Andreas Dewes

Five years ago I spoke about my work in quantum computing, building and running a tiny two qubit processor. A few weeks ago, Google announced a potentially groundbreaking result achieved with a 53 qubit quantum processor. I will therefore review the state of experimental quantum computing and discuss the progress we made in the last 5 years. I will explain quantum supremacy, surface code architecture and superconducting quantum processors and show which challenges we still have to overcome to build large scale quantum computers.

We will first dive into the basics of quantum computing and learn about quantum gates, fidelities, error correction and qubit architecture. We will then go through Google’s experiment and try to understand what they actually did and why it matters. We will then see what else we need to build a useful quantum computer, and discuss when that might happen.

When thinking about surveillance, everyone worries about government agencies like the NSA and big corporations like Google and Facebook. But actually there are hundreds of companies that have also discovered data collection as a revenue source. We decided to do an experiment: Using simple social engineering techniques, we tried to get the most personal you may have in your procession.

When thinking about surveillance, everyone worries about government agencies like the NSA and big corporations like Google and Facebook. But actually there are hundreds of companies that have also discovered data collection as a revenue source. Companies which are quite big, with thousands of employees but names you maybe never heard of. They all try to get their hands on your personal data, often with illegal methods. Most of them keep their data to themselves, some exchange it, but a few sell it to anyone who's willing to pay. We decided to do an experiment: Using simple social engineering techniques, we tried to get the most personal you may have in your procession. Your “click-stream data”, every URL you have been visiting while browsing the web. After a couple of weeks and some phone calls we were able to acquire the personal data of millions of German Internet users - from banking, over communication with insurance companies to porn. Including several public figures from politics, media and society. In the talk, we'll explain how we got our hands on this data, what can be found inside and what this could mean for your own privacy and safety now and in the future. * Introduction & background * Who collects data and for which purposes * How we got our hands on a large data sample * What's in it? Detailed analysis of the data set * How does it work? Analysis of the collection methods * Outlook: Can we still save our privacy?

Other people: @sveckert

Algorithms and „big data“ penetrate many aspects of our lives today. In the future, data collection and analysis will be even more ubiquitous and permeate our lives from morning to night.

Many people (well, mostly business people) welcome this new era of data analysis and the associated vision of an „intelligent planet“. Not so many people seem to be concerned about the other side of the coin though, which is an ever-growing influence of algorithms on our personal life and the accompanying shift of decision power from humans to machines. In as little as 10 years, algorithms might decide if you get a new job – or if you get fired from your current one –, how much you will pay for your health insurance, whether you will be allowed to travel to a given country and who you will marry. So it’s time to say hi to your new boss: the algorithm. Often people talk either about the consequences of a data-driven society, or about the technological aspects of it, but rarely about the two together. With my talk I want to change that by discussing concrete technologies and algorithms that are used in data analysis today, together with their societal and political implications. I will show how algorithms can be trained to be racist, misogynic and plenty of other things, and that this actually happens in practice if no care is taken to avoid it. Finally, I will discuss various approaches to solve this dilemma, both technological and political. Outline: * Introduction to „big data“ and data analysis, * Parts of our lives that are already under algorithmic control, * Parts of our lives that soon will be under algorithmic control, * Example use case of algorithms in data science, * How machine learning can discriminate against certain groups of people, * Example algorithm: Classifying people in good and bad customers, * How the bias comes about: Algorithm-based discrimination, * How we can fix these problems. * Outlook.

I will explain why quantum computing is interesting, how it works and what you actually need to build a working quantum computer. I will use the superconducting two-qubit quantum processor I built during my PhD as an example to explain its basic building blocks. I will show how we used this processor to achieve so-called quantum speed-up for a search algorithm that we ran on it. Finally, I will give a short overview of the current state of superconducting quantum computing and Google's recently announced effort to build a working quantum computer in cooperation with one of the leading research groups in this field.

Google recently announced that it will partner up with John Martinis -one of the leading researchers on superconducting quantum computing- to build a working quantum processor. This announcement has sparked a lot of renewed interest in a topic that was mainly of academic interest before. So, if Google thinks it's worth the hassle to build quantum computers then there surely must be something about them after all? With this talk, I want to provide a better understanding of why quantum computing is interesting and how we might actually build a working quantum computer one day. As an example, I will discuss the two-qubit chip that I built during my PhD thesis as a realization of a basic, functional two-qubit quantum processor. I will explain the building blocks of this processor and show how we can manipulate the qubits, read out their state with high fidelity and couple them to each other in order to realize two-qubit gate operations. I will then show how we used this processor to demonstrate the concept of "quantum speed-up" by implementing and running the so-called Grover quantum search algorithm on it. Finally, I will give a brief overview of the current state of quantum computing and explain the (likely) approach followed by Google and John Martinis to realize a working, large-scale quantum processor, as well as some problems they will have to overcome on their way.