Quantum Autoencoder 2D: Compressing Qubit States

What is a Quantum Autoencoder?

A quantum autoencoder is an extension of classical machine learning techniques into the realm of quantum computing. It aims to encode and decode information using quantum circuits, with the goal of compressing data efficiently.

In this 2D simulation, you can observe how a 2-qubit variational circuit learns to map a family of entangled states onto a single latent qubit.

How Quantum Autoencoders Work

The quantum autoencoder consists of an encoder and a decoder. The encoder maps the input data state into a lower-dimensional space, while the decoder reconstructs the original state from this compressed representation.

In our 2D simulation, you can see how the fidelity between the original and reconstructed states improves as training progresses.

The Role of Variational Circuits

Variational circuits are parameterized quantum circuits that can be optimized using classical optimization techniques. In this case, the variational circuit is used to learn the encoding and decoding mappings.

By adjusting parameters through gradient descent, the simulation demonstrates how these mappings can be refined to achieve better compression.

Practical Applications

Quantum autoencoders have potential applications in quantum error correction, data compression, and feature learning for quantum machine learning tasks.

By understanding the principles behind this 2D simulation, you can gain insights into how these techniques might be applied to real-world problems.

Frequently asked questions

Try it live

Everything above runs in your browser — open Quantum Autoencoder 2D: Compressing Qubit States and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.