activity
20192026
most citedGraph Neural Networks for Particle Reconstruction in High Energy Physics detectors

86 citations · 170 across the 8 of their papers we have counts for

collaborators

11 papers

quant-ph2026

Exponential quantum advantage in processing massive classical data

Haimeng Zhao, Alexander Zlokapa, Hartmut Neven +4

Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this work, we prove that a small quant…

quant-ph2024★ 1 cited

Long-range wormhole teleportation

Joseph D. Lykken, Daniel Jafferis, Alexander Zlokapa +4

We extend the protocol of Gao and Jafferis arXiv:1911.07416 to allow wormhole teleportation between two entangled copies of the Sachdev-Ye-Kitaev (SYK) model communicating only thr…

quant-ph2023

Comment on "Comment on "Traversable wormhole dynamics on a quantum processor" "

Daniel Jafferis, Alexander Zlokapa, Joseph D. Lykken +5

We observe that the comment of [1, arXiv:2302.07897] is consistent with [2] on key points: i) the microscopic mechanism of the experimentally observed teleportation is size winding…

quant-ph2021★ 12 cited

A quantum algorithm for training wide and deep classical neural networks

Alexander Zlokapa, Hartmut Neven, Seth Lloyd

Given the success of deep learning in classical machine learning, quantum algorithms for traditional neural network architectures may provide one of the most promising settings for…

quant-ph2021

Entangling Quantum Generative Adversarial Networks

Murphy Yuezhen Niu, Alexander Zlokapa, Michael Broughton +4

Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio gener…

quant-ph2020★ 21 cited

A deep learning model for noise prediction on near-term quantum devices

Alexander Zlokapa, Alexandru Gheorghiu

We present an approach for a deep-learning compiler of quantum circuits, designed to reduce the output noise of circuits run on a specific device. We train a convolutional neural n…