Basics of quantum error correction

Do usable universal quantum computers exist today? If you follow the recent press releases, you might believe that the answer is "yes", with IBM announcing a 50 qubit quantum computer and Google promoting its Bristlecone architecture with up to 72 qubits. Unfortunately, the world is more complicated than this - time to demystify the hype … Continue reading Basics of quantum error correction

Into the quantum lab – first steps with IBMs Q experience

Even though physical implementations of quantum computers make considerable progress, it is not likely that you will have one of them under your desk in the next couple of years. Fortunately, some firms like IBM and Rigetti have decided to make some of their quantum devices available only so that you can play with them. … Continue reading Into the quantum lab – first steps with IBMs Q experience

Shor’s quantum factoring algorithm

Until the nineties of the last century, quantum computing seemed to be an interesting theoretical possibility, but it was far from clear whether it could be useful to tackle computationally hard problems with high relevance for actual complications. This changed dramatically in 1994, when the mathematician P. Shor announced a quantum algorithm that could efficiently … Continue reading Shor’s quantum factoring algorithm

Grover’s algorithm – unstructured search with a quantum computer

In the last post, we have looked at the Deutsch-Jozsa algorithm that is considered to be the first example of a quantum algorithm that is structurally more efficient than any classical algorithm can probably be. However, the problem solved by the algorithm is rather special. This does, of course, raise the question whether a similar … Continue reading Grover’s algorithm – unstructured search with a quantum computer

The EM algorithm and Gaussian mixture models – part II

In this post, I will discuss the general form of the EM algorithm to obtain a maximum likelihood estimator for a model with latent variables. First, let us describe our model. We suppose that we are given some joint distribution of a random variable X (the observed variables) and and random variable Z (the latent … Continue reading The EM algorithm and Gaussian mixture models – part II

The EM algorithm and Gaussian mixture models – part I

In the last few posts on machine learning, we have looked in detail at restricted Boltzmann machines. RBMs are a prime example for unsupervised learning - they learn a given distribution and are able to extract features from a data set, without the need to label the data upfront. However, there are of course many … Continue reading The EM algorithm and Gaussian mixture models – part I