Quantum Circuit Learning - Qulacs Documentation
http://docs.qulacs.org/en/latest/apply/5.2_qcl.html
QCL is a machine learning method that replaces this neural network with a quantum circuit, and hence a quantum computer.
Quantum circuit complexity and unsupervised machine learning of ...
https://www.nature.com/articles/s41467-026-71283-5
Here, inspired by the close relationship between Kolmogorov complexity and unsupervised machine learning, we explore quantum circuit complexity
Variational Quantum Circuits for Machine Learning - Medium
https://medium.com/quantum-computing-and-ai-ml/variational-quantum-circuits-f…
Trainable quantum circuits act like tiny neural‑network layers, letting hybrid algorithms learn patterns on NISQ hardware today and scaling
Quantum Machine Learning - PennyLane
https://pennylane.ai/qml/quantum-machine-learning
Get started with quantum machine learning using PennyLane—the definitive open-source Python framework for quantum programming, built by researchers for
Introduction | IBM Quantum Learning
https://quantum.cloud.ibm.com/learning/courses/quantum-machine-learning/intro…
Machine learning (ML) applies algorithms to data sets, and so QML might plausibly include quantum mechanics in either the data or algorithmic sides, or both. But we will mostly restrict ourselves to discussions of quantum algorithms applied to classical data. It is **not** the case that some quantum feature maps enable us to solve all or many classification tasks more efficiently or scalably than classical machine learning algorithms. Several quantum machine learning (QML) algorithms that were initially thought to provide significant speedups over classical algorithms have been dequantized in recent years. Some dequantization cases have shown that when this encoding time is included, and when classical data loading can be accomplished efficiently, the quantum algorithm no longer outperforms its classical counterpart. Most machine learning problems are solved very efficiently by classical algorithms and quantum algorithms are not likely to offer any substantial speed-up. ! wget https://raw.githubusercontent.com/qiskit-community/prototype-quantum-kernel-training/main/data/dataset_graph7.csv. `--2025-05-09 10:04:28-- https://raw.githubusercontent.com/qiskit-community/prototype-quantum-kernel-training/main/data/dataset_graph7.csv.
Quantum Circuit Training for Machine Learning Tasks and Simulating Wormholes – Quantum Computing with Trapped Ions
https://iontrap.duke.edu/2019/12/06/hello-world/
Flowchart for the use of classical feedback to optimize a quantum circuit to generate target quantum states. Flowchart for the use of classical feedback to optimize a quantum circuit to generate target quantum states. In a related experiment, we use a variational technique (quantum approximate optimization algorithm or QAOA) to generate “Thermofield Double States.” These states are pairwise entangled across a ladder network when considered as a whole, but become identical thermal mixed states when considered individually, and their evolution scrambles qubits (see below). Christopher Monroe’s research group at the Duke Quantum Center, within the Department of Electrical and Computer Engineering and Department of Physics at Duke University. *Our group focuses on the use of individual atoms and photons for fundamental studies of quantum physics and applications in quantum information science.* *A long term goal of our research is the realization of large-scale quantum computers, simulators, and quantum information networks that store and process information in ways that can eclipse the performance of conventional computers.*.
IonQ | How Does Quantum Machine Learning Work?
https://www.ionq.com/blog/how-does-quantum-machine-learning-work
Quantum machine learning works by using quantum bits, or ‘qubits’ to perform computation on input data using quantum circuits. Because quantum circuits generate a large number of potential solutions each time they are run, they can often improve the performance of a model faster than classical machine learning methods. But in the quantum state, you create a 2^N dimensional vector that encodes all these complex correlations and in this way, you can represent probability distributions with N qubits that are exponentially hard to reproduce with classical computers. This type of matrix inversion and linear equation-solving methods are used in several classical machine learning problems such as in support vector machine classification or Gaussian process regression, and so in the quantum setting you can apply these algorithms to solve problems with exponentially large data sets faster than classical algorithms. The ways quantum machine learning works have the potential to generate better, more accurate machine learning models, especially when used to analyze complex data with many different variables.
Introduction to Quantum Machine Learning and Quantum ... - arXiv
https://arxiv.org/html/2504.16131v1
Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms.