Scientific presentations and the art of storytelling

How many talks have you attended in the last year? And how many of those did you enjoy? Even when the topic itself is interesting, one often leaves disappointed. Some speakers spend too much time on technical details and do not have time to discuss the main results; others are not well prepared and keep jumping backwards to remind us of something they mentioned ten minutes ago. It’s almost as if they didn’t consider sharing their work important enough to warrant careful preparation.
No matter the situation, I always have to prepare; it’s not entirely voluntary. Because of my stutter, I have to make sure that I know what I’ll say—less stress means that my speech will flow better. These preparations cost me a lot of time that many would spend working on something else. But a thorough preparation often beats my handicap, and my presentation can be above average at a conference.
In the course of my PhD, I developed an effective workflow for preparing academic talks. It takes time (a lot of time, sometimes). It’s not the only method to prepare. And possibly not even the best one. But it works for me. And the main idea is simple enough that anyone else can adapt my approach in no time.
It all boils down to a single piece of advice: You are telling a story, and your job is to make sure it’s a good one. That’s all. If you know what story you want to tell and how you want to tell it, you’re prepared.

The what

Figuring out your story is the most critical part. There are many ways how you can frame your research in the context of existing work. Maybe you started studying social behaviour of dolphins because you love the animals. Or you think that we can learn from them and improve our own relationships. Or you want to understand the variations between dolphin species.
You will also have to choose the right story for your audience. A group of academics might not care for your love of dolphins but it can be a good way to connect with a class of middleschoolers. Different academic audiences will want to hear different talks as well.
Finally, your story will depend on the time allocated for your talk. If you have to give a short talk, you can’t delve into all the fascinating details of your work; you have to pick the most interesting and important results.

The how

Once you figured out the story you want to tell, you can think about how to tell it. This is, of course, entirely up to you, but there’s one rule you should follow if you’re preparing a powerpoint/keynote presentation: Each slide should be one step forward. Not more, not less. If you jump forward too fast, you’ll lose your audience; if you’re too slow, you’ll bore them.
To ensure that I stick to this rule, I design each slide around a short sentence. This way, it is easy for the audience to understand what the main message is. The rest of the slide supports this statement—by illustrations, equations, or graphs.
Once my presentation is ready, I make a simple check that I created a good story: I copy all sentences into an empty document and make sure that they build on each other. Each sentence is a logical step in a sequence leading to the grand finale. If this is not the case, I can see where the problem is and rewrite the spot accordingly. In the end, each slide is a concise, independent idea and together they form my story arc. Each slide is so simple that I could tweet it if I want.
The rest is practice, practice, practice. I make sure I know how to transition from one slide to the next without awkward pauses. This also gives me an idea about how much time I need. I identify places where I can speed up if need be and create a few checkpoints throughout the presentation—I write down how much time I need to get to particular slides. When presenting, I can see whether I’m following my schedule and adjust my tempo as I present.


I will show you my approach on an actual presentation I will be giving in a few days. This week, the annual spring meeting of the German Physical Society is taking place in Mainz. On the last day of the conference, Friday, March 10 at 15:15 CET (9:15 am EST) I am giving a presentation about my current research.
Since not everyone can attend my talk, I will tweet it live. For me, it will be an interesting experiment. For everyone else, it’s an opportunity to follow my presentation; you will also see how I can fit each slide into a tweet. Later (probably next week) I will write a second blog post in this miniseries looking into this talk in more detail and comparing it with the live tweeted version.


Building a new habit is hard. I saw that with blogging twice already. I started and stopped and started and stopped. Now, I am starting a third time and hoping that my blogging routine will stick.

Insanity is doing the same thing over and over and expecting different results. Does that mean I’m insane? Not at all. I am not doing the same thing over and over, and not only because I learned from mistakes past. I’m not starting the same blog this time.

My blogging will take a new turn. Though writing about physics is fine (and I plan to continue that), I want to start writing about other matters as well. Because academic life is not just the research. And even if it were, my life isn’t just academia. And various things affect my scholarly experience.

All these issues are important. Some people don’t fully understand what the academic life is like; and I want to show the range of scholarly activities to non-academics. Some things we do wrong in academia; these need to be identified, discussed, and addressed. Some aspects of the everyday non-academic life affect our academic experience, or vice versa; and we need to talk about those issues. Research has its emotional side that we don’t talk about often enough; it’s time we changed that.

I don’t know how my blog will change. I might not write as regularly as I used to (which is still an improvement from not writing at all). I might write more, or less. I might experiment with form or content. I might lose readers or gain new.

I don’t know how my blog will change. And I can’t wait to find out.

Benefits and challenges of tweeting a conference

Academic conferences are usually exhausting. You spend the whole day (or, more often, several days) closed in a lecture room, often without direct sunlight or fresh air, and try to absorb as much information as you can from (sometimes poorly prepared) talks of your fellow researchers. At some events, speakers change as often as every 15 minutes which makes it even harder to keep track of their talks. At larger conferences, stress from running between parallel sessions to catch all interesting talks adds to the mix. Nobody in their right mind would voluntarily add one more task on top of that — tweeting what others are talking about, right?

It might seem that live tweeting at a conference only adds more stress and work to one’s already packed program. Yet, it helps me pay more attention to what the speaker is saying and to identify the main message of the talk. As a result, I can learn better and enjoy the conference more.

This shouldn’t come as a surprise to anyone with a solid tweeting experience. After all, Twitter forces you to express your thoughts as concisely as possible. For tweeting from a conference, that does not mean you should dumb down what you hear; instead, you have to pay close attention to what the key information is. You have to strip the information off all unimportant details (which might be crucial for the science but not necessarily for your audience). And that is also what you need in order to make good notes for yourself and remember what you heard.

Secondly, you have to adapt to your audience’s background. Most of your followers might not know why a particular research project is important and how it fits within the research that has already been done. One is thus forced to think about these questions as well. You might think that you already know the answers but you might easily find links between seemingly unrelated problems. And considering a known issue from a new angle (which you might do to help your audience understand its implications) can bring new and interesting insights.

Finally, it also helps me to concentrate better if I know that I am the only person who can share the conference with my followers. If I zone out for a minute. I will not know what the speaker said. As a result, I will also not be able to pass this knowledge on. Through accountability, I thus tend to pay more attention than I would if I decided not to tweet.

While there are certain advantages to tweeting a conference, the practice is not so simple. Tweeting the talks you are attending is great but you have to remember that it also takes your attention away from the talk. The more you tweet, the less attention you will pay to the speaker. And if you start reading the tweets of others, you can miss the talk completely.

My solution to this problem is trying to find balance. Keeping the number of tweets within a reasonable limit, I do not overwhelm my followers and have time to focus on my learning experience as well. In my tweets, I explain what problem the speaker is trying to solve, why that is important, and how it can be done. If there is some interesting information on top of that, I’ll share it as well. If the talk is short (20 minutes or less), I might even tweet less. And not getting distracted by Twitter? That is a question of self-control, and nothing more.

Even if you manage to keep things brief, not get distracted by Twitter, and not tweet too much, you can spend a lot of time crafting and perfecting your tweets. At first, your tweet is five characters too long, then a piece of (maybe crucial) information is missing, now the tweet sounds a bit clumsy. Before you know it, the speaker has moved on and you missed the one slide that was necessary to understand the rest of the talk.

You have to keep in mind that live tweeting is different from your usual tweeting. Most of the time, you have plenty of time to create the perfect tweet, but not at a conference. Here, you have to get the tweet out as fast as you can (but not at the price of grammatical errors or incomprehensibility, naturally). Your followers will understand that your tweets can’t be as polished as they usually are.

No matter what you do, you should enjoy your conference, learn new things, and talk to interesting people. If you find that Twitter doesn’t help you achieve that, let it be and tweet less; or not at all. What works for me doesn’t necessarily have to work for you.

What I learned (and didn’t) from a year of blogging

It has been a year since I started blogging. It did not go quite as well as I hoped it would but also not as badly as I was afraid it might. I started full of determination with a clear plan, wrote posts… and then stopped. It took me seven months to start again and since then, I have been writing regularly.

This is a good time to stop, take a deep breath, and look back. Analyse (I am a theoretical physicist, remember?) what I did well and what could have been better. Who knows, other bloggers (whether just starting or more experienced) might find this useful.

  1. Regularity
    It is easier to keep momentum than gain it; it is easier to lose momentum than keep it. It takes no effort to decide to write the next post later. When I momentarily have too much work to do, it seems reasonable to skip writing the next blog post. But if that happens, it becomes more difficult to write it. If I make it my priority to publish a post every week, I will. It is not always easy but it can be done.
  2. Planning and serendipity
    While it is a good idea to have a plan, one should never be too strict about sticking to it. Reacting to current affairs (if they are related to the topic of the blog) is a good way to reach new audiences. And being open to other impulses can inspire upcoming posts.
  3. Learning
    Keeping a blog about science is a constant learning process. I do not write about things that are completely new and unknown to me, of course, but I do need to make sure that everything is factually correct. What’s more, I need to make sure that the topic is understandable to non-physicists. For that, I have to consider several ways to look at a particular problem and pick the one (the ones) that is (or are) the easiest to comprehend. And I can always learn something new from that!
  4. Time
    It takes a lot of time to write a blog post. Writing a thousand words can be done fairly quickly; finding those words is a different matter entirely. A completely new blog post starts with a topic and an outline. I can think about those while doing other things (such as commuting to and from work) but they still need time. Then comes the draft, editing and proofreading. After that, I might need to prepare pictures and only then is a new post ready to be published. Without proper planning, it is impossible to get the next post out on time.
  5. Failure
    Sometimes, blog posts don’t turn out the way I was hoping. Maybe I didn’t have enough time for writing or I chose a difficult topic to write about. That happens. I can’t expect every post (or any post) to be perfect; some are better, some are worse. If I don’t want to write bad blog posts, the best strategy is to not write at all — and that’s not an option. As long as I can figure out what I did wrong and learn from it, everything is good.

Those are things I learned so far. But there are also things I am still struggling with and need to improve:

  1. Organisation
    It happens to me sometimes that I outline a blog post in my head and, before I write the post, I forget how I wanted to structure the argument. Then, I have to try and remember what I wanted to write or, in the worst case, start again from scratch. One way or the other, it costs me time. I need to learn to write these ideas down before they can flee. Or even better, make outlining part of the process of writing a draft, experiment with the outline and choose one that works the best.
  2. Finding time to write
    As I said above, writing a blog post takes time which is sometimes hard to find. There is a way out of this problem (at least partially):  Using any narrow time windows during the day to write. I just have to remember the next time I have few minutes free to take my notebook out (yes, I draft my blog posts by hand) and start writing.
  3. Writing ahead
    So far, I start writing the next post after I published the previous one. Does that sound reasonable? It isn’t, really. It means that I have exactly one week to write the next post. If I had several posts ready, I could occasionally take a little longer to write the next post — or even take a break for a week. Having a buffer is something I can start right away; all I need to do is be a little more strict about writing for the next few weeks and I will surely manage more than a post per week.

These are my experiences with blogging so far. If you also blog, what do you (or did you) struggle with? What helped you solve your problems?

The end is nigh. Well, not really

It is beginning. Earlier this week, I downloaded Scrivener and yesterday, I started outlining my dissertation. I still have a lot of time to finish — I am currently planning to submit early next year and defend in spring, though that might change — but I think it is a good idea to start now. Why, you ask?

Screenshot 2016-02-10 15.42.23
The dreaded blank page.

First of all, this is the longest, most complex piece of writing I ever set out to write. Sure, I had to write a bachelor and a master thesis but those two combined are probably shorter than my dissertation will be. It will therefore take more time to write. And it is better to start early and have plenty of time for edits than be chased by deadlines.

But more importantly — and perhaps paradoxically — I am starting to write now because I am not done researching yet. Just recently, I finished a project and am about to start a new one. What will I do? I DON’T KNOW. And that is exactly my point. This is the right moment for me to stop working on my own projects and publications and look systematically and in detail at the work of others. Then, I can better judge which open questions I can tackle. And it is only natural to write what I learn and turn it into the introductory parts of my dissertation.

Connected to both these reasons to start so early is a third one: Because not all my work is done and because the writing will be so complex, I need not only to write what I intend to but, first of all, figure out what it is that I want to write. And for that, I need to keep track of all thoughts and ideas that come to me and organise all the material I plan to use. This is a task that goes beyond what standard LaTeX editors (I used to date) can do. Therefore, I need to find a platform that can take care of that and become sufficiently familiar with it.

So far, Scrivener seems to be a good choice to do that. Not only can I use it to work on my draft but it also helps me to keep any notes and further materials at the same place as the dissertation draft. I have to look into it in more detail to find out how to best use all these features and that will need quite some time. But if all goes as smoothly as it seems it will, the writing itself will then be relatively easy.

Since I started so recently, I did not manage more than briefly outline the first half of my dissertation. And yet, it already helped me realise how much I still do not know about the basics that will form the foundations of my dissertation. If that happened with a deadline looming above me, it would mean a serious complication to my plans. Now, I can simply go and read on the stuff I still need to learn.

Quite naturally, this approach also has its disadvantages. How am I supposed to write the introductory chapters presenting the knowledge I am building upon if I still do not know what I will do during the last year of my PhD? My choice of the next project is simply constrained by that. This situation is not that much different from what I would experience anyway — my next project should, in some way, be related to my previous one. I might then need to rearrange the introductory material a little but it should not need any complicated redrafting.

In the end, this approach will probably save me a lot of binge writing. By the time my last research project is done, I will have, ideally, written most of my dissertation already; it will only remain to write about my last project and make sure the whole text is coherent. Finishing my dissertation will then be just a matter of a few weeks and not several months.

And now, if you’ll excuse me, I have some writing to do…

Good scientists publish, shitty ones blog. Or do they?

As scientists, we are in a very privileged position compared to the rest of population. Not only do we really enjoy what we do but we also get to choose what to work on ourselves. Sure, there is the dark world of academic bureaucracy and the perpetual fight for grant money but I still think that we are an extremely lucky bunch. I am not aware of any other profession where the situation is similar.

Now and then, we forget how truly exceptional our situation is and take this privilege too much for granted. Then, we try to forget about the outside world and, hidden in our ivory towers, fight against every change in the academic environment. Some times, we feel offended by accusations of sexism. Other times, we find it outrageous that we should move science to social media and to the public.

I am sure there are bad scientists who vent their frustrations by criticising the works of others on the internet. But there is also a large group of researchers who do not forget about the world outside the academic milieu and want to share the amazing science they do with others — it can be fellow researchers who do not work in exactly the same field of study, family and friends who never stop asking about one’s work, or anyone willing to listen. We then start our blogs where we talk about our own research, the work done by our fellow scientists, our approaches to tackling problems we face at work, and the joy our daily lives bring.

Maybe we will not publish as many papers as those who do not see beyond their ivory towers because we are not so focused on our research output. But there are many ways in which scientists can contribute to the community; publishing own results, reviewing works done by others, mentoring and teaching younger generations, or sharing our passion for science with the rest of the world are just a few. It is, of course, impossible to judge who is the best scientist but, as long as we all contribute in a positive way, that does not (or should not) play a role. At the end of the day, science is not a solitary endeavour but a benefit to the society.

We also must not forget that it is the public who lets us work on problems we find fascinating. The least we can do in return is tell them what we did and how it will benefit them; otherwise, we might wake up one day and find them not willing to finance our work any more. Sure, it is not always immediately clear why our results are so important or how they can be applied to benefit mankind but hiding our work from the lay public is not a solution. Even such abstract fields as theoretical mathematics can be made accessible to those willing to learn something new.

Long-term commitment to disseminate research to the public is not an easy one. But without it, it is difficult to get society to trust science and we cannot expect the public to listen to us when presenting important findings. For instance, if we want to convince public that climate change is a real threat to our civilisation, we have to explain how we found that out and what the findings mean for our near future. If we ask the public to trust us blindly, all we can expect is skepticism and denial.

I am not implying that every scientist has to blog. As I said above, there are many ways in which researchers can contribute to the community, and blogging is just one of them. If someone finds it difficult or thinks they can contribute better in other ways, that is perfectly fine. But damning every science blogger and claiming they are all failures is a very short-sighted approach.

Through the looking glass

Studying physics ultimately changes the way one sees the world. This is probably true for any subject but with physics, this change goes deeper than with biology or history. One starts to see some very basic things very differently. At least that is what I think.

Take the simple act of measurement, for example. You want to know what the weather is like? You check the thermometer. Want to know whether you lost weight? You step on the scale. In any case, the act of measurement is just a way of obtaining some information that already exists. There was a particular temperature outside before you looked and it does not depend on whether you look or not.

Then you start learning quantum physics and your understanding of measurements dramatically changes. You want to measure a position of a particle? Sure, you can do that. But unlike in normal world, it does not make much sense to talk about the position before you measure. The particle was not here or there before you measured, there was only certain probability for it to be there.

Classical particles (left) are simply little balls but quantum particles (right) are just a cloud of probability.
Classical particles (left) are simply little balls but quantum particles (right) are just a cloud of probability.

At first, this might seem similar to your everyday experience — when you are looking for lost keys, you do not know where they are so there is only a certain probability to find them at a particular place. But there is an important difference; although you are unaware of the exact position of your keys, they are lying at a particular place. A quantum particle, however, is literally at several places at once. Only by measuring its position you localise it at a particular place. It is as if you are looking at the thermometer changed the temperature outside.

When the position of a quantum particle is measured, its cloud of probability is squashed, representing the gain in information about its position we get.
When the position of a quantum particle is measured, its cloud of probability is squashed, representing the gain in information about its position we get.

Since the particle was not at a particular position before the measurement but it is at a specific position afterwards, the measurement changes the behaviour of the particle. If you now let the particle move freely (i.e., without observing it), it will behave differently than if you did not look at it in the first place. To use the analogy with measuring temperature, it is as if the weather during a day depended on whether you looked at the thermometer in the morning.

Since the measurement affects the state of the particle, its evolution is depends on whether a measurement was performed or not.
Since the measurement affects the state of the particle, its evolution is depends on whether a measurement was performed or not.

If that is still not enough for you, you can go deeper and ask how the measurement process works. First of all, you will find that people know surprisingly little about that. They will tell you that the system you are measuring (such as the particle whose position you want to measure) interacts with a second, meter system in such a way that some variable of the meter contains information about the measured system and can give a strong, classical measurement signal. But what determines whether a system is classical and can be used to measure other systems or whether it is quantum and can be measured by other systems? Not a clue.1

Even with this little knowledge about measurements, people can describe what is going  on surprisingly well. Because the system and the meter have to interact for some time, a lot can happen during the measurement. If you try to measure the position of a particle, the particle will continue to move while you are measuring. Measure too quickly and you will not know where exactly the particle is because you do not collect a strong enough signal. Measure too long and the particle will move too much during your measurement.

In the end, you can never measure as precisely as you would like. There will always be a small uncertainty in the position of your particle. And this gets even weirder when you try to look at the position later again. Quite surprisingly, the better you know the position at an early time,the more blurred the measurement will be at a later time. This is the result of Heisenberg uncertainty relation between position and momentum but that is a story for another time.2

Another thing you can do is measure really slowly so you need a long interaction time between your system and the meter. At any given time, you do not have a complete information about the state of your system, i.e., you never know exactly where your particle is, all you can have is a guess. The measurement then becomes an inherent part of the evolution of your system and can be used to steer it. There is now certain randomness in the evolution (remember, all we can talk about before the measurement are only probabilities of each outcome and the measurement is thus random at heart) but that does not matter that much since you know what the random measurement outcome is.

You can imagine a simple feedback loop as a sequence of a measurement, a feedback force, and a free evolution. This way, you can, e.g., stabilise the position of a particle.
You can imagine a simple feedback loop as a sequence of a measurement, a feedback force, and a free evolution. The measurement outcome is random but the feedback ensures that the particle stays frozen at a fixed position.

If you do not like this randomness, you can use the information you get from the measurement to control your system. You can, for instance, use the result of the position measurement to keep a particle pinned to a particular position. Every time it tries to move a bit (and everything moves a lot in the quantum world), your measurement will tell you so and you can push it back. We thus came to the notion of measurement feedback I already talked about before.

Realisations that such simple things as measurements have such rich and complex internal structure are one of the things I love about physics. Where most people see a simple (and a little boring) way to get some information, I see an incredibly complex process people still don’t understand after studying it for decades. More than that, measurements are for me a tool that we can use to control and manipulate quantum systems. And there is nothing boring about that!

1 I am, of course, simplifying things a bit here. There is a lot that we know about measurements (and a lot we don’t!) but it all involves a lot of counterintuitive things and complicated maths. There is no room for the details in a blog.

2 Here, I am mixing the notion of single-shot measurements (i.e., measurements you only do once) and repeated measurements (which can be used to obtain statistics). But since even a single-shot measurements takes up a finite time, it is, in a way, a statistical matter. I will try to get to this problem in a later post.

A new start

I am at the point in my PhD where I am truly becoming a researcher and am no longer just a student. How can I tell?

I just finished a project I worked on basically since I started my studies more than two years ago. It was my supervisor’s idea to study this particular problem, even though some of my ideas also helped shape the result, especially in the last half-year.

Now, I have to find something new to work on. Yes, I have to. It is no longer up to my supervisor to do that for me. I will now dig through the literature, see what has been done, and try to find a blank spot in knowledge I could fill.

This is not a task I could have easily done when I started my PhD. I could have gone through the literature back then, of course, but it would have been much more difficult for me to identify a problem that is worth solving and that can be solved by a graduate student. But after two years of cracking problems, reading research papers, and generally being immersed in the academic world, my view is very different from when I started. I know better what I can achieve, what problems are worth solving, and what means I should use to tackle them.

This discovery is, of course, rather encouraging. It means I can see the progress I have made since starting my PhD. Not only expressed in the number of publications that appeared on my CV but also in the less tangible ways — I am more independent than before, I can orient myself in the body of research, I can understand what others are working on, why they are interested in this particular problem, and how they go on about solving it. Still, every new situation is scary — at least up to some extent — and this one is no exception.

Doing a PhD is a lot like climbing a mountain. As you start, all you see is the large pile of rock you have to climb and nothing else. The path is long and tiring and never leads to the top in a straight line. And once you are on the top and a view opens, you see everything around you. Suddenly, you are aware that there are many more mountains around you could climb. And some — maybe even most — are higher than the one you climbed to. So now you can decide which mountain to climb next. But you must choose carefully. You have to find a mountain that is not too hard to climb which can be difficult to judge from a single look from afar.


The situation is the same with my next research project. I can see what I have done — that is the mountain I just climbed –, what others have achieved, and what has not been done yet, i.e., the mountains I can see around me. Now, I have to find a problem that has not been addressed before but is interesting, important, and relatively easy. How can I judge that? Especially since I never did that before?

Naturally, I do not have answers to these questions. But I also know that I do not necessarily need them. For start, my supervisor would not let me go looking for my next project if he did not believe I can find one, and that is an encouraging thought. I also do not need to go and find the next problem all by myself. As I progress through the works of others, I can discuss with my supervisor and colleagues what I found and what I think about it. My goal can thus develop over time and others can help me make sure that I stay on the right track. Finally, I know what my first little steps in this direction will be. Consequently, there is no vast sea of unknown research waiting to be explored but several smaller, manageable pieces.

The joys of theoretical physics

Have you always thought mathematics is dull and complicated? You are certainly not alone. But there is a lot of beauty hidden in it and in the way it describes our world.

Theoretical physics is all about using maths to describe nature. As the universe we live in is vast and filled with myriads of phenomena — starting with the universe itself expanding due to dark energy, galaxies held together thanks to dark matter, new stars being born and dying, planets and asteroids orbiting these start and colliding with one another; through processes happening on and inside those planets including the miracle of life; down to the perplexing world of molecules, atoms, and subatomic particles — so the mathematical language in which these processes are described uses a lot of tools, often very complex. And yet, there is a surprising level of similarity between different systems.

For a theoretical physicist, there is no difference between an oscillating pendulum, a vibrating string, and a propagating beam of light. Heat transfer and particle diffusion are equivalent because they obey the same mathematical law. According to quantum field theorists, every type of particle (be it a proton, an electron, or a photon) can be seen as a harmonic oscillator and there is almost no qualitative difference between them.

Some physicists are trying to take this idea one step further and find a single physical theory encompassing all physics as we know it. Thus, the Grand Unified Theory was developed which unifies three of the four fundamental interactions in nature — electromagnetic, weak, and strong. Including the fourth one — gravitational — is a feat that has not yet been achieved. Some even doubt that such a Theory of Everything will ever be formulated.

Many theoretical physicists (like me, for example) do not pursue such noble quests but focus on smaller, albeit not less meaningful tasks: How does X work? Can it be used for something worthwhile? What is the best way to do it? These are not important questions on the global scale (compared to questions such as ‘How did the universe come to be?’) but the more important for technological progress. Such development is ultimately the domain of experimental physicists and engineers but finding ways of using new bits of physics in ways humanity can benefit from is a part of theoretical physicist’s work.

Such a process can be illustrated on a problem that is occupying many a scientific mind: building the quantum computer.  It will, of course, be experimental physicists who will build the first functioning prototype (assuming we ever develop one) but theoretical physicists examine how such a device should be built. Should we use atoms as the information carriers? Photons? Something more exotic? Those are some of the questions quantum information theorists are trying to answer.

There is a lot of mathematical beauty in solving such tasks, too. After all, theoretical physicist sees a quantum computer as a large register of quantum bits on which an arbitrary operation can be performed, which can be stored for a long time in a quantum memory, and which can be sent to another quantum computer via a quantum internet channel. The need for investigating various platforms comes from the experimental realisation — each potential platform has its own unique advantages and disadvantages that have to be carefully weighed when finding the optimal architecture for a successful quantum computer.

All that said, there are many more surprises hidden in quantum information theory. One often finds other, unexpected connections between the weirdest parts of the theory. And finding them is always one of the biggest delights working with theoretical physics can bring.

All this mathematical beauty can, actually, also be useful. If two different systems behave in a similar way, we can use one to simulate dynamics of the other. This is more and more often used in quantum physics where complicated systems (especially those that cannot be observed directly in a laboratory) can be simulated using much simpler systems. This way, we can relatively easily learn a lot about the elaborate system which cannot be simulated on classical computers. (Due to their nature, it is possible to simulate only small quantum systems on classical computers.)

The field of quantum simulations (i.e., using simple quantum systems to simulate evolution of more complicated systems) is still in its infancy. But it will probably not take long before we can simulate systems that too difficult to solve for regular computers. We can then expect better understanding of many physical and chemical processes such as high-temperature superconductivity, quantum phase transitions, dynamics of chemical reactions, or photosynthesis. And all that thanks to the incredibly rich and intriguing structure of the mathematical language we use to describe our world.