Quick Navigation
Topics
Quantum Chemistry
Evaluation of Parameterized Quantum Circuits with Cross-Resonance Pulse-Driven Entanglers
arXiv
Authors: Mohannad Ibrahim, Hamed Mohammadbagherpoor, Cynthia Rios, Nicholas T. Bronn, Gregory T. Byrd
Year
2022
Paper ID
57803
Status
Preprint
Abstract Read
~2 min
Abstract Words
162
Citations
N/A
Abstract
Variational Quantum Algorithms (VQAs) have emerged as a powerful class of algorithms that is highly suitable for noisy quantum devices. Therefore, investigating their design has become key in quantum computing research. Previous works have shown that choosing an effective parameterized quantum circuit (PQC) or ansatz for VQAs is crucial to their overall performance, especially on near-term devices. In this paper, we utilize pulse-level access to quantum machines and our understanding of their two-qubit interactions to optimize the design of two-qubit entanglers in a manner suitable for VQAs. Our analysis results show that pulse-optimized ansatze reduce state preparation times by more than half, maintain expressibility relative to standard PQCs, and are more trainable through local cost function analysis. Our algorithm performance results show that in three cases, our PQC configuration outperforms the base implementation. Our algorithm performance results, executed on IBM Quantum hardware, demonstrate that our pulse-optimized PQC configurations are more capable of solving MaxCut and Chemistry problems compared to a standard configuration.
Why This Paper Matters
- This paper contributes to the Quantum Chemistry research area in the Quantum Articles archive.
- It adds a 2022 reference point for readers tracking recent quantum research.
- Variational Quantum Algorithms (VQAs) have emerged as a powerful class of algorithms that is highly suitable for noisy quantum devices.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.