The Quantum Machine Learning Hype

Despite being at a nascent stage, quantum computing managed to make a buzz in the industry. Solving the impossible within seconds is what quantum computing promises.

Tech giants, notably IBM and Google, recently proclaimed quantum supremacy.

The question is, will it change the world?

If quantum computers are here to solve complex problems, they will need quicker improvements.

Quantum Computers: What Are They?

Quantum computers are becoming the next frontiers demonstrating capabilities a traditional computer cannot solve.

Ever wondered how the term ‘quantum’ came into existence?

Quantum computing certainly works under the principle of quantum physics perform rapid calculations using qubits and quantum bits. A total contrast of what is present in the current traditional computers. A traditional classical computer works on classical physics and performs calculations using bits of all we know. But on the other hand, quantum computers can make calculations in split seconds. Wherein in the case of a classical computer, it may take tens of thousands of years to even perform such calculations.

From drug development to weather forecasts and stock trades, quantum computers will revolutionize everything. Therefore, it should not come as a surprise as to why the world is racing to build its first quantum computer.

Promises around quantum computing making strides in the medical field. As a result, it can enable AI specialists and AI professionals working in the field to gain maximum benefit from the technology.

Google Chasing the Quantum Computing Race

In October 2019, Google said in an article they have achieved quantum supremacy.

Sycamore, a 54-qubit processor, is said to perform a calculation within 200 seconds which would otherwise take 10,000 years for the world’s most powerful computer to achieve. The achievement was seen as a long-awaited milestone that was recently achieved.

In practical terms, quantum computers can be trained like a neural network. To be more specific, a trained circuit in the quantum computer can be used to identify the content of the images. This takes place encoding these images into a physical state and taking proper measurements.

Moving further, quantum machine learning utilizes quantum computing for the computation of machine learning algorithms. Now when we talk of quantum computing, some may even get confused. Well, quantum computing is another form of computation wherein it involves three fundamental properties of quantum physics: interference, superposition, and entanglement.

Interference allows a bias quantum system to move toward the desired state. While superposition refers to when a quantum system exists in multiple states simultaneously. And entanglement is referred to as a strong correlation taking place between the quantum particles.

Now quantum machine learning works on two concepts: quantum data and hybrid quantum-classical models.

What’s Quantum Data?

It is also referred to as any type of data that can occur in both a natural and an artificial quantum system. Such data can be easily gathered or generated using quantum computers. The quantum data is said to exhibit entanglement and superposition that may lead to joining probable distributions. These distributions can further require an extensive amount of classical computational resources for storing purposes.

According to NISQ processors, the quantum data that is generated are noisy and entangled before any type of measurement takes place. Machine learning technique called Heuristic helps create models and take full advantage of the useful information from the noisy entangled data.

Also, the TensorFlow Quantum (TFQ) library supplies elements that help develop models that further disentangle correlation in quantum data.

This further provides more opportunities for the existing quantum algorithms to improve and discover newer algorithms.

Talking of the AI programming languages involved in quantum computing, we have some of the common libraries, namely Pennylane and Qiskit.

  • Pennylane — a library written in Python and can be easily integrated with Qiskit. This tool helps perform parameter-shift amidst gradient descent optimization which leads to quantum gradient descent.
  • Qiskit — an open-source library useful to quantum computers. Qiskit provides tools to create and manipulate quantum programs while running them on devices (prototyped). It functions by creating a quantum neural network using a parameterized quantum circuit through a hidden layer for the neural network.

What’s a Hybrid Quantum-classical Model?

A hybrid quantum-classical model represents and generalizes data using the quantum mechanical origin. This is because, in the near term, most quantum processors are more likely to remain noisy and small, thus making it difficult for quantum models to generalize quantum data using just the quantum processor.

To remain effective, NISQ processors need to closely work with classical co-processors.

Are there any services that allow performing quantum machine learning?

You will find multiple services available, two of which the tech giants themselves provide (Google and IBM).

  • Forest — this service is offered by Rigetti Computing. This tool suite includes development tools and programming languages.
  • Xanadu — is a hardware-based cloud started by a Canadian startup. This processor can handle 8-, 12, and 24-qubit chips.
  • IBM Q Experience — an online platform that allows its users from the general public access to a certain set of IBM’s prototype quantum processor using the Cloud.

In a Nutshell

Despite being at a nascent stage, quantum computing managed to make a buzz in the industry. Solving the impossible within seconds is what quantum computing promises the world.

Source URL:quantum computing




AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning

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Albert Christopher

Albert Christopher

AI Researcher, Writer, Tech Geek. Contributing to Data Science & Deep Learning Projects. #coding #algorithms #machinelearning

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