Quantum Computing & its applications in A.I
What is Quantum Computing?
Quantum computing is the use of quantum states’ collective features, such as superposition and entanglement, to perform computation. Quantum computers are machines that can do quantum computing.
How do Quantum computers actually work?
Quantum computers, in contrast to classical computers, execute calculations based on the probability of an object’s state before it is measured, rather than only 1s or 0s, allowing them to process exponentially more data. Classical computers use the definite position of a physical state to perform logical operations. These are usually binary, which means that their operations are limited to one of two possibilities. A bit is a single state, such as on or off, up or down, 1 or 0. Instead, operations in quantum computing utilise the quantum state of an item to produce a qubit. These are the undefined qualities of an object before they are discovered, such as an electron’s spin or a photon’s polarisation.
Unmeasured quantum states exist in a mixed superposition rather than having a definite position, similar to a coin spinning in the air before landing in your hand. These superpositions can get entangled with those of other objects, implying that their outcomes will be mathematically connected, even though we don’t know what they are yet.
Types of Quantum Computers
Building a working quantum computer necessitates keeping an object in a state of superposition long enough to perform various operations on it. Unfortunately, when a superposition collides with materials that are part of a measuring system, it loses its in-between state and becomes a boring old classical bit, which is known as decoherence. Devices must be able to protect quantum states from decoherence while also allowing them to be read easily. Different approaches are being taken to address this problem, such as using more resilient quantum processes or finding better techniques to detect faults.
What are the possibilities of applying quantum computing in AI?
Although artificial intelligence has advanced rapidly during the last decade, it has yet to overcome technological limits. Obstacles to achieving AGI (Artificial General Intelligence) can be overcome thanks to the unique qualities of quantum computing. Quantum computing can be used to train machine learning models quickly and generate more efficient algorithms. In conjunction with the University of Waterloo, X, and Volkswagen, Google just announced TensorFlow Quantum (TFQ), an open-source toolkit for quantum machine learning. TFQ’s goal is to give people the tools they need to control and model natural and artificial quantum systems. TFQ is an example of a toolkit that blends quantum modelling and machine learning.
Convert quantum data to the quantum dataset: Quantum data can be represented as a multi-dimensional array of numbers which is called quantum tensors. TensorFlow processes these tensors to represent create a dataset for further use.
Choose quantum neural network models: Based on the knowledge of the quantum data structure, quantum neural network models are selected. The aim is to perform quantum processing to extract information hidden in an entangled state.
Sample or Average: Measurement of quantum states extracts classical information in the form of samples from the classical distribution. The values are obtained from the quantum state itself. TFQ provides methods for averaging over several runs involving steps (1) and (2).
Evaluate a classical neural networks model — Since quantum data is now converted into classical data, deep learning techniques are used to learn the correlation between data.
These steps make sure that an effective model is created for supervised or unsupervised tasks.
Quantum algorithms for learning: Development of quantum algorithms for quantum generalizations of classical learning models. It can provide possible speed-ups or other improvements in the deep learning training process. The contribution of quantum computing to classical machine learning can be achieved by quickly presenting the optimal solution set of the weights of artificial neural networks.
Quantum algorithms for decision problems: Classical decision problems are formulated in terms of decision trees. A method to reach the set of solutions is by creating branches from certain points. However, when each problem is too complex to be solved by constantly dividing it into two, the efficiency of this method decreases. Quantum algorithms based on Hamiltonian time evolution can solve problems represented by several decision trees faster than random walks.
Quantum search: Most search algorithms are designed for classical computing. Classical computing outperforms humans in search problems. On the other hand, Lov Grover provided his Grover algorithm and stated that quantum computers can solve this problem even faster than classical computers. AI-powered by quantum computing can be promising for near term applications such as encryption.
Quantum game theory: Classical game theory is a process of modelling that is widely used in AI applications. The extension of this theory to the quantum field is the quantum game theory. It can be a promising tool for overcoming critical problems in quantum communication and the implementation of quantum artificial intelligence.
What are the critical milestones for quantum AI?
Even though quantum AI is still a young science, advances in quantum computing have increased quantum AI’s potential. However, to mature as a technology, the quantum AI sector needs to reach certain milestones. These milestones can be summarized as:
Less error-prone and more powerful quantum computing systems
Widely adopted open-source modelling and training frameworks
Substantial and skilled developer ecosystem
Compelling AI applications for which quantum computing outperforms classical computing.
These critical steps would enable quantum AI for further developments.