REVIEW 3 cited by
Google Quantum AI's Quest for Error-Corrected Quantum Computers
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Google Quantum AI's Quest for Error-Corrected Quantum Computers
read the original abstract
Quantum computers stand at the forefront of technological innovation, offering exponential computational speed-ups that challenge classical computing capabilities. At the cutting edge of this transformation is Google Quantum AI, a leader in driving forward the development of practical quantum computers. This article provides a comprehensive review of Google Quantum AI's pivotal role in the quantum computing landscape over the past decade, emphasizing their significant strides towards achieving quantum computational supremacy. By exploring their advancements and contributions in quantum hardware, quantum software, error correction, and quantum algorithms, this study highlights the transformative impact of Google Quantum AI's initiatives in shaping the future of quantum computing technology.
Forward citations
Cited by 3 Pith papers
-
A Hardware-Efficient M{\o}lmer-S{\o}rensen Gate for Superconducting Quantum Computers
An MS gate compiled to one CNOT plus single-qubit rotations achieves 92.47% process fidelity on an IBM superconducting processor, roughly matching the native CX's 93.02%.
-
Practical Fidelity Limits of Toffoli Gates in Superconducting Quantum Processors
Benchmarking a decomposed Toffoli gate on IBM quantum hardware yields 56-64% state fidelities, but the claimed state-dependent error pattern is confounded by using different devices.
-
Project-Based Learning in Introductory Quantum Computing Courses: A Case Study on Quantum Algorithms for Medical Imaging
A first-person teaching case study reports that a project-based HHL-for-CT-imaging assignment helped the authors learn quantum computing, without measured learning outcomes, and confirms HHL is impractical for real CT today.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.