What actually is Quantum Machine Learning?
With the pacing technology, the concept of Quantum Computing and Machine Learning have been molded together to solve the greatest problems of computing and Artificial Intelligence in the form of Quantum Machine Learning!
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There is a great need for machines that could perform tasks on their own, learn on their own, and get the solutions to most difficult problems on their own at a quick rate. This demand comes with the scarcity of time when everyone wants fast and precise results. We know, Machine Learning plays a predominant role when it comes to training machines to work on their own, along with deep learning and Artificial Intelligence, it aggregates to make machines think, work, and act like humans to make more precise decisions which would think from all possible perspectives and provide most significant and suitable output. This is the key feature that is required in machines.
But there is a limitation as well. Normally the computers aren’t able to solve many advanced problems as there are various highly advanced problems, requiring advanced computations which are performed by quantum computing specifically in quantum computers. Quantum Computing comes with various algorithms to solve the problems at a faster rate and Machine learning has its roots well established along with various technologies like Neural Networking, Deep Learning, Optimization, Kernel Evaluation, Linear Algebra, Regression, and much more. With all these technologies in a cluster, we can generate a super technology which can be a quantum computer with power and ability to solve various high-level problems along with self-learning ability to make accurate decisions on concepts just like a human does but with more efficiency, accuracy and less time. This is termed as quantum Machine Learning. It can be called the technology where quantum physics and machine learning merge!
To solve various hardware and software challenges and to make machine learning effective and faster, there is a need for Quantum Machine Learning.
The idea was first visualized by the famous example of Schrodinger’s Cat and Heisenberg’s Uncertainty Principle. These two ideas put forward the concept of superposition and entanglement in a much broader view which leads to the broadening of the concept of Quantum Machine Learning through the growth of Quantum Physics and Machine Learning. This was one of the greatest achievements of that time which now has caused the evolution of the whole technology.
We will see more about it in detail in the below sections.
It came into play when classical physics wasn’t able to explain several concepts and Einstein gave rise to the “Theory of Relativity”. In simpler words, it describes the nature of the particles and the forces that hold them together and enable them to interact. It explains about mass, energy, movement, position, and momentum of an object. With the great efforts of Niels Bohr and Max Planck, quantum theory was shaped to give various detailed explanations about challenging concepts like momentum, relativity, superposition, and much more. If we link this to machine learning so we see that they share many things in common. We know that machine learning is the technology used to train machines with large amounts of training data sets so that they can take decisions and perform tasks on their own without the need of altering the algorithm each time.
In quantum mechanics also, a great challenge is to define the state, i.e., the state in which the particle is present at a given instance of time. To solve these various theories and mathematical equations are given but they provide the answer with uncertainty. So here we use a broad concept, in machine learning, instead of sorting the data instead of any particular pattern, we can train the machine to identify and learn from data sets by identifying their states, i.e., paramagnetic and ferromagnetic states. This requires the concept of Supervised and Unsupervised Learning. In supervised learning, the machine should have prior knowledge about the actual data set to be evaluated from the sample data sets on which it is trained whereas, in unsupervised learning, the machine finds the trends and patterns from the given data set and accordingly evaluates the result. This even required clustering the data into groups according to their common characteristics so that it becomes easy to find the associated trends and patterns and finally evaluate those to get better results.
Computational Neural Network
We require loads of information while dealing with machine learning to identify the state/phase for training the machines. This problem is solved by Neural Networks which are highly efficient algorithms to boost pattern recognition and work just like human brains but at a quicker rate. They are amazing at emulating the brain’s pattern recognition skills. To evaluate and describe each phase properly so that the machine could learn/train more efficiently, computational neural networks provide a proper mechanism. These are also used to model non-linear data models, they model the complex relations so that the machine can find patterns and trends in data more accurately and in less amount of time. They transform or we can say that they evaluate the data so that the machine can understand and draw relations from it. It helps the machine to understand the data provided to it and in this way it helps the machines to compute difficult problems easily. In daily life or business, we require neural networks for solving various cases like weather forecasting, risk analysis and management, data validation, and much more. For all these cases we require a powerful machine that could evaluate the data, draw insights from it by learning from previous patterns, and solve the complex queries efficiently.
CNN’s are widely used for analyzing visual images with multilayer interconnected networks consisting of neurons. They use the hierarchical pattern to break the complex data into simpler forms. They work based on their shared weights (on each layer of the neuron) and other characteristics. They require less preprocessing and are highly complex but give a far better visualization.
In classical computing techniques, we use bits to encode any information and the bits are 0 and 1 but when we come to quantum computing the mechanism changes. The quantum bit generally referred to as a qubit is the basic core unit that represents the two states or how the information can be encoded. These two states are represented by |0> and |1> symbols and a qubit can be in |0> state or |1> state or in a linear combination of both the states, explaining superposition. There are various theories and cases proposed which show that a qubit is possible just like Schrodinger’s Cat, which was a hypothetical case, we have polarisation of photons, superconducting Transmon qubit, which are real examples that show the possibility of the qubit and explain the principle of superposition.
