Quantum machine learning is the integration of quantum computing methods and algorithms within machine learning programs. According to Google Blog, it has been demonstrated that quantum machine learning can solve complex problems that are challenging for classical/traditional computers.
As our lives become increasingly data-oriented, the limitations of classical computing call for a transition to quantum machine learning. With the ability to factor and process vast amounts of datasets quickly, quantum machine learning can accelerate efficiency, decision-making, enhanced pattern recognition, improved security, and advanced modeling.
Quantum machine learning (QML) is an emerging field that will be useful for fields like supply chain management, cryptography, IT, etc.
We have listed below some of the best resources to learn Quantum Machine Learning.
QC101 Quantum Computing
Offered by Udemy, this QC101 Quantum Computing course approaches quantum physics through the study of polarized light.
In fact, it takes a math-based introduction to quantum computing while you learn quantum cryptography to communicate securely. Additionally, you get to experience the IBM quantum experience. In addition, train a quantum support vector machine to make predictions based on real-world data.
Moreover, through 12 hours of video, 10 articles, and 5 downloadable resources, you get to learn:
- How to develop and simulate quantum programs on IBM Qiskit and Microsoft Q# while debugging them
- How to analyze quantum circuits via Dirac’s notation and quantum physics models
- Likewise, how quantum computing can help in AI, machine learning and revolutionize the field of data science
This Udemy course on quantum learning is also recommended by businesses worldwide to their employees. With 17 sections and 284 lectures, this course is panned out over 12 hours.
You’ll require 12th-grade math and science knowledge to learn this course, with a special focus on Boolean logic, complex numbers, linear algebra, probability, and statistics.
Quantum ML: OpenHPI
Looking to learn how to build both basic and advanced quantum machine learning models? This quantum machine learning course by OpenHPI is free. It’s taught by Dr. Christa Zoufal, Julien Gacon, and Dr. David Sutter.
In this course, you’ll learn
- How to build basic and advanced learning models
- How to use Python and Qiskit to implement algorithms to solve ML tasks
- Challenges and future prospects of Quantum ML
Perfect for computer science students, Quantum Learning enthusiasts, and Machine Learning experts, this course will go on for two weeks, followed by a final exam you need to pass.
A look into week 1’s lecture plan tells us there’ll be lots going on with respect to support vector machines and variational quantum classifiers. Week 2 will see more of Quantum Generative Adversarial Networks and Quantum Boltzmann machines, with practical implementation techniques.
Qiskit’s Global Summer School
Next, we have up another free quantum machine learning resource that’s free and open-source. In fact, Qiskit’s lecture series is available on YouTube.
What was a two-week intensive summer school is now a YouTube learning series built over 25 episodes, each spanning an hour or two. This course is divided into 20 lectures and 5 lab-based applications.
In this course, you’ll learn
- How to explore quantum applications
- Introduction to quantum circuits, quantum computing algorithms & operations
- How to build quantum classifiers, see quantum kernels in practice
- Advanced QML algorithms, quantum hardware & how to avoid barren plateaus, and trainability issues
If you’ve been looking for free and reliable sources to start on your QML journey but haven’t so far, consider this your sign!
ML With Quantum Computers
Written by Maria Schuld and Francesco Petruccione, this book Machine Learning With Quantum Computers (2021) is a good starting point to delve into advanced quantum machine learning.
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Machine Learning with Quantum Computers (Quantum Science and Technology) | Buy on Amazon |
From near-term to fault-tolerant quantum learning algorithms, this book unravels theoretical and practical techniques on:
- Parameterized quantum circuits
- Hybrid optimization
- Data encoding
- Quantum feature maps
- Kernel methods
- Quantum learning theory
- Quantum neural networks
Now, what’s the special thing about the second edition? Additionally, how does it differ from the first edition? It goes beyond supervised learning methods and discusses the future of Quantum Machine learning methods and algorithms.
Hands-On Quantum ML With Python
Written by Dr. Frank Zickert, this book Hands-On Quantum Machine Learning With Python aims to make you a quantum machine learning expert.
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Hands-On Quantum Machine Learning With Python: Volume 1: Get Started | Buy on Amazon |
Inside, you’ll find:
- A deep dive into quantum learning fundamentals, including, but not limited to qubits, quantum gates, and quantum circuits
- How to apply Quantum Support Vector Machines (QSVM), Quantum k-means, and Quantum Boltzmann Machines to combinatorial optimization issues
- Besides, several real-life solutions to common problems like the Traveling Salesman Problem (TSP) and the Quadratic Unconstrained Binary Optimization (QUBO) problem
- How to leverage quantum fluctuations and solve problems by quantum annealing
- Also, algorithms like Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE)
- Quantum computing frameworks, real-world applications, and practical examples
Quantum ML With Python
Looking to master Quantum Machine Learning foundations? Santanu Pattanayak’s book on Quantum Machine With Python is perfect for engineers and QML enthusiasts.
