Amplitude encoding. PennyLane offers built-in support for amplitude encoding via the AmplitudeEmbedding function. Below is a Python example demonstrating amplitude encoding for a randomly generated complex vector.

Block-encoding works by embedding the desired linear operator as a suitably normalized block within a larger unitary matrix, such that the full encoding is a unitary operator, and the desired linear operator is given by restricting the unitary to an easily recognizable subspace.

Apr 14, 2025 · Leveraging insights from [GST24] where certain qubits play an outsized role in the amplitude encoding, we extend the hierarchical learning framework to encode images into quantum states. We successfully load digits from the MNIST dataset as well as road scenes from the Honda Scenes dataset.

Jul 5, 2021 · In quantum machine learning, a lot of models use amplitude encoding, which means that a data vector is represented by the amplitudes of a quantum state. Especially when trying to reproduce neural network-like dynamics one would like to …

Oct 14, 2023 · One powerful method is amplitude encoding, a fundamental concept that plays a pivotal role in quantum algorithms and quantum machine learning. In this blog post, we’ll explore the concept of amplitude encoding, its significance in quantum computation, and a step-by-step guide on how to implement it in Qiskit, a popular quantum computing ...

Nov 26, 2020 · In this paper, we compare the basic implementations of TensorFlow Quantum's Quantum Convolutional Neural Network and a hybrid quantum-classical network using angle encoding, with a third network of our design that utilizes amplitude encoding for …

Apr 17, 2025 · Explore amplitude encoding techniques in quantum machine learning, enhancing data representation for quantum algorithms. Amplitude encoding is a crucial concept in quantum computing that allows for the representation of quantum states in a highly efficient manner.

A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum compu...

To outline abstract knowledge about encodings and the consequences of choosing a particular data encoding, we present three common encodings as patterns. Especially in complex domains like quantum computing, patterns can contribute to making this new technology and its broad potential accessible to users with different backgrounds.

Various quantum encoding techniques, such as basis encoding, amplitude encoding, Qsample encoding, and others, convert classical data into quantum states with different runtime...