Abstract
Computational technologies drive progress in industry, science, government, and society. While these technologies form the foundation for intelligent systems and enable scientific and business innovation, they are also the limiting factors for progress. Quantum computing promises to overcome these limitations with better and faster solutions for optimization, simulation, and machine learning problems. Quantum computing is broadly applicable to business problems in optimization, machine learning, and simulation, impacting all industries. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Therefore, it is instrumental for industry to seek an active role in this emergent ecosystem. In this chapter, the authors present a brief overview on various applications of quantum computing in machine learning.
Top1. Introduction
The proliferation of personal computers and the emergence of the Internet have fueled the need for more and more computing power. The trend for decades has been packing more computing power into smaller spaces. People are turning to different ways of computing, such as quantum computing, to find ways to cut energy use. It is believed that quantum computers will harness the power of atoms and molecules to perform memory and processing tasks. Quantum computers could be exponentially faster at running artificial-intelligence programs and handling complex simulations and scheduling problems.
Quantum computing is the study of information processing tasks using quantum mechanical principles. It combines ideas from classical information theory, computer science, and quantum physics. It is commonly believed that QC holds the key to true artificial intelligence.
One of the significant advantages of quantum computation is the ability of massively parallel computation. By using a quantum superposition state, 2𝑛 inputs can be stored in n qubits simultaneously. Since universal quantum gates allow us to design an arbitrary quantum circuit, the n qubits can be used as the input for a quantum circuit, which performs an arbitrary computation. For example, four classical values {0, 1, 2, and 3} can be stored in two qubits simultaneously, which can be written as the state (6). For example, a circuit can be designed to compute (𝑥)=𝑥+5. It seems that four computations can be performed with only one step by placing the qubits in the superposition states in the circuit. However, the output state is a superposition state of four possible output values {5, 6, 7, and 8}. The result of the measurement on the output qubits is one of the four possible outputs. In short, when a classical logic is implemented as a quantum circuit, the output qubits are the superposition of 2𝑛 outputs for 2𝑛 inputs.
The interest in using quantum computers to compute artificial intelligent algorithms has increased exponentially with the many successful implementations of AI showing that quantum computers can be relied for calculations.
Quantum machine learning is the integration of quantum algorithms within machine learning programs (Schuld et al., 2014; Wittek, 2014; Biamonte et al., 2017). The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning. While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program (Perdomo-Ortiz et al., 2018). This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device. Beyond quantum computing, the term “quantum machine learning” is also associated with classical machine learning methods applied to data generated from quantum experiments (i.e. machine learning of quantum systems), such as learning the phase transitions of a quantum system or creating new quantum experiments (Wiebe et al., 2014). Quantum machine learning also extends to a branch of research that explores methodological and structural similarities between certain physical systems and learning systems, in particular neural networks (Huembeli et al., 2018).
The chapter is organized as follows. In section 2, the authors have presented a brief literature review on the existing papers which are present in this area. In the subsequent section 3, authors have discussed on a few basic concepts related to the quantum computing. Further in section 4, a discussion has been made on the approaches taken to enhance the application of machine learning through quantum computing. In section 5, authors have illustrated the different machine learning applications in quantum computing. Finally a conclusion to the paper is drawn in section 6.
Key Terms in this Chapter
Intelligent Systems: An intelligent system is a machine with an embedded, Internet-connected computer that has the capacity to gather and analyze data and communicate with other systems. Similarly, intelligent systems can also include sophisticated AI-based software systems, such as chatbots, expert systems and other types of software.
Optimization: An optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete. An optimization problem with discrete variables is known as a discrete optimization, in which an object such as an integer, permutation or graph must be found from a countable set. A problem with continuous variables is known as a continuous optimization, in which an optimal value from a continuous function must be found. They can include constrained problems and multimodal problems.
Quantum Computing: Quantum computing is an area of computing focused on developing computer technology based on the principles of quantum theory (which explains the behavior of energy and material on the atomic and subatomic levels). Computers used today can only encode information in bits that take the value of 1 or 0—restricting their ability. Quantum computing, on the other hand, uses quantum bits or qubits. It harnesses the unique ability of subatomic particles that allows them to exist in more than one state (i.e., a 1 and a 0 at the same time).
Simulation: A simulation is the imitation of the operation of a real-world process or system over time. Simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time. Often, computers are used to execute the simulation.
Machine Learning: Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.