23 citations · 69 across the 18 of their papers we have counts for
20 papers
Universal Bounds for Out-of-Distribution Unitary Learning
Joachim Favre, Armando Angrisani, Zoë Holmes
How much can the action of an unknown unitary dynamics on one family of states reveal about its action elsewhere? We develop a general framework for out-of-distribution quantum dyn…
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 Complexity of Dynamical Correlators: Operator Shadows and Exponential Learning Separations
Shao-Hen Chiew, Armando Angrisani, Zoe Holmes
Quantum platforms can realize many-body dynamics beyond classical simulation yet complete readout remains intractable: the cost of extracting accessible information scales exponent…
Coherent-State Propagation: A Computational Framework for Simulating Bosonic Quantum Systems
Nikita Guseynov, Zoë Holmes, Armando Angrisani
We introduce coherent-state propagation, a computational framework for simulating bosonic systems. We focus on bosonic circuits composed of displaced linear optics augmented by Ker…
Quantum simulation in the Heisenberg picture via vectorization
Shao-Hen Chiew, Armando Angrisani, Zoë Holmes +1
We present a general framework for simulating quantum systems in the Heisenberg picture on quantum hardware. Based on the vectorization map, our framework fully exploits the mappin…
Thermal State Simulation with Pauli and Majorana Propagation
Manuel S. Rudolph, Armando Angrisani, Andrew Wright +3
We introduce a propagation-based approach to thermal state simulation by adapting Pauli and Majorana propagation to imaginary-time evolution in the Schrödinger picture. Our key obs…
When quantum resources backfire: Non-gaussianity and symplectic coherence in noisy bosonic circuits
Varun Upreti, Ulysse Chabaud, Zoë Holmes +1
Analyzing the impact of noise is of fundamental importance to understand the advantages provided by quantum systems. While the classical simulability of noisy discrete-variable sys…
On the Complexity of Quantum States and Circuits from the Orthogonal and Symplectic Groups
Oxana Shaya, Zoë Holmes, Christoph Hirche +1
Understanding the complexity of quantum states and circuits is a central challenge in quantum information science, with broad implications in many-body physics, high-energy physics…
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…
Efficient simulation of parametrized quantum circuits under non-unital noise through Pauli backpropagation
Victor Martinez, Armando Angrisani, Ekaterina Pankovets +2
As quantum devices continue to grow in size but remain affected by noise, it is crucial to determine when and how they can outperform classical computers on practical tasks. A cent…
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…
Noise-induced shallow circuits and absence of barren plateaus
Antonio Anna Mele, Armando Angrisani, Soumik Ghosh +4
Motivated by realistic hardware considerations of the pre-fault-tolerant era, we comprehensively study the impact of uncorrected noise on quantum circuits. We first show that in th…
Learning unitaries with quantum statistical queries
Armando Angrisani
We propose several algorithms for learning unitary operators from quantum statistical queries with respect to their Choi-Jamiolkowski state. Quantum statistical queries capture the…
A unifying framework for differentially private quantum algorithms
Armando Angrisani, Mina Doosti, Elham Kashefi
Differential privacy is a widely used notion of security that enables the processing of sensitive information. In short, differentially private algorithms map "neighbouring" inputs…
Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms
Armando Angrisani, Mina Doosti, Elham Kashefi
Differential privacy provides a theoretical framework for processing a dataset about users, in a way that the output reveals a minimal information about any single user. Such n…
Quantum Local Differential Privacy and Quantum Statistical Query Model
Armando Angrisani, Elham Kashefi
Quantum statistical queries provide a theoretical framework for investigating the computational power of a learner with limited quantum resources. This model is particularly releva…
Information-Theoretic Limits of Quantum Learning via Data Compression
Armando Angrisani, Brian Coyle, Elham Kashefi
Understanding the power of quantum data in machine learning is central to many proposed applications of quantum technologies. While access to quantum data can offer exponential adv…