627 citations · 1.6k across the 17 of their papers we have counts for
17 papers
Exponentially many initializations to avoid barren plateaus
Ankit Kulshrestha, Ricard Puig, Diego García-Martín +4
Barren plateaus are stated as an average-case phenomenon: pick an ansatz, initialize it naively, and concentration follows. This has led to the common view that a potential cure fo…
Barren Plateaus in Variational Quantum Computing
Martin Larocca, Supanut Thanasilp, Samson Wang +7
Variational quantum computing offers a flexible computational paradigm with applications in diverse areas. However, a key obstacle to realizing their potential is the Barren Platea…
High-fidelity dimer excitations using quantum hardware
Norhan M. Eassa, Joe Gibbs, Zoe Holmes +7
Many-body entangled quantum spin systems exhibit emergent phenomena such as topological quantum spin liquids with distinct excitation spectra accessed in inelastic neutron scatteri…
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…
Large-scale simulations of Floquet physics on near-term quantum computers
Timo Eckstein, Refik Mansuroglu, Piotr Czarnik +5
Periodically driven quantum systems exhibit a diverse set of phenomena but are more challenging to simulate than their equilibrium counterparts. Here, we introduce the Quantum High…
Quantum Mixed State Compiling
Nic Ezzell, Elliott M. Ball, Aliza U. Siddiqui +4
The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states wh…
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…
The quantum low-rank approximation problem
Nic Ezzell, Zoë Holmes, Patrick J. Coles
We consider a quantum version of the famous low-rank approximation problem. Specifically, we consider the distance between two normalized quantum states, and , wher…
Quantum simulation of operator spreading in the chaotic Ising model
Michael R. Geller, Andrew Arrasmith, Zoë Holmes +3
There is great interest in using near-term quantum computers to simulate and study foundational problems in quantum mechanics and quantum information science, such as the scramblin…
Experimental Quantum Learning of a Spectral Decomposition
Michael R. Geller, Zoë Holmes, Patrick J. Coles +1
Currently available quantum hardware allows for small scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum comp…
Equivalence of quantum barren plateaus to cost concentration and narrow gorges
Andrew Arrasmith, Zoë Holmes, M. Cerezo +1
Optimizing parameterized quantum circuits (PQCs) is the leading approach to make use of near-term quantum computers. However, very little is known about the cost function landscape…
Long-time simulations with high fidelity on quantum hardware
Joe Gibbs, Kaitlin Gili, Zoë Holmes +5
Moderate-size quantum computers are now publicly accessible over the cloud, opening the exciting possibility of performing dynamical simulations of quantum systems. However, while…
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Zoë Holmes, Kunal Sharma, M. Cerezo +1
Parameterized quantum circuits serve as ansätze for solving variational problems and provide a flexible paradigm for programming near-term quantum computers. Ideally, such ansätze…
Variational Hamiltonian Diagonalization for Dynamical Quantum Simulation
Benjamin Commeau, M. Cerezo, Zoë Holmes +3
Dynamical quantum simulation may be one of the first applications to see quantum advantage. However, the circuit depth of standard Trotterization methods can rapidly exceed the coh…
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
Kunal Sharma, M. Cerezo, Zoë Holmes +3
The no-free-lunch (NFL) theorem is a celebrated result in learning theory that limits one's ability to learn a function with a training data set. With the recent rise of quantum ma…
Variational Fast Forwarding for Quantum Simulation Beyond the Coherence Time
Cristina Cirstoiu, Zoe Holmes, Joseph Iosue +3
Trotterization-based, iterative approaches to quantum simulation are restricted to simulation times less than the coherence time of the quantum computer, which limits their utility…