The Impact of Quantum Computing: Data Science and Artificial Intelligence

This article is an exploration of how quantum computing enhances machine learning and artificial intelligence systems.

The difference between classical computing and quantum computing is that classical computing is exclusively binary, with data stored in physical bits of “zeros” or “ones” but never both concurrently; while in quantum computing, there is an allowance for linearity such that a combination of both states simultaneously is possible giving room for significantly more data to be stored in a unit (quantum bit) than in a regular one. 

An illustration of the importance of quantum computing is in spotting relationships between very large datasets. A conventional system would consider each item in a parallel manner and would take a long time; in some cases, due to the size of the datasets, it might never arrive at a solution. A quantum computer on the other hand would resolve the problem in a matter of seconds.

Source: Image by Gerd Altmann from Pixabay

Impact of Quantum Computing

The application of quantum algorithms in techniques involving artificial intelligence will enhance the learning abilities of machines. This will result in the development of prediction systems like those of the financial industry being improved. There is, however, a waiting period before these improvements will be evident. 

The processing power needed to derive value from the numerous streams of data being collected, particularly for the application of artificial intelligence techniques like machine learning continually increases. Researchers have been putting efforts into expediting these processes by the application of quantum computing algorithms to AI techniques; this has resulted in a previously non-existent discipline referred to as Quantum Machine Learning being formed.

Artificial intelligence and machine learning technologies are two main aspects of research in quantum computing algorithm application. A characteristic of this system of calculation is its allowance for the representation of multiple states simultaneously; this is especially suitable for AI techniques. 


Intel notes that voice assistants would be beneficiaries of the implementation with quantum computing increasing accuracy in folds, enhancing the quantity of data they are capable of handling as well as their processing power. Machines can process a higher amount of calculation variables when quantum computing is used, leading to answers being arrived at more speedily than if a person does it.

Increased Algorithm Accuracy

Quantum computing is applicable in many fields for the solution of problems because it is capable of representing and handling numerous states. Intel has made several forays into researching quantum algorithms owing to the sheer number of opportunities it presents.

An example would be material science which is a field where the initial applications will yield results; where small molecule modeling is a task heavily reliant on computing. Bigger, more complex machines will give room for medicine design and logistics optimizations to discern the route with the greatest efficiency.

Supervised learning forms the bulk of industrial application of AI in such areas as recognition of images and prediction of consumption trends. 

Fernandez Lorenzo expounds that going on various QML proposals put forward, this aspect will very likely experience potentially exponential growth. In the aspect of reinforcement learning, there is still plenty of ground to cover; as well as specified application to the solution of practical issues plaguing the industry.


Another promising, but less explored aspect is that of non-supervised learning. A researcher considers the case of dimensionality reduction algorithms, used for the representation of data in a space more limited than that occupied by the original but still retains the most vital characteristics of the parent dataset. 

He states that quantum computing will be useful in identifying more general properties than the ones specific to the dataset.

The capability of reinforcement learning to manage complicated scenarios is evident in its video gaming application. 

The most difficult task with regards to time consumed and computing workload is the training received by the algorithm. Fernandez Lorenzo highlights that theoretical proposals have been put forward to hasten this training by engaging quantum computers which may instigate a significantly more advanced artificial intelligence than is currently obtainable.

Use in the Banking Sector

The unification of quantum computing and artificial intelligence in the sector of finance may aid the fight against fraud and improve its detection. Models trained to utilize a quantum computer would be able to identify patterns that would likely elude more mainstream instruments. 

Models are also being developed whereby numerical calculations can be used in conjunction with professional advice to arrive at financial resolutions. An NBD researcher from BBVA identifies a key benefit of these models as their ease of interpretation when compared to neural network algorithms, increasing the chances of them being approved by a regulatory board.

Provision of customized products and services to customers is the learning of the banking sector currently. This is done by utilizing developed systems of recommendation. Several quantum models have been suggested for the improvement of the performance of these systems. Fernandez believes that in the not-so-distant future, the sector would be able to project favorable strategies for investment inspired by quantum algorithms. To arrive at this destination, research is being done into investigating the links between machine learning and quantum supremacy concerning what existing quantum processors are capable of. 


The breakthrough will be dependent on how possible it would be to build models that regular computers would be almost incapable of implementing. Studies are yet to be done on how these models would be applicable in the industry from a practical viewpoint.

The limitations on machine language algorithms due to classical computers’ computational power will be far less in quantum computers. 

Sycamore, a quantum processor Google claims to have developed, solved in 200 seconds a task that would take the world’s fastest supercomputer at least 10,000 years to solve. A potential problem that could have arisen from quantum computing would be sensitivity to environmental alterations potentially leading to errors, but a research team at Max Planck Institute for the Science of Light showed that artificial intelligence neural networks are capable of correcting quantum errors.

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