Demystifying Quantum Machine Learning

By Data Science Salon

Everything a computer knows or sees is in the form of either ones or zeros. This combination of ones and zeros allows computers to store data and perform mathematical operations. Ones and zeros are also referred to as bits. 

Computers have electrical components called "Transistors" which create these bits, and each modern computer has billions of transistors installed. However, even though computers today have reached unimaginable processing speeds, there are still limits to their operation. 

Modern-day processors can parallelize tasks to some extent, but there still exists a bottleneck for extreme workloads. For example, if you wanted to search through a database of 1 million records, a conventional computer would go through each record one by one (linear search), which can take a lot of time.

To eliminate the processing bottleneck of modern computers, scientists have started developing quantum computing. Quantum computing revolutionizes how machines operate and opens a new paradigm for computer scientists. Let's discuss quantum computing in more detail below.

Basics of Quantum Computing

The concept of quantum computing came to light in 1981 when Paul Benioff argued the possibility of using quantum principles to operate a computer. In 1984, he published his ground-breaking paper, which discussed the opportunities of a machine that worked exclusively on quantum rules.

Quantum computing defines new rules for how computers operate. Instead of working with bits, they introduce a new concept called ‘qubits’ or ‘Qquantum bits.’ Unlike a regular bit, a qubit can have a state of one, zero, or both.

Qubits are represented by quantum elements such as electrons. The direction of spin of an electron determines whether the qubit is zero or one. However, before a qubit is measured, it exists in a state between 1 and 0, i.e., it contains some portions of one and some of zero. This overlapping state is called superposition. 

The superposition property allows qubits to be in a state in-between 1 and 0. It enables quantum computers to perform multiple tasks simultaneously. This will make more sense if we look back at the problem of searching through an extensive database. 

We established that a regular computer would take a long time to complete. But what if we use a quantum computer? We will get results instantaneously. Unlike a standard computer, a quantum computer can look at each record simultaneously and output the result immediately. 

Apart from parallel processing, quantum computers can also exhibit high processing power due to quantum entanglement.

Quantum entanglement allows us to change the state of a quantum particle by modifying another particle. These particles are said to be entangled. And since altering one particle enables us to change multiple particles, we get an exponential speed-up in processing with minimal power consumption.

Quantum computers might be overkill for everyday tasks but prove groundbreaking for high-intensity tasks such as quantum simulations.

What is Quantum Machine Learning?

A quantum computer can perform calculations in 4 minutes which would take 10,000 years on the fastest modern supercomputer. Quantum machine learning (QML) utilizes the high processing power of a quantum computer to speed up its training process. Hence it provides more accurate results in less time. Conversely, ML can also be utilized to create quantum error-correcting algorithms, which are essential to creating an error-free quantum system.

The use of quantum machine learning requires specialized quantum algorithms and quantum data encoding. It is also important to note that much of the work in QML is still theoretical, and fully utilizing it will require further advancement in quantum computing. That said, specific algorithms have been created that use the potential of QML, and we will discuss those later on.

How is Quantum Machine Learning Different than Traditional Machine Learning?

Classical ML performs several sequential algebraic operations to produce an output. Older models take a few seconds to train on modern machines. High-end GPUs are required for more advanced algorithms such as neural networks. GPUs can perform several thousand operations per second, speeding up training. However, ML algorithms have grown so advanced that even with the highest-end GPU, they could take weeks to train.

In comes quantum computing

The use of quantum computing techniques to assist machine learning models gives birth to quantum machine learning. Quantum computers can process multiple states at the same time, allowing them to perform multiple calculations simultaneously and help reduce training time. However, at its core, QML works differently compared to classical ML. Algorithms created according to quantum rules are needed to carry out the processing, and data needs to be encoded in a form that a quantum system can understand. 

As of yet, QML is not in a reliable state since most of the algorithms are not fault tolerant. It might take a few years before we see significant progress.

Algorithms and Models for Quantum Machine Learning

Quantum computing follows a different set of rules, and engineers are designing new algorithms which comply with them. QML is still in its construction phase, so there is not a lot that we can see in action; however, certain algorithms harnessing the power of quantum computing have been created. These are mentioned in the paper by Jacob Biamonte 

Let us view some of these algorithms below.

  1. Solving linear algebraic equations

Quantum computers specialize in solving complex matrix operations in high-dimensional vector spaces. They can solve problems like calculating Fourier transform, Eigenvectors, and Eigenvalues in extremely high dimensions, which would not be possible with a conventional computer.

2. Quantum Principal Component Analysis

Principal component analysis (PCA) is a feature reduction technique for machine learning algorithms. PCA requires the calculation of eigenvectors and eigenvalues in an n-dimensional space, n being the dimension of our data. This becomes a problem when the dimensionality is too large for a conventional computer. 

A quantum computer uses its high-speed QRAM to map dense data vectors onto their quantum state. It then uses a series of quantum algorithms to calculate the quantum principle components.

3. Quantum Support Vector Machines

Support vector machines (SVM) try to create a decision boundary between data points in the form of a hyperplane. Most data points are not linearly separable, so SVM increases data to a higher dimension until a distinction is formed. SVM is already a computationally expensive algorithm, and time complexity only worsens with high-dimensional data. 

In quantum computers, the properties of superposition and entanglement allow these processes to be carried out at a much faster rate.

Some other algorithms include Quantum Deep Learning, K-means Clustering, Quantum Optimization, Quantum Data Encoding, etc. Sadly discussing all of these algorithms is not possible for this article.

These algorithms outperform their counterparts in classical machine learning. However, these still are not used in production environments, mainly because the algorithms are not fault-tolerant but also because of the lack of availability of quantum computers.

Quantum Machine Learning; An AI Revolution?

The advent of quantum computing comes with the dawn of a new technological era. It provides computer scientists with the capabilities to solve complex real-world problems. The principles of quantum entanglement and superposition allow computers to perform several billion calculations per second. 

The benefit of these high-performing systems will be most prominent in machine learning problems. Quantum processing can reduce the training time of complex algorithms from weeks to minutes. Additionally, machine learning engineers can perform operations on high-dimensional data without any bottlenecks.

With all its bells and whistles, quantum machine learning is still under development, and it might be some time before we can explore its full potential. Most of the work done for QML is still theoretical, and it will take years to gain momentum and produce promising results.

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