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Quantum Machine Learning with Python: Using Cirq from Google Research and IBM Qiskit | Buy on Amazon |
Inside, you’ll learn:
- Quantum ML computing fundamentals like Dirac Notations, Qubits, and Bell state
- Quantum-based algorithms like Quantum Fourier transform, phase estimation, and HHL (Harrow-Hassidim-Lloyd)
- How to use QML to solve problems in finance, forecasting, genomics, supply chain logistics, etc
- In addition to Quantum adiabatic processes and Quantum-based optimization
- Use Qiskit toolkit from IBM and Cirq from Google Research to work on Quantum computing algorithms
- Use Python to implement some more Quantum-based algorithms and review major challenges of real-life applications
If you don’t want to just stop at QML resources, continue your learning journey by exploring these Quantum computing platforms:
IBM Quantum
Get free cloud access to the most advanced quantum computers online with IBM’s Quantum.
Perfect for educators, developers, and learners, IBM lets you run your quantum circuits just by signing up and getting an API token.
So, you’ll find access to simulators and 7-qubit and 5-qubit QPUs where you get the chance to learn, develop and run programs. Additionally, here’s what the IBM quantum platform lets you do:
- Learn quantum programming using step-by-step guides
- Also, use IBM Quantum Composer to help build and visualize quantum circuits graphically on quantum hardware and simulators
- Code, program, and prototype with Python in IBM Quantum lab, a cloud-enabled Jupyter Notebook environment
There’s more. You can sign up for the Quantum Researcher’s program and Educator’s program. Plus, IBM’s docs directory is also quite powerful. From Quantum Composer documentation for beginners to Qiskit Runtime for developers, you’re sure to find all you need here.
Moreover, if you’re an educator, you can leverage the Field Guide to teach topics. In addition to trying out Quantum lab tutorials to build and test algorithms as researchers.
Google’s Cirq
Google’s Cirq is a Python software library that you can use to build and optimize quantum circuits and run them on quantum hardware and simulators. Being entirely open-source, it lets you achieve state-of-the-art results using abstractions made for today’s quantum computers.
Cirq is perfect for both beginners and advanced users and has offerings suiting each. As a beginner, you can learn how to build and simulate quantum circuits to perform transformations.
As an advanced user, Cirq lets you write a quantum approximate optimization algorithm for NISQ hardware to optimize solutions that were unthinkable in classical computing. Let’s take a look at the features of Google Quantum AI’s Cirq that can empower you:
- Explore QML insertion strategies to build desired quantum circuits and better them
- Learn to define devices and hardware to determine if QML circuits are practical and won’t have operational restraints
- Simulate with Cirq or wave-function simulator qism, and mock quantum hardware and Quantum Virtual Machine
- Carry out end-to-end experiments on Google’s quantum processes and go through the code of previous simulators
What makes Cirq dependable is the step-by-step detailed tutorials and guides it features. From learning how to go about Cirq to a good list of textbook quantum algorithms to learning the ins and outs of Quantum Virtual Machine (QVM), you get to know everything paramount.
Most importantly, you can also learn how to implement Quantum Optimization Algorithms on real hardware. But that’s not all!
As it’s an open-source community, you can join weekly meetings and start contributing to the open-source framework.
Amazon Braket
Designed to accelerate quantum computing research, Amazon Braket is a fully managed service.
Here are the most important features.
- Use a consistent set of development tools to work on quantum computers
- Build quantum algorithms on a trusted cloud and test them in high-performance simulators
- Innovate with tech and expert guidance from Amazon Quantum Solutions labs
- Research algorithms & have access to superconducting, trapped ion, neutral atom, and photonic devices to test different hardware
- Build quantum software or develop open-source frameworks
You can sign up for AWS Free Tier for 1 year or get started with academic research under the AWS Cloud Credit for Research Program.
Azure Quantum Cloud Service
A cloud service incorporating quantum hardware, software, and a diverse portfolio of tools: that’s Azure Quantum cloud service.
What does this platform let you do? Let’s take a look:
- Get a better idea of executing quantum applications using the Azure quantum resource estimator tool
- Besides, mix classical computing and quantum computing methods to build hybrid algorithms
- Access educational resources like Microsoft Learn, Quantum Kata’s tutorials, and industry use cases to understand the world of QML
So, you can get started with free access to the open-source development kit that’s compatible with Q#, Cirq, and Qiskit.
How is Quantum Machine Learning Different from Regular Machine Learning?
QML differs from regular machine learning in 4 ways.
- Quantum machine learning uses qubits instead of bits to improve operational systems
- By leveraging the concepts of superimposition and quantum entanglement, quantum computers can perform multiple complex problems simultaneously
- The speed-up potential of QML is massive, and quantum computers can also handle high-dimensional data
- In the future, quantum machine learning can bring about enhanced security protocols, accelerate the development of new drugs and amplify recommendation system suggestions
Summary
While we have discussed advanced QML courses that’ll help you stay on top of what’s happening in the quantum world, you can get started with the books for a traditionally structured introduction to quantum computing.
You can also explore the 4 platforms (IBM, Google Cirq, Amazon Braket, and Azure) to have a hands-on learning experience of quantum machine learning, with access to quantum hardware and the cloud.
Most of these platforms are open-source, and if you’re looking for a community to grow with, they’d be perfect!