From the 1 of 13 linked papers with an AI index.
13 papers
Entanglement geometry separates circuit cutting, classical hardness, and trainability
Maria Gragera Garces, Sabina DrÄgoi, 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 WesoÅowski, Dimitrios Thanos, Daniel Leykam +1
The paper proposes a quantum topological data encoding (QTDE) framework that maps topological features of datasets into quantum states via topology‑driven quantum evolution, and sh…
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
MarÃa 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…
Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Arthur G. Rattew, Po-Wei Huang, Naixu Guo +2
Fault-tolerant Quantum Processing Units (QPUs) promise to deliver exponential speed-ups in select computational tasks, yet their integration into modern deep learning pipelines rem…
QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
Petr Ivashkov, Po-Wei Huang, Kelvin Koor +2
We introduce quantum Kolmogorov-Arnold networks (QKAN), a quantum algorithmic framework inspired by the recently proposed Kolmogorov-Arnold Networks (KAN). QKAN inherits the compos…