35 citations · 42 across the 16 of their papers we have counts for
9 papers · 1 filter
Structural Conditions for Distributed Quantum Advantage
Sabina Drăgoi, María Gragera Garcés, Lirandë Pira
Circuit cutting runs a quantum computation on processors smaller than the circuit, at the price of classical knitting whose cost grows exponentially with the number of cut gates. K…
Optimal query complexity for fractional quantum evolution
Anthony Yuezhang Liu, Adam Wesołowski, Jayne Thompson +2
Given oracle access to an unknown unitary , the fractional query problem asks how many queries are required to implement a noninteger power , , when…
Weighted Quantum Signal Processing: Low-Depth Polynomial Approximation with Applications to Kolmogorov-Arnold Networks
Rohit Sarma Sarkar, Rupayan Bhattacharjee, Elias F. Combarro +5
Quantum Signal Processing is a powerful quantum framework for generating and approximating univariate polynomials. However, QSP is often limited by circuit-depth bottlenecks and pa…
Quantum Hamiltonian Evolution for Coherent Quantum Learning
Ignacio B. Acedo, Javier Gonzalez-Conde, Pablo Rodriguez-Grasa +2
We introduce Coherent Quantum Learning (CQL), a training framework for quantum learning models in which the model parameters are quantum degrees of freedom evolved under a Hamilton…
Entanglement geometry separates circuit cutting, classical hardness, and trainability
Maria Gragera Garces, Sabina Drăgoi, Lirandë Pira
Circuit cutting promises to scale quantum computations beyond current hardware, but variational quantum advantage also requires low cutting overhead, classical hardness, and traina…
Quantum Topological Data Encoding
Adam Wesołowski, Dimitrios Thanos, Daniel Leykam +1
Many datasets encountered across a wide range of domains possess rich geometric and topological structure that is difficult to capture using conventional vector-based representatio…
The Cost of Removing Tunability in Quantum Data Re-Uploading
Anthony Yuezhang Liu, Lirandë Pira
Fixed encoding data re-uploading quantum circuits provide a striking example of universality emerging from a highly constrained architecture. However, universality alone is insuffi…
Quantum ring all-reduce: communication and privacy advantages for distributed learning
María Gragera Garcés, Lirandë Pira
Machine learning models have scaled to unprecedented sizes, making training across distributed devices the de facto standard in the field. In this work, we explore how quantum comm…
Fundamentals of Quantum Machine Learning and Robustness
Lirandë Pira, Patrick Rebentrost
Quantum machine learning (QML) sits at the intersection of quantum computing and classical machine learning, offering the prospect of new computational paradigms and advantages for…