These define the phase in which the particle can be present. Quantum Machines use qubit as the basic source of information and qubit determines the output of the machine and the state in which it is present.
The qubits can be present in |0> state or |1> state or it can be present in the combination of both the states and the ability of qubits to present in the combination of both the states is termed as superposition as the machine can be found at two states or phases at the same time. This is quite interesting if we will notice it clearly and with the superposition principle, quantum machines can solve various difficult problems. It means that the machine can hold information using a system that is present in a combination of two states or two states at the same time. It provides detailed information about a system in all the possible states hence data interpretation becomes more valuable as we have deep insight into our data and we can get each detail of it.
Here we can measure and evaluate data in broad respect. With all the states in which the information can be available, it becomes easy to get solutions to various queries that might not have come to notice before.
The interaction or strong correlation between two or more particles is termed as entanglement. If a large distance separates particles, the correlation between them becomes strong, and this helps to study their properties by keeping them in various sample environments and one thing is noted that they attract each other when kept in opposite places or ends of the sample space.
This might seem impossible given how a particle is dependent on others but is true. All particles in a sample space or to be exact, all particles in this universe are dependent on all other particles in their environment and this determines their properties. It states that a pair of qubits exist in a single phase and change their states dependently, i.e., if one changes its state the other has to change its state also.
In Quantum Machine Learning it is used for networking. To transfer data, the networks used are dependent on quantum entanglement. It is also used in communication through the use of photons to transfer information. It is a much faster way of communication which can be used in machines for effective and speed data transfer. This has been proven the best way for machines to communicate through the networks and photons mechanism for data transfer at high speed and low or negligible noise in data. Both the techniques can be used depending upon the type of system on which we want to implement it. This has increased the working capability of machines.
Till now, we have been talking about different factors of Quantum Mechanics affecting and influencing Quantum Machine Learning in one or the other way, but that’s only one side of the coin which we were looking at. The other side of the coin is also a vast domain which makes the machines to learn on their own so that no spoon-feeding of algorithms are required each time and this is known as Machine Learning.
Machine Learning has brought advancement significantly, making the machines robust so that they have their capabilities and can manage tasks on their own, without human intervention. It requires training machines on some sample data sets so that they can draw patterns and/or trends from the data set provided which will help them to learn how to evaluate data and give an output when some actual data set would be provided in the future. This has led to drastic changes in technology and approach to making and training machines. With this broad concept, manual work has been reduced and logical work has increased to attain a more technical hand on the existing technology and make it better. Along with various other technologies like Deep Learning, Neural Networks, Big Data, Data Mining, Artificial Intelligence, and much more, Machine learning has been able to transform machines so that they work, think, and act like humans. This is the main requirement, to make machines think and work like humans when they have been provided input for evaluation as this makes them think from all the possible perspectives so that the final output satisfies most of the requirements.
Quantum Computers provide a hand over speed, they perform computations very fast, and with such incredible computational power, they are in high demand. This is the current demand, i.e., Quantum Machine Learning is emerging to make such computational machines that work at a higher pace and have the capability to perform tasks on their own. This is the brief domain where quantum machine learning stands. It has been shaped lots of times, to include all the necessary domains and the output gives that it is the combination of Quantum Theory and Machine Learning by which machines have the capability of performing faster computations on their own.
Quantum Machine Learning is used in all the fields whether it would be related to business and marketing, health, weather forecasting, and much more.
In this way, quantum machine learning covers almost all the domains and hence is an essential domain itself that can evolve all the technological stuff. Machine Learning itself is a vast domain that includes various factors to be considered, some of which are listed below.
Linear Algebra – To solve complex linear algebraic equations we require advanced computers to solve this problem at a higher pace and this is where quantum computers are used. Quantum computers speed up the process of computation and solve even the hardest problems at a higher rate. This has made computing easy and efficient in the best possible way.
Optimization – To solve problems most effectively and efficiently possible, quantum machine learning is used. With high technology, quantum computing, and time-saving requirements, the need for optimization gradually becomes the priority. Hence, to solve this problem and make our system more advanced, quantum machine learning is used which provides a highly optimized mechanism to solve a query.
Deep Learning – Along with Machine Learning and Artificial Intelligence, Deep Learning is one of the most important technologies for Quantum Machine Learning. It is necessary to train our machines properly with the provided sample data set so that it can draw patterns and trends from the data set and will give an accurate output when the actual data set would be evaluated. For that, we need to make the machine to understand the data properly and this is done with the help of Deep Learning. It boosts the performance of the machine because if the machine understands the data in a better way then it would be able to draw accurate patterns and trends from the data within a shorter duration.
Future Scope of Quantum Machine Learning
Within the coming years, as technology will rise, the domain of Quantum Machine Learning would be covering all other domains in one or the other way. There would be machines that would be evaluating inputs and data sets on their own, generating outputs of the most difficult problems, computing various high-level inputs, and doing all this at a higher pace. The business would be growing and tasks would be completed in the minimum amount of time possible.
This would widen the scope for new machines with the base of Quantum Computing and Machine Learning and a pinch of Artificial Intelligence. This would revolutionize technology and make things compatible, effective, efficient, and satisfying.
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