Quantum machine learning is an emerging field in science and technology. It is the intersection of quantum physics and machine learning. It is often associated with machine learning methods applied to data generated from quantum experiments. Quantum computers will provide the computational advantage to classify objects in nth dimensions. The areas that quantum machine learning is applied is in the areas of nano-particles, material discovery, chemical design, drug design, pattern recognition, and classification. The applicable use cases are creating new materials that can be applied in space tech, wearable tech, renewable energy, nanotech, new drugs, and chemical combinations, genetic science, biometrics, IoT devices, and universe discovery. Quantum walks are a quantum analog to random walks and have substantially reduced the time-consumption in Monte Carlo simulations for mixing of Markov chains as reported by Ashley Montanaro (2015). These quantum algorithms are applied for investment strategies in wealth management and trading.
According to — Dr. Mandaar Pande, The number of bits in a 300 qubit quantum computer will be more than the known atoms in the universe. Quantum machine learning is the intersection of quantum physics and machine learning. This is related to machine learning algorithms in the programs running on a quantum computer. These algorithms are used to analyse big data. Quantum machine learning is analysing quantum states and systems. The computationally complex modules are performed on the quantum device. Quantum algorithmic design makes the device learn them instead of composing algorithms.
Quantum Machine learning is related to machine learning methods using data generated from quantum experiments. This is related to exploring methodological and structural similarities between certain physical and learning systems such as neural networks. Tacchino and co have extended Rosenblatt’s work on a quantum computer. They used IBM’s Q-5 “Tenerife” superconducting quantum processor. It is a quantum computer capable of processing five qubits. They have developed an algorithm that takes a classical vector as an input. The input is combined with a quantum weighting vector. A quantum perceptron can process 2N dimensions. A classical perceptron can process N dimensions of data.
A quantum perceptron can classify the patterns in the data provided as vector inputs. The quantum model of a perceptron is a nonlinear classifier of simple patterns. Quantum information processing uses amplitude amplification methods based on Grover’s search algorithm to solve unstructured search problems.
Quantum Machine Learning
Classical Machine Learning methods are supervised, unsupervised and reinforcement. Supervised learning uses a dataset to train a machine learning algorithm. The goal is to understand specific tasks related to classifying. A new unknown data is added based on its previous training. The learning algorithm is used to predict how to classify this new input. Quantum reinforcement learning is related to a quantum agent interacting with a classical environment. Occasionally the agent receives rewards for its actions. This helps in adapting its behavior to learn what to do in order to gain more rewards.
Quantum Machine Learning algorithms consist of well-known quantum subroutines. The subroutines might be quantum phase estimation, Grover search and fast annealing techniques through quantum tunneling.
Quantum annealing is related to determining the local minima and maxima of a function. This is different from simulated annealing by the Quantum tunneling process. Quantum Tunneling is a process by which particles tunnel through kinetic or potential barriers from a high state to a low state. Quantum annealing starts with superposition of all states in a system. The Schrödinger equation guides the time evolution of the system. It helps serve to affect the amplitude of each state as time increases. The ground state provides the instantaneous Hamiltonian of the system. Quantum annealer is used to solve quadratic unconstrained binary optimization problems.
ASICs and FPGAs are using machine learning and artificial intelligence. They have slim architectures which reduce the overhead of a central processor. Quantum Devices are used to run simple quantum circuits. They are made similar to Field-Programmable Gate Arrays. The hybrid quantum technique of variational circuits is used to evaluate a hard-to-compute cost function.
QNN is a quantum circuit with trainable parameters which are continuous. Quantum neural network is an expansion on Deutsch’s model of a quantum computational network. Nonlinear and irreversible gates make certain phases unable to be observed and generate specific oscillations. Quantum neural networks apply the principles of quantum information and quantum computation to classical neurocomputing. They can exponentially increase the amount of computing power and the degrees of freedom for a computer. It has computational capabilities to decrease the number of steps, qubits, and time.
Hidden Quantum Markov Model is a quantum Hidden Markov Model. They are used in fields such as robotics and natural language processing. Hidden Markov Model use probability vectors to represent hidden ‘belief’ states. HQMMs use the quantum analogue which is density matrices.
Quantum Machine Learning is used for Understanding nano-particles, Material discovery, Designing chemicals and drugs, Pattern recognition and classification, Drug Discovery, Brain circuitry simulation, genetic makeup analysis, biometrics, universe research, space tech, wearable tech, renewable energy, nanotech, and IOT devices.
Quantum support vector machines
SVM algorithms are supervised learning algorithms used for classification and regression problems. SVM’s can classify data in the nth-dimensional space. The decision is calculated using d-1. D1 is a one dimension hyperplane separating your data. For higher dimensions, a quantum computer can analyze the complex dataset for accurate classification. This algorithm is called Support Vector Machine Quantum Kernel Algorithm.SVM Quantum Kernel algorithm is related to translating it into a quantum circuit. The algorithm is about setting up how many qubits your quantum circuit will have. The classes are defined and data is imported. The parameters for how many runs and depth of the circuit is set in the code. The results are outputted after the algorithm is finished.
Generative quantum machine learning algorithms are evolving, which offer potential exponential improvement on three key elements of the generative models. The key elements of the generative models are the representational power, runtimes for learning and inference. Efficient algorithms for both training and inference are evolving to make the generative model computationally useful in real life. A hybrid quantum algorithm using a quantum Boltzmann machine is being developed to get electronic structure calculations. These algorithms can help the renewable energy industry and solar industry.