78 citations · 244 across the 16 of their papers we have counts for
18 papers
Pushing beyond the memory barrier in propagation algorithms with randomization
Manuel S. Rudolph, Ricard Puig, Slimane Thabet +1
Performant classical tools for simulating quantum circuits are important for benchmarking and supporting upcoming quantum devices. Pauli propagation offers promising capabilities,…
Dissipation accelerates quantum and classical simulation of open-system dynamics
Armando Angrisani, Ricard Puig, Yanting Teng +1
Simulating open quantum systems reveals how environmental coupling shapes relaxation, excitation transport, and the dynamics of quantum correlations. On quantum hardware, dissipati…
The cost of simulating classically tractable quantum circuits and dynamics
Su Yeon Chang, Supanut Thanasilp, Zoë Holmes +1
Determining whether a quantum evolution can be efficiently simulated classically is central to understanding the boundary between classical and quantum computation. However, polyno…
Double-bracket quantum algorithms for thermal state preparation
Andrew Wright, Reyhaneh Aghaei Saem, Supanut Thanasilp +2
We propose quantum algorithms for preparing thermal states via the simulation of the thermofield double states. The key idea is to leverage double-bracket quantum algorithms to imp…
Leveraging Symmetry Merging in Pauli Propagation
Yanting Teng, Su Yeon Chang, Manuel S. Rudolph +1
We introduce a symmetry-adapted framework for simulating quantum dynamics based on Pauli propagation. When a quantum circuit possesses a symmetry, many Pauli strings evolve redunda…
Pauli Propagation: A Computational Framework for Simulating Quantum Systems
Manuel S. Rudolph, Tyson Jones, Yanting Teng +2
Classical methods to simulate quantum systems are not only a key element of the physicist's toolkit for studying many-body models but are also increasingly important for verifying…
Role of scrambling and noise in temporal information processing with quantum systems
Weijie Xiong, Zoë Holmes, Armando Angrisani +3
Scrambling quantum systems have attracted attention as effective substrates for temporal information processing. Here we consider a quantum reservoir processing framework that capt…
A unifying account of warm start guarantees for patches of quantum landscapes
Hela Mhiri, Ricard Puig, Sacha Lerch +4
Barren plateaus are fundamentally a statement about quantum loss landscapes on average but there can, and generally will, exist patches of barren plateau landscapes with substantia…
Simulating quantum circuits with arbitrary local noise using Pauli Propagation
Armando Angrisani, Antonio A. Mele, Manuel S. Rudolph +2
We present a polynomial-time classical algorithm for estimating expectation values of arbitrary observables on typical quantum circuits under any incoherent local noise, including…
Efficient quantum-enhanced classical simulation for patches of quantum landscapes
Sacha Lerch, Ricard Puig, Manuel S. Rudolph +5
Understanding the capabilities of classical simulation methods is key to identifying where quantum computers are advantageous. Not only does this ensure that quantum computers are…
Classically estimating observables of noiseless quantum circuits
Armando Angrisani, Alexander Schmidhuber, Manuel S. Rudolph +3
We present a classical algorithm based on Pauli propagation for estimating expectation values of arbitrary observables on random unstructured quantum circuits across all circuit ar…
Quantum Convolutional Neural Networks are Effectively Classically Simulable
Pablo Bermejo, Paolo Braccia, Manuel S. Rudolph +3
Quantum Convolutional Neural Networks (QCNNs) are widely regarded as a promising model for Quantum Machine Learning (QML). In this work we tie their heuristic success to two facts.…
Variational quantum simulation: a case study for understanding warm starts
Ricard Puig, Marc Drudis, Supanut Thanasilp +1
The barren plateau phenomenon, characterized by loss gradients that vanish exponentially with system size, poses a challenge to scaling variational quantum algorithms. Here we expl…
On fundamental aspects of quantum extreme learning machines
Weijie Xiong, Giorgio Facelli, Mehrad Sahebi +4
Quantum Extreme Learning Machines (QELMs) have emerged as a promising framework for quantum machine learning. Their appeal lies in the rich feature map induced by the dynamics of a…
Does provable absence of barren plateaus imply classical simulability?
M. Cerezo, Martin Larocca, Diego García-Martín +9
A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and…
Classical surrogate simulation of quantum systems with LOWESA
Manuel S. Rudolph, Enrico Fontana, Zoë Holmes +1
We introduce LOWESA as a classical algorithm for faithfully simulating quantum systems via a classically constructed surrogate expectation landscape. After an initial overhead to b…
Trainability barriers and opportunities in quantum generative modeling
Manuel S. Rudolph, Sacha Lerch, Supanut Thanasilp +5
Quantum generative models provide inherently efficient sampling strategies and thus show promise for achieving an advantage using quantum hardware. In this work, we investigate the…
Exponential concentration in quantum kernel methods
Supanut Thanasilp, Samson Wang, M. Cerezo +1
Kernel methods in Quantum Machine Learning (QML) have recently gained significant attention as a potential candidate for achieving a quantum advantage in data analysis. Among other…