What Quantum Machine Learning Really Is
Think quantum machine learning is the next big thing? Fortunately, it isn't. Like rocket science, it's chaotic, uncertain, and only the brave will venture into this scientific adventure.
•5 min read
The Unprecedented Way To Learn Practical Quantum Machine Learning
Get started with quantum machine learning easily
Quantum Machine Learning: Is It Engineering Or Research?
The tension between working code and deeper meaning of learning. But what does that mean for you? Is it about building pipelines that work today? Or is it about asking the questions that shape tomorrow?
•8 min read
How To Combine Quantum Computing And Machine Learning
Quantum Machine Learning promises breakthroughs by merging two very different worlds: probabilistic pattern recognition of machine learning and the unitary dynamics of quantum computing. Can we turn short-lived fireworks into rockets—systems powerful and stable enough to achieve real quantum advantage?
•7 min read
Combining Quantum Computing and Machine Learning
For quantum machine learning to be meaningful, it must truly combine the strengths of quantum computing with those of machine learning. Any proposed approach must demonstrate representation power or computational efficiency that goes beyond what classical methods already achieve. Without this, quantum machine learning runs the risk of becoming a costly imitation of classical techniques. So, discover the various ways in which such a fusion can succeed and why they are important for the future of this field.
Quantum Probabilistic Modeling
At the heart of modern science and machine learning lies a seemingly simple process: sampling from a probability distribution. Whether you're running a Bayesian model, training a generative network, or simulating molecules, the quality of your samples determines the quality of your predictions.
•6 min read
Variational Quantum Machine Learning Algorithms
Variational quantum machine learning algorithms are a hybrid approach in which a parameterized quantum circuit is tuned by a classical optimizer to approximate solutions to complex problems.
•8 min read
Spectral Quantum Algorithms for Eigenvalue Estimation and Transformation
Eigenvalues determine everything from how a quantum system evolves to how a kernel method in quantum machine learning defines similarity between data points. Without a way to compute or transform them efficiently, the promise of quantum speedups in machine learning collapses into impractical theory.
•5 min read
Fundamentals
The tunable gate toward practical Quantum Machine Learning
•5 min read
Parameterized quantum circuits let us embed classical data into quantum states, tune them with adjustable gates, and read out predictions through measurement. The real question is whether these simple building blocks can be scaled into architectures that deliver genuine quantum advantage.
Quantum Circuits
•5 min read
Quantum circuits are more than just abstract math. They bridge theory and hardware by turning dense unitary matrices into structured recipes that real devices can execute. Therefore, they provide an essential layer of abstraction that makes quantum computation both understandable and practical.