Non-parametric Greedy Optimization of Parametric Quantum Circuits
2024 25th International Symposium on Quality Electronic Design (ISQED)(2024)
Abstract
The use of Quantum Neural Networks (QNN) that are analogous to classical
neural networks, has greatly increased in the past decade owing to the growing
interest in the field of Quantum Machine Learning (QML). A QNN consists of
three major components: (i) data loading/encoding circuit, (ii) Parametric
Quantum Circuit (PQC), and (iii) measurement operations. Under ideal
circumstances the PQC of the QNN trains well, however that may not be the case
for training under quantum hardware due to presence of different kinds of
noise. Deeper QNNs with high depths tend to degrade more in terms of
performance compared to shallower networks. This work aims to reduce depth and
gate count of PQCs by replacing parametric gates with their approximate fixed
non-parametric representations. We propose a greedy algorithm to achieve this
such that the algorithm minimizes a distance metric based on unitary
transformation matrix of original parametric gate and new set of non-parametric
gates. From this greedy optimization followed by a few epochs of re-training,
we observe roughly 14
the cost of 3.33
observed for a different dataset as well with different PQC structure.
MoreTranslated text
Key words
PQC,greedy optimization,transformation matrix
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined