A few weeks ago I found myself boarding a series of planes that would take me from the Netherlands to pretty much the furthest point reachable from this starting point that still includes dry land: Sydney, Australia. I wasn’t going there entirely of my own accord. Rather I had been invited to speak at a quantum information theory workshop called Coogee. For those knowledgeable of the Sydney region, it is indeed named after a beach and yes the conference takes place less than a stone’s throw away from said beach.
Even if you’re in a niche research field, it seems almost impossible to keep up with all the scientific literature that has been coming out in the past couple of years. There are estimations that the global scientific output doubles every 9 years, so it’s not going to get any easier. If you want people to read about your results, you’ll have to stand out. An important part of standing out is having a good abstract.
In the fall of 2015 QuTech and Intel Corporation joined forces in an active collaboration working on the realisation of a quantum computer. The collaboration comprises comprises Edoardo Charbon’s control electronics, Koen Bertels’ architecture work, Leo DiCarlo’s superconducting qubits and Lieven Vandersypen’s silicon spin qubits. After having worked on the Delft side of the spin qubit part of that collaboration for almost two years, I spent three months this summer in Hillsboro, Oregon to be on the other side of the phone in our weekly Skype meetings. In this blogpost, I will share some of my experiences with you.
Research in academic is a tough, gruelling but ultimately rewarding job (otherwise we wouldn’t work so hard at it!). Usually if you ask a scientist about what it is like to work in research, you will be subjected to a coffee fuelled rant about tiresome data analysis, demanding students and endless paper preparation. Unless you catch us in an unusually good mood we won’t take the time to talk about the many things about our job that we genuinely enjoy.
Last Thursday was the yearly Applied Physics sports day. As is tradition, QuTech participated in big numbers. We competed with three teams, and it was clear already from the start that the goal of the day was not just to participate, it was also to win!
The winners mentality of the QuTech teams made me wonder: why were we more competitive than the average student team? Is there an analogy between sports and research that underpins this?
So this post will be a bit more, let’s say, philosophical. I’d like to share some of my thoughts on a particular subject which has always struck me when I was studying physics and also now while I’m doing it in what might be called a professional fashion. That subject is mathematics. More precisely it is mathematics as applied to physics. Now I won’t pretend to be anything close to a real mathematician, but when you need a math-person and there are no mathematicians around you can probably do worse than a theoretical physicist. In physics, and also in computer science, we use math; a lot of it. In fact I would say that, and I think most physicists would agree with me, that mathematics is the language the universe is written in. Or at least the only language capable of describing it in an efficient manner. People often marvel at the ability of mathematics to capture physical phenomena in an extremely accurate and efficient manner, often waxing philosophically about the inherent simplicity of the universe. Here I’d like to give some of my, fragmented and incomplete, thoughts on the matter. While I certainly think that the fact that nature is describable at all is a fact worth pondering over long and hard I think the prevalence of math in physics and its remarkable effectiveness is at least partly due to decidedly more down to earth cultural forces present throughout the history of mathematics.
Hi! My name is Sophie Hermans and I am a Master student in the group of Ronald Hanson. I have started my MSc project about five months ago in the “cavity team”. Today I will take you along and show you what I do on a regular day.
By Jonas Helsen, Christian Dickel, Adriaan Rol, James Kroll and Suzanne Van Dam
The March Meeting of the American Physical Society, held every year in March (hence the name) is probably the largest meeting of physicists in the world. Held in a different city in the US every year it is a five day long whirlwind of talks, discussions, meetings, catching up with old friends and making new ones from all over the world. Since a sizeable subsection of the March meeting deals with quantum information processing (as of this year we are officially a Division!) a large group of Qutech scientists made the trek to New Orleans, both to speak about our latest developments and to learn about science going on all around the world. For this occasion we asked a few people to jot down their impressions of this weeklong carnival of physics and have bundled them in this blogpost. We will also add some pictures which hopefully convey the general scale and feel of the March meeting.
When I’m at a party people often ask me what I do.
There is a lot of things I can talk about: why is a quantum computer interesting or useful , or:what do I actuallydo during my day. But quite often people end up asking a confused question about this curious story of an undead cat. In this blog post I will try to shed some light on this case as well as delve into the question of why we use these kind of stories.
One of the things that is often repeated about quantum computing is the idea that a quantum computer is somehow more powerful than regular computers because, when considering a problem it can “try all possible solutions at once”. Let’s get this out of the way first and say that this is not exactly the case. While we would very much love a computer that tries all solutions at once (this would be extremely useful) quantum computers sadly aren’t quite this powerful. Of course, as with all good clichés it does contain a grain of truth. In this blog post I will try to explain in a (sort of) simple way what makes quantum computers more powerful than classical computers.