Top resources to learn quantum machine learning

By: Flaka Ismaili    October 27, 2022

As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. We implement the Quanvolutional Neural Networks on a quantum computer to recognize handwritten digits, and compare its performance to that of its classical counterpart, the Convolutional Neural Networks . Additionally, we implement the QSVM on the breast cancer dataset and compare it to the classical SVM. Finally, we implement the Variational Quantum Classifier and many classical classifiers on the Iris dataset to compare their accuracies.

These companies are hardware-agnostic and the whole sector seems likely to become very competitive as early tire-kicking transitions from POC projects to production deployment. Quantum computing needs highly specialized rooms dramatically different from the way cloud providers build and execute their existing data centers. The problem lies in aligning the qubit states in the system with a given problem since quantum computers still have not proven to solve problems better than traditional computers. Finally, data sets that exhibit learning advantages should be balanced. This means that the data set should contain an equal number of data points from each class or label. For example, if a data set is used to train a machine learning model to identify images of cats, the data set should contain an equal number of images of cats and images of other objects.

Google Cirq

They might be adaptations of traditional machine learning models to function with quantum algorithms, or they can be entirely novel creations inspired by the characteristics of quantum computing. In this paper, we aim to introduce quantum computing to the field of machine learning from a basics perspective all the way to its applications. In Section 2, we explore the background of quantum computing, the architecture of quantum computers, and an introduction to quantum algorithms. In Section 4, we implement three machine learning algorithms in order to compare the performance of each algorithm with its classical counterparts.

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There does not exist a scenario where both the qubits are in the same state. The idea of Quantum Entanglement refers to the idea that if we take two qubits, they are always in a superposition of two states. Suppose there is a box and inside of it, there is a pair of gloves. The glove that was taken out was found to be right-handed, so we automatically know that the glove that is still inside the box is left-handed.


A more considerable loss function means there will be more unreliable and less accurate outputs, which can be costly and lead to wrong estimations. In classical computers, SVM can be performed only up to a certain number of dimensions. After a particular limit, it will be hard because such computers do not have enough processing power. The summarized vector that we get after Quantum Principal Component Analysis has logarithmic qubits.

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Kerenidis I., Prakash A. Quantum gradient descent for linear systems and least squares. Khoshaman A., Vinci W., Denis B., Andriyash E., Sadeghi H., Amin M.H. Quantum variational autoencoder. Kieferová M., Wiebe N. Tomography and generative training with quantum Boltzmann machines. Under each input, the four outcome channels produced by our quantum convolution were shown in grayscale images. Other quantum or classical layers can be added after the quanvolutional layer.

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Google’s Cirq is an open-source quantum computing platform that enables users to build and test algorithms. Although there are a variety of ways to create entangled Bell states using quantum circuits, the most basic uses a computational basis as input and includes a Hadamard gate and a CNOT gate . Inside Machine Learning, there is a more advanced field known as Reinforcement Learning.

open source quantum machine learning services

Microsoft also provides simulators, and you can run your code in the cloud. Cirq is an open-source Python framework for “creating, editing, and invoking Noisy Intermediate-Scale Quantum circuits. Cirq is in the alpha phase and not (yet?) an official Google product. Further, IBM even hosts a summer school aiming to teach quantum computing to high-school students. And there is the material provided by the community that already works with the framework.

MAQML: a Meta-approach to Quantum Machine Learning with Accentuated Sample Variations for Unobtrusive Mental Health Monitoring

The research of quantum machine learning is an emerging field that has flourished with the rapid development of quantum computing. In particular, quantum neural networks , similar to classical neural networks, have already been applied in many large-scale machine learning tasks such as automatic speech recognition, speech enhancement, and natural language understanding. Moreover, more secured data privacy can be ensured by applying QNN based models. Typically, VQAs employ variational quantum circuits to measure the cost function on a quantum computer and outsource its optimization to a classical computer. While one can manipulate gradient-free optimizers, such as Nelder-Mead simplex, to minimize the cost function, using gradient-based methods like gradient descent can help us speed up and guarantee the convergence of the optimization. Several quantum algorithms have been proposed to evaluate the cost function gradient measured on quantum computers .

  • User-friendly and efficient numerical simulations of a wide variety of open quantum systems.
  • This field investigates ways to use some of the special properties of quantum physics to build quantum computers that take advantage of quantum bits that can contain 0 and 1 combinations in superposition at the same time.
  • It offers users a two-qubit “semiconductor electron spin processor,” a five-qubit “superconductor Transmon processor,” and three simulators.
  • These libraries often provide pre-existing examples adapted from promising research so that developers don’t need to reinvent the wheel if they don’t want to.
  • Quantum++ is written in standard C++17 and has very low external dependencies, using only the Eigen 3 linear algebra header-only template library and, if available, the OpenMP multi-processing library.
  • Specification and implementation of quantum common lisp, for gate-model quantum computers.