanced continuous variable optical phase sensing
Jens A. H. Nielsen, Mateusz Kicinski, Tummas N. Arge +7
Variational quantum algorithms (VQAs) are hybrid quantum-classical approaches used for tackling a wide range of problems on noisy intermediate-scale quantum (NISQ) devices. Testing…
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…
Quantum metrology in the finite-sample regime
Johannes Jakob Meyer, Sumeet Khatri, Daniel Stilck França +2
In quantum metrology, one of the major applications of quantum technologies, the ultimate precision of estimating an unknown parameter is often stated in terms of the Cramér-Rao bo…
Exponentially tighter bounds on limitations of quantum error mitigation
Yihui Quek, Daniel Stilck França, Sumeet Khatri +2
Quantum error mitigation has been proposed as a means to combat unwanted and unavoidable errors in near-term quantum computing without the heavy resource overheads required by faul…
Classical surrogates for quantum learning models
Franz J. Schreiber, Jens Eisert, Johannes Jakob Meyer
The advent of noisy intermediate-scale quantum computers has put the search for possible applications to the forefront of quantum information science. One area where hopes for an a…
Exploiting symmetry in variational quantum machine learning
Johannes Jakob Meyer, Marian Mularski, Elies Gil-Fuster +4
Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on f…
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…
Training Quantum Embedding Kernels on Near-Term Quantum Computers
Thomas Hubregtsen, David Wierichs, Elies Gil-Fuster +3
Kernel methods are a cornerstone of classical machine learning. The idea of using quantum computers to compute kernels has recently attracted attention. Quantum embedding kernels (…
Fisher Information in Noisy Intermediate-Scale Quantum Applications
Johannes Jakob Meyer
The recent advent of noisy intermediate-scale quantum devices, especially near-term quantum computers, has sparked extensive research efforts concerned with their possible applicat…
The effect of data encoding on the expressive power of variational quantum machine learning models
Maria Schuld, Ryan Sweke, Johannes Jakob Meyer
Quantum computers can be used for supervised learning by treating parametrised quantum circuits as models that map data inputs to predictions. While a lot of work has been done to…
A variational toolbox for quantum multi-parameter estimation
Johannes Jakob Meyer, Johannes Borregaard, Jens Eisert
With an ever-expanding ecosystem of noisy and intermediate-scale quantum devices, exploring their possible applications is a rapidly growing field of quantum information science. I…
Stochastic gradient descent for hybrid quantum-classical optimization
Ryan Sweke, Frederik Wilde, Johannes Meyer +4
Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of…
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Ville Bergholm, Josh Izaac, Maria Schuld +65
PennyLane is a Python 3 software framework for differentiable programming of quantum computers. The library provides a unified architecture for near-term quantum computing devices,…