# On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks

arXiv: https://arxiv.org/abs/2302.10467 · HTML: https://mlsanity.com/paper/2302.10467/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Continuous-variable quantum neural networks](https://mlsanity.com/paper/1806.06871/) (2018)
- [Quantum Neuron: an elementary building block for machine learning on quantum computers](https://mlsanity.com/paper/1711.11240/) (2017)
- [A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification](https://mlsanity.com/paper/2201.01820/) (2022)

## Abstract (arXiv)

In the last two decades, the combination of machine learning and quantum computing has been an ever-growing topic of interest but, to this date, the limitations of quantum computing hardware have somewhat restricted the use of complex multi-qubit operations for machine learning. In this paper, we capitalize on the cyclical nature of quantum state probabilities observed on pulsed qubits to propose a single-qubit feed forward block whose architecture allows for classical parameters to be used in a way similar to classical neural networks. To do this, we modulate the pulses exciting qubits to induce superimposed rotations around the Bloch Sphere. The approach presented here has the advantage of employing a single qubit per block. Thus, it is linear with respect to the number of blocks, not polynomial with respect to the number of neurons as opposed to the majority of methods elsewhere. Further, since it employs classical parameters, a large number of iterations and updates at training can be effected without dwelling on coherence times and the gradients can be reused and stored if necessary. We also show how an analogy can be drawn to neural networks using sine-squared activation functions and illustrate how the feed-forward block presented here may be used and implemented on pulse-enabled quantum computers.