2 citations · 2 across the 6 of their papers we have counts for
7 papers
Modular Neural Decoder for Surface Code Memory and Logic on Superconducting Processors
Moritz Lange, Ole Fjeldså, Gustaf Jonasson Johansson +1
Neural decoders for quantum error correction have achieved high accuracy on surface code memory experiments, but practical fault-tolerant computation requires decoders that transfe…
Bias-Preserving Gates and Quantum Error Correction With Dual-Rail Cat Codes
Debjyoti Biswas, Nikhil Sharma, Alberto Salvador +4
Scalable fault-tolerant quantum computation requires quantum error-correcting codes that simultaneously support universal logical operations, suppress hardware-specific noise, and…
Comparing and learning figures of merit for quantum circuit compilation
Harshdeep Singh, Marvin Richter, Mats Granath +1
To make quantum algorithms executable on a particular quantum device, they need to be compiled into circuits that respect constraints of the quantum hardware. This compilation usua…
Low Latency GNN Accelerator for Quantum Error Correction
Alessio Cicero, Luigi Altamura, Moritz Lange +2
Quantum computers can solve selected problems more efficiently than classical computers, but current devices are limited by high physical error rates. Quantum Error Correction (QEC…
Multiple-time Quantum Imaginary Time Evolution
Julio Del Castillo, Mats Granath, Evert van Nieuwenburg
Quantum Imaginary-Time Evolution (QITE) is a powerful method for preparing ground states on quantum hardware. However, executing QITE has costly measurement budgets for general Ham…
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…
Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks
Longde Huang, Oleksandr Balabanov, Hampus Linander +3
Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning problems considered in this context f…