Quantum Machine Learning- an amalgamation of two breakthroughs in the world of science and technology domain, namely Quantum Physics and Machine Learning/ Artificial Intelligence- has been in the air since quite a while now and is supposed to present a new dimension to how we solve ML problems using the concept of quantum computing. This article focuses on what quantum is, how it can change the way we know machine learning as of now, its basic functional blocks, how it achieves its high computational power and the applications of quantum machine learning in a real-world scenario.
What is Quantum?
In terms of Physics, a quantum is a unit that is smaller than an atom; things at that minuscular level do not behave the same way the objects around us behave. The basis of the quantum theory is that these particles can change their state and be in any state at a given point of time. Quantum mechanics is crucial to understand the behaviour of microscopic as well as large physical systems.
Quantum computing is the use of quantum- mechanical phenomena like superposition to perform computations which are faster and perform better with respect to space and time complexities. Proving themselves to be better than classical computing techniques, quantum computing when used in the field of machine learning gives rise to Quantum Machine Learning.
Why Quantum Machine Learning?
In 2017, Microsoft CEO Satya Nadella explained the difference in computing power and method of classic and quantum computers using an example of a corn maze. The modern-day classical computers would use the brute force and backtracking algorithm to find a path through the maze. It would choose a path, hit an obstruction, backtrack to the original starting point, choose another path and continue until it finds a way out. It will surely find a solution but at the cost of a lot of time. Imagine your mobile is draining its battery and the algorithm is running for long with no final solution.
This is where quantum computers come to rescue. They unlock amazing parallelism and traverse every path in the corn maze simultaneously to find you an optimal solution in very less time and an exponentially reduced number of steps. It’s like sending a ‘n’ number of drones to the ‘n’ number of paths and get all the results, i.e. path information in unit time.
Building Blocks of Quantum Machine Learning
-
Qubits: Just like classical computers use bits, i.e. 0 and 1 to perform operations, quantum computers use quantum bits or qubits. Qubits can be represented in one of the given ways:
- Electrons, where 1 and 0 are the excited and ground state respectively of the electron orbiting the nucleus.
- Protons, where 1 and 0 are polarizations of the photon.
One can understand the power of qubits by the fact that Google used 53 qubits in its Quantum Supremacy Experiment to demonstrate that it can perform a calculation in 200 seconds on a quantum computer that would take more than 10000 computers on the most powerful existing classical computer.
- Superposition: In quantum mechanics, every particle or quantum exhibits wave-particle duality. This means that we cannot use the wave or particle definitions alone to explain the behaviour of quantum- scale objects. Similarly, qubits exist as both 0 and 1 at the same time. This phenomenon is called superposition. Although when place under supervision to measure its energy or position, qubits lose their superposition and then exist in only one state.
- Entanglement: The state of one qubit particle cannot be described independently of the other particles even when they are separated by a large distance. If one particle in a pair is in the spin state of ‘down’ during measurement, the information is conveyed to the other correlated particle which takes the spin state of ‘up’. This phenomenon of qubits by which they interact with each other is called entanglement.
How does Quantum Computing Impose Such Amazing Level of Parallelism?
While one classical binary bit can represent only one binary configuration at a given time, i.e. 0 or 1, a qubit can assume both states (0 and 1) at the same time. Thus, ‘n’ qubits can represent 2n states which can explore 2n solutions to a problem simultaneously at a given time unlike one solution at a time as is done in a classical computer. Adding more qubits would increase the computational power of the computer exponentially, resulting in an amazing level of parallelism that has never been witnessed before.
Where Can We Apply Quantum Machine Learning?
- Model classical data on quantum computers, or create novel quantum- inspired classical algorithms for faster computation and better results.
- As the feature space of the problem domain expands, the computations become really expensive for classical computers. Using superposition and other quantum properties, quantum machine learning helps extensively in kernel evaluation and optimization.
- Quantum machine learning also has the capability of mapping the trillions of neurons in our brain and decoding the genetic makeup.
- Supervised learning and adaptive layer-wise learning with the help of quantum classifiers and neural networks
Conclusion:
Although quantum machine learning has proved to have the great computational power and an extremely high level of parallelism, it is still a relatively new concept. While significant progress and research have already been made in this direction, we are yet to tap the true potential of quantum machine learning to develop new algorithms and discover newer and better solutions to solve the real-world problems like never before.