The only way SMEs and SMBs will likely access a quantum computer is through the cloud which can connect IT enthusiasts to a quantum-enabled device via the internet.
Table of Contents
1. Importance of Quantum Machine Learning Platforms
2. Top Three Scalable Quantum ML Platforms
2.2. Rigetti Computing: Forest
2.3. Xanadu: Xanadu Quantum Cloud
Introduction
With its enormous calculation speed, quantum computing (QC) has the potential to tackle a wide range of issues that traditional computers struggle to handle. The study of the nexus between quantum computing and machine learning is known as quantum machine learning (QML). Many small and medium-sized businesses (SMEs) and small-to-medium-sized businesses (SMBs) are searching for ways to use QML in all areas of science and technology. However, building quantum computers is a significant technical challenge; thus, with the help of QML algorithms and applications, numerous IT companies like IBM, Google, Xanadu, and many others have created several libraries and platforms that work as a subset of QC.
In this article, we will focus on why it is important to implement quantum machine learning tools and platforms in SMBs and SMEs and also take a look at the top three scalable quantum ML platforms.
1. Importance of Quantum Machine Learning Platforms
The development of machine learning models may be accelerated and enhanced with the use of quantum machine learning, which makes use of this enormous processing capacity. CEOs and IT professionals need to understand the value of QML platforms before they can integrate QML into their businesses. Here are some important QML platforms:
1.1. Accessibility
Cloud-based quantum machine learning platforms make work easy for IT professionals and CEOs. All you need is a good internet connection so that you can access the powerful capabilities of QML from anywhere.
1.2. Scalability
In the initial days, when you work with complex algorithms, you might not need much computing power. However, as you get more into details, the easiest way to tackle complex algorithmic problems is through QML platforms. Thus, with the implementation of cloud-based quantum machine learning platforms, it can easily adapt to your traditional computers, giving you more resources when you need them.
2. Top Three Scalable Quantum ML Platforms
Researchers and IT engineers have formulated a few QML cloud services that allow CEOs and IT professionals of SMEs and SMBs to have early access to quantum processing. Thus, to conduct a task properly, you need the right QML tools and applications. Here is a list of the top three QML platforms and tools:
2.1. IBM: Quantum Experience
IBM Q Experience, a product of IBM launched in May of 2016, is a cloud-based platform for programming and running quantum circuits on IBM’s quantum computers. It comes with several tools for creating and evaluating quantum machine-learning algorithms. Currently, this platform is offered on many transmon qubit processor-equipped quantum devices and simulators. Those with five and sixteen qubits can be accessed by everyone. However, the IBM Q Cloud Network offers devices up to 65 qubits.
2.2. Rigetti Computing: Forest
Forest, the first quantum-first cloud computing platform, was developed by a California-based startup IT company, Rigetti Computing. Its quantum processors (QPUs) are made available to users via the cloud and are seamlessly linked with classical computing infrastructure thanks to Quantum Cloud Services (QCS). With the help of the Forest SDK, users may create quantum programs in Quil, compile them, and execute them using a simulator.
2.3. Xanadu: Xanadu Quantum Cloud
Xanadu, an IT business started in Canada, offers Xanadu Quantum Cloud, which has three completely programmable photonic quantum computers, through cloud-based access. The company has a full-stack Python library with the codename Strawberry Fields, which is for designing, optimizing, and utilizing photonic quantum computers. This platform makes access to a unique set of near-term applications possible within quantum chemistry, finance, logistics, and cloud-based quantum machine learning.
Conclusion
Even though it could take a few years for quantum computing to reach its full potential, SMEs and SMBs should start getting ready now by learning about the technology and weighing how it will affect their businesses. SMEs and SMBs who are prepared to adapt and adopt might find themselves at a competitive edge in the future digital world as more resources for quantum education become available and as technology advances and becomes more accessible.
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