Tensorflow Quantum

Introduction to Tensorflow Quantum

• The Tensorflow Quantum (i.e., TFQ) is a quantum machine learning library introduced for speedy prototyping of fusion (hybrid) quantum-classical ML models.
• Even research in quantum algorithms and applications may control Google’s quantum calculating frameworks, which are all from inside TensorFlow.
• The Tensorflow Quantum emphasizes quantum data and constructing hybrid quantum-classical models. It incorporates Quantum calculating algorithms and logic intended in Cirq, delivering quantum primitives friendly with prevailing Tensorflow APIs, laterally with high presentation quantum circuit simulators.

Tensorflow Quantum Motivation

With the attainment of Quantum Supremacy, the Quantum calculating at Google has smashed an exciting milestone. In the come round of this illustration, Google is now considering creating and executing new algorithms for running on its Quantum Computer, which has real-world applications.

To deliver users the necessary tools they require to program and pretend to be a quantum computer, Google is functioning on Cirq. Here, Cirq is aimed at quantum computing researchers concerned with running and projecting algorithms that influence prevailing quantum computers.

Tensorflow Quantum offers operators the tools they want to interweave quantum algorithms and logic intended in Cirq with the authoritative and performant ML tools from Tensorflow. Hence, we expect to reveal new and stirring tracks for Quantum Computing research that would not have been promising without this association.

How does Tensorflow Quantum work?

The TFQ (Tensorflow Quantum) agrees with scientists to build quantum datasets, classical control parameters, and quantum models as tensors in a particular computational graph. With the help of TensorFlow Ops, one can acquire the result of quantum measurements, foremost to classical probabilistic events. Training will be performed using normal Keras functions.

To deliver a few perceptions on how to implement quantum data, anyone can deliberate an administered organization of quantum states using a quantum neural network. Similar to classical ML, one of the main tasks of Quantum ML defines to categorize “noisy data.” For building and teaching like a model, here the researcher performs the below tasks:

1. Organize a quantum dataset: The Quantum data is arranged as tensors, i.e., a multi-dimensional array of statistics. Every quantum data tensor defines as a quantum circuit scripted in Cirq, which produces quantum data on the fly. Thus, the tensor is implemented by TensorFlow to produce a quantum dataset on the quantum computer.
2. Estimate a quantum neural network model: The scientist can prototype a quantum neural network using Cirq, which they can embed later within a Tensorflow compute graph. We can select the parameterized quantum models from various broad categories based on quantum data structure knowledge. The aim of the model states to execute quantum processing to extract data info secreted in a normally entwined state. Generally, the quantum model principally separates the input quantum data, parting the secreted data info encrypted in traditional correlations, hence creating it reachable to the local dimensions and traditional post-processing.
3. Sample/Average: the measurement of Quantum defines extracts traditional data info as samples from a traditional random variable. From this unsystematic variable, the delivery of values is done based on the quantum state itself along with the measured noticeable. As several variation algorithms can be determined by mean measurements, even as expectation values, the TFQ offers techniques for averaging various runs consisting of steps first and second.
4. Estimate a traditional neural networks model: After the traditional data info has been mined, it is in an agreeable design to traditional auxiliary post-processing. Moreover, since the mined data info may still be encrypted in traditional correlations concerning measured expectations, traditional deep neural networks may be used to refine such correlations.
5. Estimate Cost Function: We can estimate the cost function provided by the outcomes of traditional post-processing. This depends on how precisely the model executed the classification task when the quantum data was considered or additional criteria when the task was unverified.
6. Estimate Gradients & Modernize Parameters: After estimating the cost function in the previous step, the unrestricted parameters in the pipeline should be modernized in a direction predictable to reduce the cost. It is the utmost normally executed through gradient descent.

Install TensorFlow Quantum

You will find some techniques to configure your environment to implement the TFQ (Tensorflow Quantum) mentioned below:

• The simplest technique to learn and apply TFQ needs no installation – execute the Tensorflow Quantum tutorials openly in the browser using Google Colab.
• We can install the TFQ package using Python’s pip package manager to implement Quantum on a local machine.
• Or even construct Quantum from the source.

TFQ is maintained on Python 3.8, 3.7, and 3.6 and rests openly on Cirq.

Tensorflow Quantum Issues

A few issues reported on Tensorflow Quantum are listed as follows:

1. Problems of installation
2. Complex128/float64 support for tfq
3. Supporting channels in fidelity op
4. Document how to apply TFQ on simulated chips or physical chips
5. Further documentation difficulties
6. Eliminate implementation of finite differences in Adjoint gradient
7. Provision of NamedQubit and LineQubit in TFQ
8. Create Unitary calculations Differentiable
9. Apply MPS ops below tfq.math
10. Design-CUDA support
11. For serialized circuits, design and enhance proto wire size.
12. Electric Boogaloo-Error found in TFQ API Rendering 2
13. ‘Cirq.sim.simulator’ includes no attribute such as ‘SimulatesExpectationValues.’
14. Fitting flaky gradient tests
15. Upgrade TF 2.5.0
16. Ansatz library brainstorming
17. Substitute TFQPauliSumCollector is having cirq.PauliSumCollector
18. GSOC21 Project Probability/Support
19. How to perform regression using gradient?
20. Upgrade & repair yapf
21. C++ compilation warnings setting
22. Multi-qubit X gate enhanced to TOFFOLI gate; hence serialization cannot be executed by TFQ.
23. At tf.keras.Sequential.fit() Kernel freeze
24. Using tf.jacobian, the Hessian calculation is failed
25. Tensorflow-quantum, tensorflow, grpcio dependence conflicts.

Conclusion

• A quantum model contains the capacity for signifying and simplifying data with a quantum mechanical origin.
• A Quantum is thus a Python framework dedicated to hybrid quantum-classical machine learning, which is most emphasized in forming quantum data.
• It is an application-type framework that permits quantum algorithms researchers and machine learning applications researchers to explore calculating workflows.

Recommended Articles

This is a guide to Tensorflow Quantum. Here we discuss some techniques to configure your environment to implement the TFQ. You may also have a look at the following articles to learn more –

1. TensorFlow Dataset
2. TensorFlow estimator
3. TensorFlow Debugging
4. TensorFlow Models