As previously covered in Data-licious, Artificial intelligence (AI) and machine learning (ML) are revolutionizing the way we approach data analysis and decision-making. From predictive modeling to natural language processing, AI and ML are being used to tackle complex problems in a variety of fields, including healthcare, finance, and transportation. However, despite the tremendous progress made in recent years, traditional AI and ML algorithms still have their limitations. Many tasks, such as optimization and classification, require enormous amounts of computational power and can be time-consuming and resource-intensive.
This is where quantum machine learning (QML) comes in. By combining the principles of quantum mechanics with the power of machine learning, QML has the potential to revolutionize the field of AI and open up new possibilities for solving complex problems. In this article, we'll explore the basics of QML, how it works, its potential benefits, challenges and limitations, real-world examples, and what the future holds for this exciting new field.
Quantum mechanics is a branch of physics that deals with the behavior of particles on a very small scale, such as atoms and subatomic particles. It is a radically different way of looking at the world compared to classical physics, which deals with larger objects that can be observed with the naked eye.
One way you can conceptualize this weird concept is through Schrödinger's cat, a famous thought experiment that illustrates some of the bizarre and counterintuitive aspects of quantum mechanics. In the experiment, you have a cat in a sealed box with a radioactive atom that has a 50/50 chance of decaying and releasing a deadly poison. Until you open the box to observe the cat, its state is described as a superposition of being both alive and dead at the same time. This means that according to quantum mechanics, the cat is simultaneously alive and dead until you observe it, at which point the superposition collapses into a definite state of either alive or dead(1).
The idea behind the experiment was to highlight the strange and mind-bending nature of quantum mechanics, which can be challenging to wrap your head around. The concept of superposition, where particles can exist in multiple states at once, is a fundamental idea in quantum mechanics but it clashes with our everyday experience of the world. Schrödinger's cat is a good example of how quantum mechanics challenges our intuition and forces us to rethink our understanding of reality.
Pulling away from theoretical stories, quantum mechanics is a highly complex and abstract theory, but at its core, it describes the behavior of particles and energy in terms of wave functions and probabilities. Unlike classical mechanics, which treats particles as distinct, independent objects with definite properties like position and velocity, quantum mechanics allows particles to exist in multiple states at the same time. This concept, known as superposition, means that a particle can be in two or more states simultaneously, and its properties are described by a wave function that gives the probability of finding the particle in a particular state.
Another key concept in quantum mechanics is entanglement, which occurs when two particles become linked in such a way that their properties are no longer independent of each other. This means that measuring the properties of one particle can instantly determine the properties of the other particle, regardless of the distance between them. Entanglement is a crucial feature of quantum computing, as it allows quantum computers to perform calculations much faster than classical computers.
Quantum + Computer
One of the key reasons why quantum computing is so promising is because it has the potential to perform certain calculations much faster than classical computing. This is because quantum computers can take advantage of two key features of quantum mechanics: superposition and entanglement.
Superposition allows qubits to exist in multiple states at the same time, which means they can represent multiple values simultaneously. This allows quantum computers to perform multiple computations at once, in parallel, rather than sequentially like classical computers. This can make certain calculations much faster, such as searching large databases or factoring large numbers.
Entanglement, on the other hand, allows qubits to become correlated in such a way that their states become dependent on each other, even when they are separated by large distances. This allows quantum computers to perform certain calculations much faster than classical computers by sharing information between qubits in a way that is not possible with classical bits.
One of the most famous use of QML is Shor's algorithm, which can factor large numbers exponentially faster than classical algorithms. This is a critical problem in cryptography, as many encryption methods rely on the difficulty of factoring large numbers. Another important quantum algorithm is Grover's algorithm, which can search an unsorted database exponentially faster than classical algorithms. Shor's algorithm is a quantum algorithm that can efficiently factor large integers, which is considered to be a difficult problem for classical computers. This algorithm was developed by Peter Shor in 1994 and is widely regarded as one of the most important contributions to the field of quantum computing(2).
The significance of Shor's algorithm lies in its ability to break many of the commonly used cryptographic systems, such as the RSA encryption scheme, which relies on the fact that factoring large integers is a computationally difficult problem for classical computers. With the advent of quantum computers, however, Shor's algorithm can factor large numbers in polynomial time, making these cryptographic systems vulnerable to attacks.
The development of Shor's algorithm sparked a flurry of research into quantum algorithms for other computationally difficult problems, such as the traveling salesman problem and the knapsack problem. While many of these algorithms are still in the development stage, the potential for quantum computers to solve these problems more efficiently than classical computers is clear.
One of the major challenges in developing quantum computers is their sensitivity to environmental factors, such as temperature fluctuations, electromagnetic fields, and other forms of noise. These factors can cause decoherence, which is the loss of the quantum state and information encoded in qubits. To overcome this challenge, researchers are exploring various approaches. One approach is to use error-correcting codes that can detect and correct errors caused by environmental noise. Another approach is to use materials and designs that can better shield qubits from environmental noise.
Researchers at IBM have developed a new material called "Holey Artifacts," which is designed to protect qubits from environmental noise. The material consists of a thin layer of silicon and germanium with a pattern of holes that trap unwanted charges and prevent them from interfering with qubits(3). In addition, researchers are also exploring the use of quantum error correction codes, which can protect quantum information against decoherence. For instance, researchers at the University of Sydney have developed a new quantum error correction code that can protect quantum information against more errors than previous codes.
As Dr. Michael Biercuk, a professor of quantum physics and quantum technology at the University of Sydney, explains
"The new code is capable of protecting against the errors that are most likely to occur in a quantum computer, such as those arising from interactions with the environment, and thus provides a critical step forward in the development of robust quantum technologies." - Dr. Michael Biercuk
While there is still much work to be done to overcome the challenges of environmental noise in quantum computing, these efforts are crucial for realizing the full potential of quantum computers in solving complex problems that are beyond the reach of classical computers.
Future of Quanta
Quantum machine learning is a relatively new and rapidly evolving field, with ongoing research and development. Experts predict that quantum machine learning could have far-reaching implications for AI and other fields. One potential area of application is in quantum chemistry, where quantum machine learning could be used to simulate chemical reactions and help identify new drugs and materials. Another potential application is in financial modeling, where quantum machine learning could help analyze complex financial data and identify patterns and trends.
There are also ongoing efforts to develop new quantum machine learning algorithms and hardware. Researchers are exploring the potential of quantum neural networks and other new approaches to quantum machine learning.
Overall, the future of quantum machine learning is exciting and full of possibilities. As the field continues to evolve, it will be interesting to see how it impacts AI and other fields, and what new discoveries and breakthroughs emerge.
"We are seeing the beginnings of a new era of computing, one where quantum supremacy is possible, and quantum computers may potentially solve problems that are impossible for classical computers." - Google's CEO Sundar Pichai