Quantum Machine Learning Concepts

GUPTA, Gagan       Posted by GUPTA, Gagan
      Published: July 14, 2021

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Quantum computers are becoming available, which begs the question: what are we going to use them for? Machine learning is a good candidate. In short, Quantum machine learning is a research area that explores the interplay of ideas from quantum computing and machine learning. QML bridges the gap between abstract developments in quantum computing and the applied research on machine learning. QML has the potential to change what the future looks like. Individually, they're amazing; but together, they're unstoppable.

Quantum computing can theoretically speed up matrix multiplications and process massive amounts of data very quickly, and thus may represent a paradigm shift in AI and ML.

In March 2020, Google announced the release of TensorFlow Quantum - a series of tools combining state-of-the-art machine learning and quantum computing algorithms.

So, what actually is Quantum Machine Learning?

Quantum machine learning is the integration of quantum algorithms within machine learning programs. There are several definitions of the term quantum machine learning. Aimeur, Brassard and Gambs, introduced a way that used for approaches of how to combine quantum computing and machine learning, depending on whether one assumes the data to be generated by a quantum (Q) or classical (C) system, and if the information processing device is quantum (Q) or classical (C), namely, CC, QC, CQ, and QQ.

-The case CC refers to classical data being processed classically. This is the conventional approach of Machine learning.
-The case QC investigates how machine learning can help with quantum computing.
-The CQ setting uses quantum computing to process classical datasets. The central task of the CQ approach is to design quantum algorithms for data mining, and there are a number of strategies that have been proposed by the community.
- The last approach, QQ, looks at 'quantum data' being processed by a quantum computer. This can have two different meanings. First, the data could be derived from measuring a quantum system in a physical experiment and feeding the values back into a separate quantum processing device.

The Bigger Picture

But the story is bigger than just using quantum computers to tackle machine learning problems. Quantum circuits are differentiable, and a quantum computer itself can compute the change in control parameters needed to become better at a given task. The idea of training quantum computers is larger than quantum machine learning. Trainable quantum circuits can be leveraged in other fields like quantum chemistry or quantum optimization. It can help in a variety of applications such as the design of quantum algorithms, the discovery of quantum error correction schemes, and the understanding of physical systems.

PennyLane is one such open-source software framework built around the concept of quantum differentiable programming. It seamlessly integrates classical machine learning libraries with quantum simulators and hardware, giving users the power to train quantum circuits.

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Quantum Machine Learning Concepts
Quantum Machine Learning Concepts

Building Blocks of QML

Quantum machine learning (QML) is built on two concepts: quantum data and hybrid quantum-classical models.

Quantum Data

Quantum data is any data source that occurs in a natural or artificial quantum system. This can be data generated by a quantum computer. Quantum data exhibits superposition and entanglement, leading to joint probability distributions that could require an exponential amount of classical computational resources to represent or store.

Hybrid quantum-classical models

A quantum model can represent and generalize data with a quantum mechanical origin. Because near-term quantum processors are still fairly small and noisy, quantum models cannot generalize quantum data using quantum processors alone.

Applications of QML

Here are some of the areas QML will disrupt:

- Understanding nanoparticles.
- Supervised learning with quantum classifiers.
- The creation of new materials through molecular and atomic maps.
- Adaptive layer-wise learning for quantum neural network.
- Molecular modeling to discover new drugs and medical research.
- Classification with quantum neural networks on near term processors.
- Understanding the deeper makeup of the human body.
- Creating complete connected security through merging with IoT and blockchain.

Why should an ML expert be interested in quantum computation?

And why are we expecting quantum computers to be useful in ML? There can be 2 reasons. First, with an ever-growing amount of data, current ML systems are rapidly approaching the limits of classical computational models. In this sense, quantum algorithms offer faster solutions to process information for selected classes of problems. Second, results in quantum learning theory point, under certain assumptions, to a provable separation between classical and quantum learnability. This implies that hard classical problems might benefit significantly from the adoption of quantum-based computational paradigms. But optimism should come with a dose of skepticism. The known quantum algorithms for ML problems suffer from a number of caveats that limit their practical applicability and, to date, it is not yet possible to conclude that quantum methods will have a significant impact in ML.

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Comparing the performance of classical and quantum algorithms

Currently, computing devices capable of performing quantum computing (quantum computers) are available for consumers of computing services using the QCaaS (quantum computing as a service) model. As of June 2020, the maximum number of qubits available for simultaneous use does not exceed 60, which is significantly less than the number required to achieve 'quantum supremacy'. This raises the problem of investigating the possibilities of quantum programming for machine learning tasks implementation, namely, the use of machine learning algorithms, implemented by the quantum programming language, to analyze traditional data and compare the performance of quantum and vonneumanns implementations at the present stage of their development.

In a research, it was concluded that at the current stage of quantum technologies development traditionally machine learning provides greater performance than quantum-enhanced. At the same time, quantum-enhanced machine learning algorithms turned out to be inversely sensitive to the complexity of the dataset: training on a more complex dataset breast_cancer (30 inputs, 2 outputs, 569 elements) was performed at a higher speed than training on a less complex data set wineICon-MaSTEd 2020 Journal of Physics: Conference Series 1840 (2021) 012021 IOP Publishing doi:10.1088/1742-6596/1840/1/0120219 (13 inputs parameters, 3 outputs, 178 elements), while a direct relationship was observed for classical machine learning, confirmed by [2] and other sources. The results of the analysis give an opportunity to make the assumption that it is advisable to apply quantum-enhanced machine learning to datasets with a large input dimension, the assumed value for which is the probability of choosing one of two sets of classes - such classes are effectively worked out by single-qubit system.


As the size of a classical dataset increases, the algorithm promises to significantly speed-up the task compared to currently employed ML approaches that require polynomial time. For the very same reasons, however, millions of qubits would be needed to handle datasets where state-of-the-art classical ML starts to struggle. We do not expect such devices to appear in the next decade. General scheme for hybrid quantum - classical algorithms is one of the most promising research directions to demonstrate quantum enhancement in ML tasks.


While machine learning itself is now not only a research field but an economically significant and fast growing industry and quantum computing is a well established field of both theoretical and experimental research, quantum machine learning remains a purely theoretical field of studies. Attempts to experimentally demonstrate concepts of quantum machine learning remain insufficient. Quantum computing and quantum machine learning are still in their infancy. However, the field is rapidly developing, and advances in hardware design and algorithm development are open to interested researchers in industry and academia. It's likely that commercially-available quantum computers will drop in price and find more uses in certain industries and companies within the next 5-10 years, and the field of quantum machine learning is widely open to innovation and guidance in its development.

Hope this was helpful, in giving you a quick brief about these two new technologies, our world is experiencing.

Do contact, if you are interested in learning Quantum Machine Learning Programming, it would be fun !

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Quantum Machine Learning Concepts

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