Overview of Quantum AI

March 16, 2022

As the name suggests, Quantum Artificial intelligence (AI) applies quantum technology to machine learning procedures. Quantum technology and machine learning are game-changing innovations that will make problem-solving easier.

This article will discuss quantum computing, how quantum computers operate, and its relation to machine learning. We will also look at Quantum AI, its applications, and real-life examples.

Table of contents

Key takeaways

By the end of this article, the reader should be able to understand:

  • Quantum computing and its relation to machine learning.
  • Applications of quantum computing.
  • Quantum AI, its milestones, application, and examples.

Quantum Computing

To fully understand the meaning of Quantum computing, we must first define the terms below:

  • Quantum: The term “quantum” refers to quantum mechanics used by quantum computers to compute outputs. A quantum is the tiniest single component of any physical attribute in physics. Generally, it refers to electrons and photons, atomic or microscopic particles.

  • Qubit: In quantum computing, a qubit refers to the fundamental data unit. Qubits serve the same purpose as bits in standard computers. However, they act quite differently in this case. Qubits may retain a superposition of all states, unlike standard bits, which are binary, maintaining a 0 or 1.

  • Quantum data:These are data packets found in qubits used for processing.

  • Quantum algorithms: An algorithm is a set of procedures that results in a solution to a problem. To carry out these procedures on a device, you will need to employ certain instruction sets that the device was built to support.

    Quantum computing provides instructions based on a fundamentally different concept of execution. Quantum algorithms seek to speed up the solution by utilizing quantum concepts such as superposition and entanglement.

Quantum computers make use of quantum physics properties by applying them in computing. The properties are quantum interference, superposition, and entanglement.

1. Superposition

Quantum particles in superposition are mixed in all conceivable states. They vary unless they are monitored and computed. Consider a coin to see the differences between binary placement and superposition.

Standard bits are countable by tossing the coin, and it probably lands on heads or tails. The coin is in superposition if you can see heads, tails, and every state in between at the same time.

2. Entanglement

Quantum particles in entanglement can link their test findings together. When qubits entangle, they create a unified system that interacts with one another. Judgments on other qubits can easily be made based on data from one qubit.

3. Quantum interference

It involves the inherent activity of a qubit. It influences the chance of it falling in one direction because of superposition.

Quantum machine learning

Quantum Machine Learning (QML) uses quantum algorithms to solve machine learning challenges. Its objective is to identify the most suitable solution to a given quantum algorithm for a given issue.

Furthermore, use it in real-life applications such as optical recognition, language translation, or any other work that can be handled with machine learning. Quantum algorithms for quantum machine learning include:

  1. Quantum annealing: This is a quantum computing approach for searching and optimizing quantum systems. It is an optimal control approach for determining a function’s local minima and maxima across a set of selected functions. The method determines the function’s observables by separating a function with local minima or maxima.

  2. Quantum Boltzmann machine (QBM): QBM is a quantum continuous neural network used to mimic supervised and unsupervised learning tasks. Boltzmann machines are neural networks involved in creative machine learning. QBMs are a subset of the larger category of quantum generative models, which play a crucial role in information processing.

  3. Quantum reinforcement learning (QRL): QRL aims to use quantum computers’ processing advantages by creating reinforcement learning agents. These agents use quantum models of computation. Remember that reinforcement learning is a machine learning approach for training AI agents environmental information.

    It then enables them to accomplish tasks freely instead of giving detailed instructions on what actions to perform at each phase of the trial-and-error process.

  4. Quantum walks: These are the general quantum computation model, a quantum-enhanced search algorithm that is used to solve optimization challenges. They propose a new method for performing quantum algorithms such as database search, network inspection and routing, and quantum simulations.

  5. Quantum big data analytics: Combines quantum processing and machine learning skills to handle big data challenges. It results in more outstanding performance than standard neural networks. This quantum algorithm is utilizable in various cases like image recognition and language translation.

How Quantum AI works

TensorFlow Quantum is a technology with an open-source framework. Its goal is to offer tools to manage and model natural and artificial quantum processes. TensorFlow Operations results from quantum experiments that result in classical probabilistic occurrences.

TensorFlow Quantum can create tensors using quantum data sets, quantum models, and conventional command variables in a single computer network. TensorFlow Operations gets the results of quantum experiments that result in classical probabilistic occurrences.

