Quantum Machine Learning: On the Verge of the Next Revolution
We, humans, are inclined to look for patterns in data. Long before machines were invented, people strove to find patterns, whether it was Ptolemy looking for patterns in stars or Kepler analyzing the data of Copernicus and Brahe to reveal the pattern of planets revolving in ellipsis with the sun at the center. With the development of digital computers in the twentieth century, the data analysis techniques and pattern recognition have progressed exponentially and has further spawned the development of machine learning methods.
Machine learning algorithms have existed for a long time. From the self-flying Google car to buying recommendations on e-commerce sites, machine learning has enabled the analysis of massive quantities of data. However, the application of quantum computing to machine learning algorithms is all set to take AI to the next level.
Quantum Machine Learning: Applications
The successor to classical computers, quantum computers work on the principle of quantum mechanics. While the key informational unit of a classic computer is a bit, the quantum equivalent of it is known as a “qubit” (quantum bit). Unlike classic bits which can only be in 0 or 1, qubits encode information in both states of 0 and 1. The possibility of being in a “superposition” (Instead of thinking of particles as being in one state or changing between a variety of states, quantum mechanics postulates the quantum superposition of states, which means a particle can be in two places at once) brings to the qubit a continuous of possible states, compared with a classical bit.
This allows qubits to simultaneously calculate huge amounts of data and solve complex computations that would have taken classical computers thousands of years to calculate. The difference between a bit and qubit opens a whole new dimension in information processing.
The probabilistic architecture of a quantum computer allows the exploration of countable scenarios at once. This has raised hopes that quantum devices can provide the solution to complex problems in simulation, optimization and machine learning. Quantum machine learning is expected to speed up information processing rates, far beyond what’s possible today.
The basis of quantum learning is the Harrow-Hassidim-Lloyd (HHL) algorithm. The HHL algorithm which solves a linear system of equations in time proportional to the logarithm of the input data size can be applied to an exponential amount of data in linear time. This entails that the more data that is entered into the computer, the better are the results produced by machine learning methods. With data size not being a computational concern, quantum computing is likely to give machine learning the push in the right direction.
The other application of quantum computers in machine learning is in deep learning. Deep learning is a neural network that has contributed enormously to machine learning, helping it reach human accuracy levels in intractable tasks such as speech recognition and computer vision by mimicking how a human brain learns and adapts to new data. While quantum neural networks are still in their infancy, there’s a huge scope for improvement with intensive research.
Machine Learning Quantum Computing: The Challenges
The potential of quantum machines to outperform traditional computers is known as quantum speedup. To perform the calculation, qubits must be sustained in interdependent superpositions of states, known as quantum-coherent states. Today’s quantum computers, however, struggle to qubits in a quantum state for more than a few seconds due to thermal fluctuations, radiation and the sheer quantity of particles.
The fully functional quantum computers have been built with four to five qubits. However, to outperform the supercomputers, quantum computers must cross the theoretical threshold of 50 qubits. While IBM and Intel have already developed quantum computers with 50 and 49 qubits, Google is working on developing one at a similar scale.
Despite major challenges, quantum machine learning applications are improving quickly, much like quantum computing itself. Certainly, it is no overstatement to say that the huge interest in machine learning and artificial intelligence— and in their extremely large application domain — is fueling this progress. Tech leaders such as Google, Intel and IBM are funding billion-dollar quantum research projects because they believe in quantum power and are determined to make the quantum future happen.