303 citations · 874 across the 12 of their papers we have counts for
12 papers
Classical shadows with arbitrary group representations
Maxwell West, Frederic Sauvage, Aniruddha Sen +6
Classical shadows (CS) has recently emerged as an important framework to efficiently predict properties of an unknown quantum state. A common strategy in CS protocols is to paramet…
Parameter-optimal unitary synthesis with flag decompositions
Korbinian Kottmann, David Wierichs, Guillermo Alonso-Linaje +1
We introduce the flag decomposition as a central tool for unitary synthesis. It lets us carve out a diagonal unitary with degrees of freedom in such a way that the remaining…
Unitary synthesis with optimal brick wall circuits
David Wierichs, Korbinian Kottmann, Nathan Killoran
We present quantum circuits with a brick wall structure using the optimal number of parameters and two-qubit gates to parametrize , and provide evidence that these circuit…
Recursive Cartan decompositions for unitary synthesis
David Wierichs, Maxwell West, Roy T. Forestano +2
Recursive Cartan decompositions (CDs) provide a way to exactly factorize quantum circuits into smaller components, making them a central tool for unitary synthesis. Here we present…
Symmetric derivatives of parametrized quantum circuits
David Wierichs, Richard D. P. East, Martín Larocca +2
Symmetries are crucial for tailoring parametrized quantum circuits to applications, due to their capability to capture the essence of physical systems. In this work, we shift the f…
Backpropagation scaling in parameterised quantum circuits
Joseph Bowles, David Wierichs, Chae-Yeun Park
The discovery of the backpropagation algorithm ranks among one of the most important moments in the history of machine learning, and has made possible the training of large-scale n…
Here comes the SU(N): multivariate quantum gates and gradients
Roeland Wiersema, Dylan Lewis, David Wierichs +2
Variational quantum algorithms use non-convex optimization methods to find the optimal parameters for a parametrized quantum circuit in order to solve a computational problem. The…
General parameter-shift rules for quantum gradients
David Wierichs, Josh Izaac, Cody Wang +1
Variational quantum algorithms are ubiquitous in applications of noisy intermediate-scale quantum computers. Due to the structure of conventional parametrized quantum gates, the ev…
Local, Expressive, Quantum-Number-Preserving VQE Ansatze for Fermionic Systems
Gian-Luca R. Anselmetti, David Wierichs, Christian Gogolin +1
We propose VQE circuit fabrics with advantageous properties for the simulation of strongly correlated ground and excited states of molecules and materials under the Jordan-Wigner m…
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 (…
Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer
David Wierichs, Christian Gogolin, Michael Kastoryano
We compare the BFGS optimizer, ADAM and Natural Gradient Descent (NatGrad) in the context of Variational Quantum Eigensolvers (VQEs). We systematically analyze their performance on…
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,…