5 Best Quantum Computing Frameworks

Quantum computing is a novel technology that uses quantum physics to address issues that are beyond the capabilities of traditional computers.

Many companies are now attempting to make actual quantum hardware available to tens of thousands of developers, a tool that scientists only dreamed about three decades ago.

As a result, our engineers are frequently deploying increasingly powerful superconducting quantum computers, bringing us closer to the quantum computing speed and capacity necessary to alter the world.

In this post, we’ll take a closer look at quantum computing and the tools and frameworks that go along with it, as well as where they’ll be in 2022.

What is Quantum Computing?

These supercomputers are built on the principles of superposition and entanglement, which are two aspects of quantum physics. Quantum computers can now do tasks at rates that are orders of magnitude faster than traditional computers while using far less energy.

In the 1980s, the area of quantum computing arose. Then it was revealed that quantum algorithms were more efficient than their conventional equivalents in solving certain computer tasks.

Quantum computing is a discipline of computer science that focuses on the advancement of computer technology based on quantum theory concepts. It makes use of the extraordinary ability of subatomic particles to exist in several states at once, such as 0 and 1. They are capable of processing considerably more data than ordinary computers.

The quantum state of an item is used to create a qubit in quantum computing operations. Qubits are the fundamental data units of quantum computing. In quantum computing, they serve the same job that bits do in ordinary computing, but they behave quite differently.

Traditional bits are binary and can only maintain a position of 0 or 1, whereas qubits can include a superposition of all possible states.

Best Frameworks for Quantum Computing

1. Cirq

Cirq was built by Google’s Quantum AI team. It’s used to design and improve quantum circuits that are then tested on quantum computers and simulators. Cirq is fantastic since it offers development simulators that are quite similar to those seen in real life.

This implies that the library works its way through the hardware details surrounding NISQ (Noisy Intermediate-Scale Quantum) so that we can be sure that the algorithm or circuit can be run on a real quantum computer after it’s finished.

As a result, it has the potential to be exploited to create adaptive and deployable quantum circuits. It also has interoperability features. A software that imports and exports quantum circuits and simulations, for example.

A framework for programming quantum computers that are open-source. Cirq is a Python software package that allows you to create, manipulate, and optimize quantum circuits before executing them on quantum computers and simulators.

Cirq is an efficient abstraction for dealing with today’s noisy intermediate-scale quantum computers, where hardware requirements are critical for attaining cutting-edge results.

Features

• From gates operating on qubits, you can learn how to design quantum circuits. Learn what a Moment is and how various insertion tactics might assist you in constructing your ideal circuit. Learn how to slice and dice circuits in order to create new and improved circuits.
• Technology restrictions have a significant influence on whether or not a circuit can be implemented on contemporary hardware. Learn how to program Google’s Quantum Computing Service and how to create devices to address these limitations.
• Both wave functions and density matrices have built-in simulators in Cirq. Monte Carlo or full density matrix simulations can be used to tackle noisy quantum channels.
• To execute tests on Google’s quantum processors, Cirq collaborates with Quantum Computing Service.

2. ProjectQ

ETH Zurich created ProjectQ, an open-source quantum computing software architecture. It provides a robust and straightforward syntax for users to create quantum applications in Python. ProjectQ can then convert these scripts to any form of back-end, whether it’s a classical computer simulator or a quantum processor.

ProjectQ can then convert these applications to any sort of back-ends, such as a classical computer simulator or a quantum processor, such as the IBM Quantum Experience platform. 

Features

• IT is a high-level programming language for quantum programs.
• It has a modular and adaptable compiler.
• It also offers a number of hardware and software backends.
• A quantum computer library (FermiLib) for solving fermionic issues
• The IBM Quantum Experience chip, AQT devices, AWS Braket, and IonQ service-provided devices can all be used to run quantum algorithms.
• At a higher level of abstraction, quantum programs can be emulated (e.g., mimicking the action of large oracles instead of compiling them to low-level gates)
• On classical computers, quantum programs can be simulated.

3. Tensoflow Quantum

The Python framework TensorFlow Quantum (TFQ) is for quantum machine learning. TFQ is a TensorFlow application framework that allows quantum algorithm and machine learning researchers to use Google’s quantum computing frameworks directly from TensorFlow.

