activity
20242026
collaborators

13 papers

quant-ph2026

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…

quant-ph2026

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…

quant-ph2026

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…

quant-ph2026

IQP Born Machines under Data-dependent and Agnostic Initialization Strategies

Sacha Lerch, Joseph Bowles, Ricard Puig +3

Quantum circuit Born machines based on instantaneous quantum polynomial-time (IQP) circuits are natural candidates for quantum generative modeling, both because of their probabilis…

quant-ph2026

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.…

quant-ph2026

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…