Want to try out quantum computing without buying expensive equipment? You’re in luck! There are free cloud-based quantum machine learning software to help. In this guide, we’ll check out the most popular software for quantum machine learning in 2024. These tools are easy to use and perfect for anyone curious about exploring this exciting field. By making them free and accessible, we’re opening doors to new discoveries in AI and more. Our selection criteria are based on several factors, including user reviews, ease of use, and the range of quantum computing capabilities offered. By considering these aspects, we’ve curated a list that reflects the current landscape of free cloud-based quantum machine learning tools. Let’s dive in and see what quantum-powered machine learning can do!
Table of Contents
Project Q
ProjectQ provides native support for various hardware platforms, making it versatile for different quantum computing environments. Additionally, users have access to FermiLib, a library that facilitates code access and provides comprehensive documentation. Furthermore, ProjectQ tools enable users to identify and address anomalies effectively, enhancing the overall user experience and ensuring smooth operations in quantum computing tasks.
Project Q Features
- Write quantum programs thanks to a high language level.
- Different backend for hardware and software.
- Compiler is customisable and modular.
- Library for solving issues.
- Python implemented framework.
Project Q Pros
- Native support for other hardware platforms.
- Automation for project planning.
- Tools to keep project plans up to date.
- Can be used for AI prediction
- Powerful syntax.
Project Q Cons
- Long queue times for experiments because of the limited availability of quantum hardware.
- Steep learning curve.
- Noisy.
- Limited resources, so experimentation is limited.
- Dependent on the internet.
Project Q Pricing
| Plan | Cost |
| Full Service | Free |
Software suitable for: Small Businesses, Startups
Perceval
Perceval is one of the finest quantum machine learning tools, this platform’s focus is to develop new-generation quantum computers, based on light manipulation. It is an open-source framework for programming computers, offering quantum machine learning tools to create circuits using a single photon source. It uses quantum simulation to create a new generation of quantum algorithms.
Perceval Features
- This platform aims to provide tools for developing quantum photonic circuits.
- Users get back-end algorithmic access both numerically and symbolically.
- Users can design their own algorithms.
- It creates a new generation of quantum algorithms using quantum simulation.
Perceval Pros
- Develops new generation quantum computers.
- Open source.
- Relatively easy learning curve.
- Back-end algorithmic access.
Perceval Cons
- Free software has its limitations.
- This tool focuses mostly on version control systems and ignores other repositories.
- The accuracy of the data gathered is questionable.
- Gathering data from multiple sources potentially impacts overall system performance.
- Functionality depends on the availability and reliability of APIs and external services.
Perceval Pricing
| Plan | Cost |
| Full Service | Free |
Software suitable for: Software Development teams, Project Managers
QuTip
This quantum computing software comes with open systems and a library that depends on numerical packages to provide a graphical output. QuTip looks to provide users with friendly numerical solutions and is found in physics applications like quantum optics and superconductivity circuits. Available for Mac, and Linux, it is the best for exploring quantum mechanics details.
QuTip Features
- Quantum computing software package with open systems.
- Graphical output.
- Friendly numerical solutions.
- Free.
- Available for Mac, and Linux.
QuTip Pros
- The library depends on numerical packages hence accuracy is a given.
- Provides users with friendly numerical solutions.
- Over a thousand tests with different functions.
- Well-tested platform.
- Range of tools for simulating quantum systems, including quantum optics, open quantum systems, and quantum information processing.
QuTip Cons
- Steep learning curve.
- Limited support for hardware implementation.
- Performance constraints, especially if simulations involve a large number of qubits or complex interactions.
- No comprehensive documentation and support resources.
- Integration with other tools is difficult.
QuTip Pricing
| Plan | Cost |
| Full Service | Free |
Software suitable for: Customer Support Teams
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IBM Q-experience
IBM Q Experience is a cloud-based platform developed by IBM that provides access to quantum computing resources for researchers, developers, and enthusiasts. It allows users to experiment with quantum circuits, run quantum algorithms, and explore the potential of quantum computing firsthand.
IBM Q-experience Features
- Cloud-based platform.
- Program and run quantum circuits.
- Access to actual quantum hardware.
IBM Q-experience Pros
- Backed by the research computation power of IBM.
- Access to actual quantum hardware to run quantum circuits and experiments.
- Users can simulate quantum circuits on classical hardware.
- Integration with Qiskit.
IBM Q-experience Cons
- Limited access to quantum hardware and processors.
- Steel learning curve.
- Prone to errors
- Free tier has limited resources.
- Needs a stable internet connection.
IBM Q-experience Pricing
| Plan | Cost |
| Full Service | Free |
Software suitable for: Researchers, and Developers
Tensorflow Quantum
This Google-developed library boosts the creation of quantum machine learning models in TensorFlow. Relatively easy to use, this platform is a must for those wanting to create content.
Tensorflow Quantum Features
- Google developed library.
- Free software.
Tensorflow Quantum Pros
- Easy to use.
- Great for those looking to create content.
- High-level interface so you can construct quantum circuits and integrate them into classical machine learning models.
- Needs a stable internet connection.,
Tensorflow Quantum Cons
- Not a great performer.
- A high-level interface might be difficult for beginners.
- Steep learning curve.
- Not a very stable architecture.
Tensorflow Quantum Pricing
| Plan | Cost |
| Full Service | Free |
Software suitable for: Software Development teams, Project Managers
PennyLane
Pennylane is an open-source software library for constructing and training quantum machine-learning models. YOu get a unified interface for different quantum hardware and simulators so researchers and developers can develop and test their algorithms on a variety of platforms. It also has a robust architecture, which makes it extremely stable and has a proven track record.
PennyLane Features
- Open source.
- Build and train quantum machine learning models.