TensorFlow Quantum is a program that focuses on quantum data and the creation of quantum-classical hybrid models. It combines Cirq-designed quantum computing techniques and logic with TensorFlow APIs, as well as high-performance quantum circuit simulators.

The TFQ framework can be used to run both traditional and hybrid models, such as Quantum CNN (QCNN). As a result, TFQ can be used for any problem that was previously impossible to answer using traditional approaches. To answer certain real-world problems, start with TFQ to create quantum or quantum-classical hybrid models.

Features

• Researchers can use TFQ to create tensors using quantum datasets, quantum models, and conventional control parameters in a single computational network.
• Tensors are used to store quantum data (a multi-dimensional array of numbers). Each tensor of quantum data is described as a Cirq quantum circuit that creates quantum data on the fly.
• The researcher can use Cirq to prototype a quantum neural network that will be included in a TensorFlow compute graph later.
• The capacity to concurrently train and execute numerous quantum circuits is a major feature of TensorFlow Quantum.

4. Percevel

Perceval is an open-source framework for programming photonic quantum computers developed by Perceval, a French business focusing on building a new generation of quantum computers based on light manipulation.

Perceval offers tools for composing circuits from linear optical components, defining single-photon sources, manipulating Fock states, running quantum simulations, reproducing published experimental papers, and experimenting with a new generation of quantum algorithms through a simple object-oriented Python API.

Its goal is to be a companion tool for constructing quantum photonic circuits — for simulating and refining their design, modeling both ideal and actual behavior, and offering a standardized interface to control them via the notion of backends.

It is optimized to operate on a local desktop, with many enhancements for HPC clusters, and provides access to sophisticated backends for numerical and symbolic simulation of quantum algorithms on photonic circuits.

You can also utilize a wide number of prefabricated components to create algorithms and complicated linear optics circuits. A library of well-known algorithms is accessible, as well as lessons on how to use them.

You can also use a few lines of code to execute experiments to fine-tune algorithms, compare with experimental data, and recreate published publications.

Features

• A one-of-a-kind architecture dedicated entirely toward linear optics and photonic quantum computing
• The project is an open-source project with a modular architecture that welcomes community contributions.
• Using a huge library of prefabricated components, create algorithms and complicated linear optics circuits. A library of well-known algorithms is accessible, as well as lessons on how to use them.
• Experiment with algorithms to fine-tune them, compare them to experimental data, and copy existing publications in a few lines of code.
• To emulate quantum algorithms on photonic circuits, use sophisticated backends. Perceval is designed to run on a local desktop in terms of both numeric and symbolic performance, with many enhancements for HPC clusters.

5. Qiskit

We know that if we’re talking about next-generation technology, IBM will have something to offer. It certainly does. QisKit is an open-source platform for developing quantum software.

Qiskit is an IBM-funded software framework that makes it easier for users to learn about quantum computing. Because quantum computers are difficult to come by, you can use a cloud provider like IBM’s Qiskit toolkit to acquire access to one.

It’s completely free to use, and all of the code is open source. There is an online textbook that teaches you all the fundamentals of quantum physics, which is very useful for beginners who are unfamiliar with the subject.

Quantum computers can be used at the level of pulses, circuits, and application modules.

Features

• Users of various levels can use Qiskit for research and application development because it comes with a complete collection of quantum gates and a range of pre-built circuits.
• You can use Qiskit Runtime to coordinate quantum applications on cloud-based CPUs, QPUs, and GPUs, as well as run and schedule activities on actual quantum processors.
• The transpiler converts Qiskit code into an efficient circuit utilizing the native gate set of the backend, allowing users to design for any quantum processor or architecture with minimum inputs.

Conclusion

To summarize, quantum computers can quickly penetrate today’s encryption techniques in a short amount of time, whereas the greatest supercomputer accessible now takes years.

Despite the fact that quantum computers will be capable of cracking many of today’s encryption schemes, it is expected that they would develop hack-proof alternatives. Quantum computers are fantastic at tackling optimization issues.