Discussion – 


Discussion – 


How TensorFlow Quantum can help with Machine Learning challenges

We are in the 21st century, and the world has been digitalized so much in this century. We are going to talk and understand about Machine Learning in this article and will know the different frameworks of Quantum Machine learning.  Machine Learning can be also known as a subfield of Artificial Intelligence(AI). 

Machine Learning is a scientific process where the study of statistical and algorithms models used to perform a specific task without any specific instructions or patterns. The American Pioneer, Arthur Samuel, who was in the field of Artificial Intelligence and Computer gaming, introduced the term “Machine Learning” in 1959 and now there is tremendous growth in this field. 

Tensor flow Quantum is a recently launched program that is very useful and helps to bring together quantum computing initiatives and machine learning. Tensor flow quantum is an end to end open source Machine learning that helps to solve many challenges.

Design of Tensor Flow Quantum

To solve the problems of NISQ-era quantum machine learning, TensorFlow Quantum (TFQ) is designed. In the design of Tensor flow, different models and operations used quantum computing primitives to the tensor flow ecosystem which creates powerful quantum-classical hybrid systems. By combining both traditional and quantum computing techniques, Tensor flow quantum (TFQ) helps users and developers to create hybrid Artificial intelligence algorithms.

TensorFlow Quantum primitives

TFQ has two datatype primitives.

  1. Quantum circuit: Quantum Circuit represents Cirq-defined quantum circuits-cirq.Circuit within TensorFlow. That Creates batches of circuits of different size, that includes similar batches of different real-valued data points.
  2. Pauli sum: Pauli sum Represent linear combinations of tensor products of Pauli operators that are defined in Cirq. Like circuits, that create batches of operators of different sizes.

TensorFlow Quantum helps to face different kinds of challenges of Machine learning. Some of them are mentioned below.

1. Solves Complex Problems Faster

With the help of different and growing size, the computers with Tensor quantum can complete calculations within seconds of large data. quantum computers use “qubits, but traditional computers use “0” and “1”, which takes more time to calculate the same data.

2. Handling Large datasets

Quantum Computers are designed to manage huge data within a short period of time, that helps newer technologies like AI and Machine learning programs without error and developers are now able to manage large data sets more efficiently.

3. Combat Fraud Detection

Since many of the banks and financial sectors are using AI. Using the application of Tensor Flow Quantum helps the application to detect frauds.

4. Integration of Multiple datasets

Handling large and different kinds of data is one of the main and tough jobs to handle for any company or organization, sometimes data could be too small or too large. To solve and handle these kinds of data sets Tensor flow Quantum computers can be used that makes the analysis easier and faster.

5. Error Correction

Error correction is one of the main issues faced by many of the organizations. The Tensor flow quantum system is used to protect and correct.

Apart from these, there are many more challenges like Sensitivity to Interaction with The Environment, Wrapping Up, Building Better Models etc.

Since Tensor flow Quantum focuses on Open source philosophy, it makes easier to use and manage the software and systems. Using this helps to combine Traditional and Newer technologies together which helps for Organization, banking sectors and all over hum society.

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