To understand how to use quantum data, consider a quantum neural system’s guided categorization of quantum states. A crucial issue of quantum Machine Learning, like conventional Machine Learning, is to categorize “noisy data”. Follow the steps below to develop and train a quantum framework using TensorFlow Quantum:

  1. Construct a quantum data collection - Tensors represent quantum data. Cirq quantum network generates this quantum data, and TensorFlow controls all the activities on the system, creating a quantum database.

  2. Assess the quantum neural system design - Cirq can design a quantum neural system that will then be embedded in a TensorFlow computing structure. It may choose customized quantum systems from many groups depending on the quantum’s data nature. The model’s purpose is to use quantum computation to retrieve information concealed in an entangled form.

    The quantum model frees incoming quantum data and keeps the concealed information stored in classical associations, hence available to localized measures and classical post-processing.

  3. Sample or Average - Quantum form measurement derives classical information from classical randomized variables in samples.

    The quantum state and the observable influence after the dispersion of values from the random variable. Many variational algorithms rely on average measurement values, also called expectation values. TensorFlow Quantum includes techniques for combining over many runs comprising steps (1) and (2).

  4. Analyze the classical neural systems design: The retrieved classical information is now in a state that can be processed further because the retrieved information still contains some classical connections.

  5. Cost Function Evaluation: A cost function is assessed based on the findings of classical processing. When the quantum data is tagged, this may depend on how well the model executes the classifying job or other factors if the job is uncontrolled.

  6. Examine Gradients & Upgrade Parameters - After examining the cost function, the pipeline’s available parameters should be adjusted in a way that reduces the cost. Gradient descent is the known way of doing this.

For more about training quantum frameworks using TensorFlow Quantum, click here.

Possible application of quantum computing in AI

The workable goal for quantum AI developers is to build quantum algorithms that outperform classical computer algorithms.

  • Learning quantum algorithms. Quantum algorithms for quantum extensions of classical learning models are being developed. It may speed up or enhance the deep learning training process somehow. By delivering the ideal solution set of weights for artificial neural networks, quantum technology can contribute to classical machine learning.

  • Decision trees state classical decision issues, and quantum methods for decision problems are based on them. Forming branches from particular places is one way to get at the solutions.

    The effectiveness of this strategy declines when each problem is too complicated to be handled by splitting it into two. Quantum algorithms centered on Hamiltonian time evolution are quicker than random marches in solving issues represented by a set of decision trees.

Milestones for quantum AI

Even though quantum AI is still a young field of study, advances in quantum computing increase quantum AI’s potential, yet the AI sector must reach certain milestones to develop the technology. The milestones are as follows:

  • Quantum computing devices are less error-prone and very powerful.
  • Open-source analysis and training frameworks are widely used.
  • A large and well-trained developer ecosystem.
  • Quantum computing surpasses classical computing in intriguing AI applications.

These crucial stages will pave the way for future quantum AI advancements.

Examples of quantum AI applications

Quantum AI crypto bot

Quantum AI Crypto Bot is a technology developed using quantum methods that enables you to earn money online by betting on foreign exchange, market indices, and cryptocurrencies. This trading method is well known as cryptocurrency trading.

The crypto bot is effective as it has all its activities automated. It uses Artificial Intelligence to facilitate more accurate trading. The site uses blockchain’s version of Smart Contracts to enhance its user’s security and provides a streamlined dispute resolution mechanism.

How quantum AI checks crypto markets

As the name suggests, Quantum AI merges artificial intelligence with trade to provide exact results. It uses quantum computing to analyze many information streams at the same time. It allows quicker and more accurate decisions. This trading technique is built on volatility rather than price appreciation. Hence profits may be achieved even when prices decline. The Quantum-based robot performs all the work for you since it is automated.

To get started, create a free Quantum AI account. After you sign up, you will have access to Quantum AI’s pro services, which will enhance your trading process and raise your profits.


At the moment, a typical home computer is incapable of processing enormous volumes of data at once. Quantum computers may be capable of reaching into an extensive database and delivering an evaluation in seconds; with this, we can discover patterns within a short period using quantum technology.

Further Reading

Happy learning!

Peer Review Contributions by: Briana Nzivu