- Develop and test algorithms on a wide variety of platforms.
- Robust architecture.
- Great for constructing and training quantum machine learning models
PennyLane Pros
- Construct and train quantum machine learning models.
- Build and train quantum machine learning models.
- Robust architecture so it never hangs or crashes.
- Develop and test algorithms on a wide variety of platforms.
PennyLane Cons
- Free software has its limitations.
- Unified interfaces may not always be working well.
PennyLane Pricing
| Plan | Cost |
| Full Service | Free |
Software suitable for: Researchers, and Developers
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pyQuil
Brought to us by Rigetti Computing, pyQuil is a library for quantum programming in Python, with a simple interface for constructing and simulating quantum circuits, and allows for the creation of hybrid quantum-classical models for machine learning.
Pyquil Features
- Suite of tools for developing and running quantum applications.
- The robust interface does not hang easily.
Pyquil Pros
- Simple interface.
- Allows for the creation of hybrid quantum classical models for machine learning.
- Tools for developing and running quantum applications.
- The robust interface is not prone to hanging.
- The easy learning curve for beginners.
Pyquil Cons
- Free software has its limitations.
- Limited quantum hardware support because – PyQuil interfaces primarily with Rigetti’s quantum computing platform.
- Not compatible with others.
Pricing
| Plan | Cost |
| Full Service | Free |
Software Suitable for: Researchers, Developers, Educators and Students
Qiskit Machine Learning
Qiskit Machine Learning is a powerful and versatile open-source software development kit (SDK) provided by IBM for quantum computing and machine learning integration. It offers a range of tools and libraries that enable researchers, developers, and enthusiasts to explore the intersection of quantum computing and machine learning. This open-source framework for programming quantum computers is an extension that clubs quantum machine learning algorithms to its toolkit.
Qiskit Machine Learning Features
- Wide range of functionalities including quantum circuit design, optimization, simulation, and access to quantum hardware.
- Modular architecture so users can easily extend its functionality and integrate it into their quantum computing workflows.
Qiskit Machine Learning Pros
- Easy to use thanks to the simple user interface.
- Integration with IBM Quantum Experience.
- Active community of developers, researchers and enthusiasts.
- Support for quantum computing tasks like quantum machine learning, quantum algorithm development, and quantum error correction.
- Active development with regular updates.
Cons
- Complex, not for beginners.
- Access to real quantum hardware via Qiskit might be limited.
- Navigating and understanding the documentation is a challenge.
- Running quantum simulations can be computationally intensive, stressing local servers.
- Dependent on various Python libraries and packages.
Pricing
| Plan | Cost |
| Full Service | Free |
Software Suitable for: Freelancers, Startups
Azure Quantum from Microsoft
Azure Quantum from Microsoft gives you a full suite of quantum developer tools and simulators so you can run programs on an ion trap quantum processor from IonQ. You get libraries and SDKs for quantum software development. It uses Q#, which is Microsoft’s language for quantum programming. It simulates quantum algorithms and has quantum development plugins.
Azure Quantum Features
- You get a range of quantum developer tools and simulators.
- Libraries and SDKs for quantum software development.
- Uses Q#, which is Microsoft’s language for quantum programming.
- It simulates quantum algorithms and has quantum development plugins.
- Has a simple interface.
Azure Quantum Pros
- Exclusive quantum developer tools and simulators that others don’t have.
- Supports quantum software development.
- Uses Q#, which is a robust and stable language for quantum programming by Microsoft.
- Quantum development plugins.
- Easy to use.
Azure Quantum Cons
- Not a very robust quantum architecture.
- Limited scope for quantum software development.
- Q# is not very stable, as a quantum programming language.
- A simple interface has some limitations.
- Limited availability of quantum hardware which leads to queue times for accessing quantum computing resources.
Azure Quantum Pricing
| Plan | Cost |
| Full Service | Free |
Software Suitable for: Developers, Researchers, and Enterprises
Amazon Braket
This platform delivers access to quantum computing hardware from vendors like IonQ, D-Wave, and Rigetti. To get started with quantum programming, it provides Jupyter notebooks. With a robust architecture and excellent quantum computing capabilities, Amazon Braket is popular among developers, researchers, and others.
Amazon Braket Features
- Integration with Amazon Web Services. So you can leverage their scalable infrastructure and management capabilities.
- Quantum simulators so you can test and debug quantum algorithms prior to running them on actual quantum hardware.
- Supports popular quantum programming frameworks like Qiskit and Rigetti.
Amazon Braket Pros
- Support for multiple quantum hardware providers.
- Cost-effective.
- Secure infrastructure thanks to AWS.
Amazon Braket Cons
- Steep learning curve.
- Limited hardware access.
- Overdependence on cloud connectivity.
- Costs for data transfer.
- Might face vendor lock-in
Pricing
| Plan | Cost |
| Full Service | Free |
Software Suitable for: Developers, Researchers, and Scientists
| S.No | Software Name | Pricing | Download Links | Compatible with |
| 1 | Project Q | Free | Link | Small Businesses, Startups |
| 2 | Perceval | Free | Link | Software Development teams, Project Managers |
| 3 | QuTip | Free | Link | Customer Support Teams |
| 4 | IBM Q-experience | Free | Link | Researchers, and Developers |
| 5 | Tensorflow Quantum | Free | Link | Software Development teams, Project Managers |
FAQs
What is Free Cloud-based Quantum Machine Learning Software?
Free Cloud-based Quantum Machine Learning Software is software that helps you perform cloud computing.
What are the benefits of cloud-based quantum computing?
Conduct research on the latest hardware and software without the costs of building and maintaining either.
Is IBM Quantum Free?
The Lite plan allows access to only quantum simulators and is free of charge.
