# Tunable Quantum Neural Networks in the QPAC-Learning Framework

arXiv: https://arxiv.org/abs/2205.01514 · HTML: https://mlsanity.com/paper/2205.01514/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 1 of 1)

- [Quantum Approximate Counting, Simplified](https://mlsanity.com/paper/1908.10846/) (2019)

## Abstract (arXiv)

In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. Tunable neural networks are quantum circuits made of multi-controlled X gates. By tuning the set of controls these circuits are able to approximate any Boolean functions. This architecture is particularly suited to be used in the QPAC-learning framework as it can handle the superposition produced by the oracle. In order to tune the network so that it can approximate a target concept, we have devised and implemented an algorithm based on amplitude amplification. The numerical results show that this approach can efficiently learn concepts from a simple class.