# Supervised Quantum Learning without Measurements

arXiv: https://arxiv.org/abs/1612.05535 · HTML: https://mlsanity.com/paper/1612.05535/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2016.

## Built on by (most cited first, 8 of 8)

- [Quantum Self-Supervised Learning](https://mlsanity.com/paper/2103.14653/) (2021)
- [The role of entanglement for enhancing the efficiency of quantum kernels towards classification](https://mlsanity.com/paper/2209.05142/) (2022)
- [Quantum Pattern Recognition in Photonic Circuits](https://mlsanity.com/paper/2107.09961/) (2021)
- [Image Compression and Classification Using Qubits and Quantum Deep Learning](https://mlsanity.com/paper/2110.05476/) (2021)
- [Learning Quantum Processes with Memory -- Quantum Recurrent Neural Networks](https://mlsanity.com/paper/2301.08167/) (2023)
- [Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study](https://mlsanity.com/paper/2501.04690/) (2025)
- [Quantum Inspired Adaptive Boosting](https://mlsanity.com/paper/2102.00949/) (2021)
- [Quantum vs. Classical Machine Learning Algorithms for Software Defect Prediction: Challenges and Opportunities](https://mlsanity.com/paper/2412.07698/) (2024)

## Abstract (arXiv)

We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.