quAssert: Automatic Generation of Quantum Assertions
March 02, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Hasini Witharana, Daniel Volya, Prabhat Mishra
arXiv ID
2303.01487
Category
quant-ph: Quantum Computing
Cross-listed
cs.SE
Citations
4
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Functional validation is necessary to detect any errors during quantum computation. There are promising avenues to debug quantum circuits using runtime assertions. However, the existing approaches rely on the expertise of the verification engineers to manually design and insert the assertions in suitable locations. In this paper, we propose automated generation and placement of quantum assertions based on static analysis and random sampling of quantum circuits. Specifically, this paper makes two important contributions. We automatically uncover special properties of a quantum circuit, such as purely classical states, superposition states, and entangled states using statistical methods. We also perform automated placement of quantum assertions to maximize the functional coverage as well as minimize the hardware overhead. We demonstrate the effectiveness of the generated assertions in error detection using a suite of quantum benchmarks, including Shor's factoring algorithm and Grover's search algorithm.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Quantum Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Quantum machine learning: a classical perspective
R.I.P.
π»
Ghosted
Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers
R.I.P.
π»
Ghosted
ProjectQ: An Open Source Software Framework for Quantum Computing
R.I.P.
π»
Ghosted
Quantum Recommendation Systems
R.I.P.
π»
Ghosted
Traffic flow optimization using a quantum annealer
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted