S2E9: Practical Quantum Computing with Dr Alex Moylett

What are the obstacles on the path towards finding practical uses for quantum computers? Take a listen to Season 2, Episode 9 of insideQuantum to find out!

This week, Dr Alex Moylett, a Senior Quantum Scientist at Riverlane, tells us all about their work in determining what current-generation quantum computers are good for, and which problems might see a computational advantage on quantum hardware.

Dr Alex Moylett obtained an MEng in Computer Science from the University of Bristol, followed by a PhD at the Quantum Engineering Centre for Doctoral Training, also at the University of Bristol. They then joined Riverlane, where they are now a Senior Quantum Scientist working on applications for near-term quantum computers.

🟒 Steven Thomson (00:06): Hi there and welcome to insideQuantum, the podcast telling the human stories behind the latest developments in quantum technologies. I’m Dr. Steven Thomson, and I’ll be your host for this episode.

(00:16): In most of our previous episodes, we’ve talked to academic researchers based in universities, but over the last few years there has been an explosion of exciting new startups working on all aspects of the quantum computing ecosystem, including the hardware and software aspects, but also recruitments community building and training the people who will use and maintain the quantum computers of the future despite this huge commercial interest in quantum computing. However, the biggest question remains unresolved: just what are quantum computers actually good for?

(00:46): Today’s guest works on solving precisely this key problem: finding useful applications for the kind of quantum computers we have here and now as well as the ones we’ll have in the near future. It’s a pleasure to be joined today by Dr. Alex Moylett, a senior quantum scientist at Riverlane. Hi Alex and thank you so much for joining us here today.

🟣 Alex Moylett (01:04): Thank you.

🟒 Steven Thomson (01:05): So before we get into the details of how you’re helping Riverlane to find useful applications for current and next-gen quantum hardware, let’s first talk about your journey to this point and let’s go right back to the very beginning. And at this point I normally ask people what got them first interested in quantum physics, but if I understand rightly, you didn’t originally start off in quantum physics, is that correct?

🟣 Alex Moylett (01:28): Yes, I originally started in computer science.

🟒 Steven Thomson (01:32): I see. So then how did you go from computer science into quantum computing?

🟣 Alex Moylett (01:37): So the first time I learned about quantum computing was at an open day at the University of Bristol and I remember receiving a leaflet in the open day pack talking about quantum computing. I didn’t really understand it at the time. I have in the years since then reread that same leaflet and understand it a lot better, but that was kind of the first moment in which I learned about quantum computing. It wasn’t really where I got interested in it though, my interest in it came later in my undergraduate. In my final years of study, I became more interested in the theoretical side of computer science and as part of this we had a guest lecture from Ashley Montenaro of the University of Bristol who gave a high-level introduction to quantum computing. And from that I then in my final year took a master’s level mathematics module on quantum information theory, which led to my interest in the topic. I knew I wanted to do a PhD anyway, but hadn’t quite decided what I wanted to do that on. And I found out about this Centre for Doctoral Training in quantum technologies, quantum engineering at the University of Bristol. And so I decided to apply for it and managed to be offered a position.

🟒 Steven Thomson (03:21): So what was it about quantum computing that appealed to you more so than the type of, I guess classical computing that you’d seen earlier in your undergrad career?

🟣 Alex Moylett (03:29): So one topic that I was particularly interested in during my undergraduate was complexity theory, this idea of there are some problems out there which are fundamentally difficult for classical computers to solve, and a topic of particular interest as well was cryptography. So in my kind of third and fourth years of study, this was a topic that particularly interested me and it was through learning about these topics and then learning about how quantum computers could solve problems which classical computers cannot, and particularly the problems such as factoring, which is one of the key problems for modern, for current crypto cryptographic systems. Through knowing that quantum computers were capable of solving problems, which classical computers cannot, that kind of first whetted my appetite for learning more about these new devices.

🟒 Steven Thomson (04:45): I see. So it’s really the cutting edge of what computers are capable of then that’s what interested you more than just, I guess optimising classical computers. It was really what can these new devices do that the existing ones can’t, this whole new avenue for computers.

🟣 Alex Moylett (05:00): Yeah, this completely different paradigm as it were.

🟒 Steven Thomson (05:03): Yeah, I see. So then was it immediately obvious to you that you wanted to do quantum computing as a career or was the PhD more of a sort of trial run and then at a later date you decided this was something worth pursuing further?

🟣 Alex Moylett (05:19): The PhD was initially kind of a β€˜dipping my toes in the water’ type scenario, particularly since I was coming from a computer science background. The last time I had studied anything to do with physics was before university, and so going from that straight into a postgraduate level physics degree was definitely a bit of a difficulty spike. It wasn’t entirely clear, especially my first year, whether or not this would be a long-term thing. As I progressed through my PhD, I grew more comfortable. I found that there were spaces in which I could fit my kind of research background, and so at that point I felt more settled. I still wasn’t entirely sure about staying in academia versus moving to industry, but I definitely did want to kind of stay in quantum technologies for a little bit longer.

🟒 Steven Thomson (06:32): That’s quite a jump. Then going from not really having a background in quantum physics to suddenly working at the frontiers of quantum physics, I guess it’s only relatively recently that these sorts of opportunities have been available now that quantum computing has kind of arisen as a field and it requires people with this more computer science information theoretical type of background. And now it’s kind of a union of computer science, which has been established for a long time for classical computers and quantum physics, which has mostly been about, I guess materials and things and now suddenly these two are coming together to design new devices to solve new problems. So I guess maybe you’re one of the first generations who’s actually been able to kind of come at quantum computing from that particular angle.

🟣 Alex Moylett (07:17): And it was definitely a bit of a jump doing it, though, one of the things that I’m very thankful for is that the PhD program I was on had a cohort model to it, so they brought on students from a variety of backgrounds, so not just physicists but also mathematicians and chemists and computer scientists, electronic engineers. And so you had this wide diversity of different disciplines and that meant yes, I did struggle a bit with the more physics oriented aspects, but I was able to reach out to the physics students on the program to help with those aspects. And likewise, when we got to the more computer scientific algorithmic parts of the course, I was able to give back and contribute my experience working with algorithms and computer science and help the other students on my cohort. And that’s something I also continue to notice in my current job as well. So if you look on our website, we have a mixture of mathematicians, computer scientists, physicists, software developers, chemists, all working under one roof together and all collaborating on different projects.

🟒 Steven Thomson (08:45): Yeah, I think the EPSRC will probably be very happy to hear you say their Doctoral Training Centre model works well. I guess that’s exactly what they try to achieve with these centres to bring people from different backgrounds so they can all learn from each other and all contribute their own various specific expertise into some kind of new field exactly as you describe.

🟣 Alex Moylett (09:07): Yes, and it’s definitely something which is required for a field like quantum computing. It’s not just a physics problem, it requires contributions from all sorts of different backgrounds.

🟒 Steven Thomson (09:24): Yeah, definitely. So you mentioned the company that you work for. Can you give us a quick summary of your career to date and tell us what it is that you actually do at the moment please?

🟣 Alex Moylett (09:35): Sure. So I was hired at Riverlane in early 2020 as I was finishing up my PhD and wasn’t entirely decided on whether or not to stay in academia or move to industry. In the end, I made a decision based on, there wasn’t much of a distinction in terms of what work I would be doing. Even the kind of industrial side of quantum computing was still very research oriented. In the end, I ended up settling for my current position. I then ended up not actually moving to Cambridge for about a year or so due to the pandemic.

🟒 Steven Thomson (10:24): Of course…

🟣 Alex Moylett (10:25): But I have been based here ever since. Nowadays my work…so since working at Riverlane, I’ve been on a variety of different areas from, so previously I worked on some quantum algorithms for chemistry. Nowadays I am working on the quantum error correction side, so exploring different error correction codes and seeing how well they perform under different noise models and different constraints.

🟒 Steven Thomson (11:03): So you’ve explored a couple of different topics then even within Riverlane, a single job, you’ve been able to explore several different things. If you hadn’t gone down this road and if you hadn’t joined Riverlane, if you hadn’t continued maybe with research into quantum computing, what do you think you might have done instead?

🟣 Alex Moylett (11:21): I’ve always had the backup plan of moving back to classical software development. Yeah. So yeah, even as I was applying for this PhD program, I was still looking at kind of software positions within industry. So if I hadn’t gone down the quantum computing route, I would probably be working in a software company.

🟒 Steven Thomson (11:49): Well, I think quantum computing should probably be thankful to have someone who knows how to do software development because speaking as someone coming from the physics background and as someone who does code things for physics, physicists don’t know how to code, do we? It’s very helpful to have input from people who actually understand how to structure this stuff, how to develop software, not how to just write a short script to solve an individual problem when it comes to the more complex, bigger problems. It’s definitely really important to have people with that sort of a skill set.

🟣 Alex Moylett (12:18): Yes, I always agree entirely. A lot of my work is a mixture of research oriented code and exploring new ideas, but it’s also about building our company infrastructure and making sure that our code is high quality, that it’s well documented, that it’s well tested. A lot of these kind of best practices which you learn as a computer scientist but not necessarily if you’ve come from a mathematics or physics background.

🟒 Steven Thomson (12:49): I guess that’s almost more important for a company if I suppose you don’t know necessarily who’s going to take the code that you’re writing, who’s going to run it or what the purpose is. Whereas a lot of the time in pure research maybe you write a script to solve a specific problem and you can be pretty confident, probably no one’s ever going to use it to do anything else. Maybe they’ll rerun it to verify your conclusions or something, but how to say the use cases of that code are pretty narrow, whereas I guess in a company it’s probably much, much broader. You maybe don’t know what people are going to do. So I guess it has to be much more robust and able to handle people coming from all sorts of different backgrounds with all sorts of different ideas in mind for what they want to use with what they want to do with your code.

🟣 Alex Moylett (13:35): And there’s also a sense, and you’re not just writing code for yourself, you’re writing code for the benefit of other people as well. You could be writing them for hardware partners or customers and you want to make sure that the code works well for their particular needs.

🟒 Steven Thomson (13:53): Yeah, definitely. That’s also a good point. I hadn’t really thought of that - writing code that other people can use and understand for their own purposes. Again, that’s something I think in research we’re not always great at. There’s not a lot of incentive to document your code or comment or even structure it in a way that other people are able to easily read or understand. Yeah. So you mentioned a couple of different applications that you’ve been involved in. Can we zoom out for a second? What’s the big picture goal of the field that you work in and where does your work fit into that big picture?

🟣 Alex Moylett (14:28): So the big picture of what we work in is fault tolerant quantum algorithms. So this is looking at what will a quantum computer be able to do, say five or ten years from now at a point where our physical quantum computers have scaled up to enough qubits that we can start to implement quantum error correction techniques. So these are techniques where you take many noisy physical qubits and use them to represent a smaller number of logical qubits which are more immune to errors. And this is an important milestone because it’s the point where we can start to implement these large scale quantum algorithms, the ones which will be able to, say, factor 2048 bit integers or solve new quantum chemistry applications. And so in order to understand where this point is, we work across the full quantum computing stack. So looking at improvements in the quantum algorithms themselves as well as looking at better ways of implementing these algorithms at an error correction level all the way down to developing better control systems so that people can manage their qubit. It’s better. And so the work I do is kind of in the middle of that stack looking at different error correcting codes and learning how well they perform.

🟒 Steven Thomson (16:32): I see, I see. So the goal is that current quantum computers then are they’re small and they’re noisy, and is it fair to say it’s unclear what problems they can solve at the moment until these challenges are overcome and then the hope is that once these challenges are overcome, once people have better ways to control qubits to error correct them and so on, that’s really when these things are going to come into their own and start being able to do genuinely new things that classical computers cannot do.

🟣 Alex Moylett (17:01): Yes. So in the near term there might be some interesting proof of principle demonstrations that people can run. This is kind of your noisy, intermediate scale quantum-type applications. I think there are some interesting problems there, but if you want to get to solving problems where quantum computers have a known theoretical advantage, then we need to think about these long-term applications and fault tolerant quantum algorithms.

🟒 Steven Thomson (17:38): I see. So speaking of situations where quantum computers do have a proven theoretical advantage while preparing for this, I was looking through your record, and I saw the title of your PhD thesis was - the beginning part - was “Towards a quantum speed up” and I feel like that summarises kind of both the state of the field at the moment and the goal. Can you maybe break that down for us? What does it mean? What is the quantum speed up and in which situations could we expect or hope to see it?

🟣 Alex Moylett (18:07): I actually had to look through my PhD thesis to remind myself of this. So the way I defined a quantum speedup in my thesis was using three particular points. The first is that it had to be a problem which is feasible for quantum computers to solve. The second was that it had to be a problem which was infeasible for classical computers to solve. And the third was that it needed to have real world applications. So in my PhD thesis I looked at this from two different directions. One was from an application perspective looking at how quantum computers could solve a mathematical problem known as the travelling salesman problem, which we were able to demonstrate had a proven benefit on quantum computers and is known to be hard for classical computers and does have real world applications. The disadvantage to that approach is that you require error correction.

(19:24): So this is not an application which is going to be feasible in the near term. The other direction that I looked at, which is more feasible in the near term was a more architecture-oriented approach. So looking at the problem of a problem known as boson sampling, which is…so boson sampling is a problem based around linear optics. So experiments with light, and this is feasible for, this is feasible for near term quantum computers and is proven to, it is proven to be infeasible to solve on the classical device. And there are potentially some applications though nothing completely concrete in terms of being able to offer quantum advantage.

🟒 Steven Thomson (20:25): Yeah, I have to say, boson sampling is one of those things I’ve heard a lot about, but I’ve never actually understood what it is. I understand that it’s a neat demonstration of quantum systems, but I’ve never understood enough about what it is to see how it could be useful. Which is not to say that it’s not useful, just to say that I don’t understand that part.

🟣 Alex Moylett (20:46): It’s okay. Took me at least two years into my PhD before I could say with confidence that I understood this either.

🟒 Steven Thomson (20:55): Now I don’t feel so bad.

🟣 Alex Moylett (20:58): So nowadays my focus is much more on the first of those two sides, thinking much more about fault tolerant algorithms and applications and thinking about how to make those more feasible as a direction for a quantum speed up.

🟒 Steven Thomson (21:17): So something like the travelling salesman problem, I may not remember the details, but this is essentially an optimization problem, right? It’s about finding the best route for the eponymous travelling salesman to travel between certain points. So are optimization problems in general a good use case for quantum hardware?

🟣 Alex Moylett (21:35): So there is some debate about this in the community optimization problems. There are few challenges with the feasibility of optimization problems for quantum advantage. So the first problem is that quantum computers do not offer an exponential speed up over classical computers. So other problems such as factoring or understanding chemical properties. We have reason to believe that quantum algorithms for those problems have an exponential speed up. Whereas for optimization problems like the travelling salesman, we expect only a polynomial speed up compared to classical algorithms. Then a second problem with that is once you incorporate error correction into that system and you add on the overhead for quantum error correction, you still have that polynomial speed up asymptotically, but you’ve now got a significantly larger overhead out front. And the third problem that you’re dealing with is that classical algorithms for optimization problems have been very heavily optimised.

(23:06): So comparing to the very best classical algorithms sets quite a high benchmark. And so when you combine these three problems together, what you find is that asymptotically, there will be a large enough problem for a quantum computer to offer you benefit, but the problem size needs to be so big by that point that your runtime might be impractically large anyway. And so there comes a question of should you be trying to solve a problem of this scale in the first place? So I think optimization problems are interesting and I think there could be some benefit to them, but the benefit might be more limited than for other applications.

🟒 Steven Thomson (24:02): I see. So the real holy grail, there are the problems that display the exponential speedup that you mentioned. That’s really the difference between being able to solve the problem at all on a quantum system and it being essentially impossible on a classical system. Okay. So can you tell us a little bit more about the work that you’re doing at the moment?

🟣 Alex Moylett (24:22): So related to this problem of trying to find a quantum speed up, my colleagues and I are currently working on understanding the overheads for applying error correction primitives [Editor’s note: this work is now out and available at https://arxiv.org/abs/2307.03233]. And so we have taken a small scale application, estimating the ground state energy of the hydrogen molecule, and we are researching how many physical qubits and how many gates or rounds of error correction would it take to actually solve this problem on a fault tolerant quantum computer using our current error correction techniques. And so this has involved working at a number of levels of the quantum computing stack, so looking at what optimizations we can make at the algorithmic level, understanding how those translate into operations that you can run on a quantum error correcting code, and then going to what resources it are required for you to be able to run this application on an error correcting code at a certain desired level of accuracy. And so we’ve investigated improvements in all of these directions and come up with some rough estimates for what it would require. This is important for a number of reasons. Firstly, it provides us with an understanding of what is required to achieve a quantum advantage or not even a quantum advantage, just what is required for a proof of principle algorithmic demonstration. And this also gives us an idea of what the constant factor overheads are for fault tolerance quantum computing, and gives us a sense of the scale required in order to achieve fault tolerance.

🟒 Steven Thomson (27:04): Given the existing challenges in things like fault tolerance, that sounds like a pretty important thing to look into

🟣 Alex Moylett (27:13): And even for…one of our findings is that even for an application this small, you still require on the order of hundreds of qubits and thousands of gates. So definitely. So it is still quite a daunting task. This emphasises why we need to think about error correction for the near future where we might not have millions of qubits, but if we’ve got say a couple hundred qubits, what error correction techniques could we apply there and how could those help us with proof of principle algorithmic demonstrations?

🟒 Steven Thomson (28:05): So the hardware is right on the verge of being able to do something genuinely. Very, very interesting. That’s great to hear. So then what does a day in your life look like? It sounds like you’re working on very different things and very exciting things. What does a typical day look like for you and what are the kinds of tools that you use to address these questions?

🟣 Alex Moylett (28:29): So I normally start my day with a couple of minutes to browse through the arXiv and look for any interesting new papers that have come out.

🟒 Steven Thomson (28:41): I’m thinking that…when you said arXiv in a few minutes, I’m thinking it must take a lot longer than a few minutes to read the quantum computing arXiv these days. It seems like there are more and more papers every day.

🟣 Alex Moylett (28:50): I don’t read it in detail, is the trick. I skim the titles and the abstracts.

🟒 Steven Thomson (28:59): Yeah, sensible move, I think.

🟣 Alex Moylett (29:02): So a lot of our work is programming based, so it’s running small simulations of algorithms under different noise models or error, simulating small error correcting schemes under different forms of noise and seeing how well they perform. A lot of this simulation is Python based because having people come from not necessarily a computer science background, Python is a very good language to pick up, is a very simple language to pick up and understand and be able to start writing code quickly.

🟒 Steven Thomson (29:46): Yeah, yeah, I can agree with that. Certainly it was the first language that I learned as a physicist wanting to use some computer code to solve a problem. Python was my go-to for exactly the reasons that you described. It was relatively easy to pick up and less frightening than something like C, which sometimes just fails for bizarre reasons that are very hard to track down unless you really know what you’re doing. And I certainly did not when I was learning that stuff.

🟣 Alex Moylett (30:10): My undergrad dissertation was written almost entirely in C and I can strongly support that statement.

🟒 Steven Thomson (30:18): Yeah, I’m sure it’s great if you know what you’re doing, but yeah, I certainly was never someone who was all that comfortable in C. It’s been interesting actually that Python seems to have become the de facto language of a lot of quantum computing. We have all these nice packages now from various different companies and it just seems to be, they’re all kind of converging towards Python. I guess for the reasons that you mentioned. It’s quite easy to pick up, I suppose, and if you know what you’re doing it can also be quite performant, and it can interface with other code libraries if you really need certain other features or other things to be done quickly. I guess from what you said there, a lot of what you’re doing is modelling qubits. Do you ever run your simulations directly on quantum hardware or is it always running it on simulated models with simulated noise?

🟣 Alex Moylett (31:12): We do work with a number of hardware partners in the company. We do a mixture of simulations based on their noise models as well as running algorithms directly on their hardware. So one of my colleagues recently…or a couple of my colleagues have recently submitted a paper to the arXiv about some work they’ve done running an algorithm called Statistical Phase Estimation on Rigetti’s hardware (https://arxiv.org/abs/2304.05126).

🟒 Steven Thomson (31:43): So then the idea is there that one of the customers could come to you and say, okay, I have a quantum computer, this is the noise profile, this is roughly the type of errors that I would expect. And then you take this model and then you can design some algorithms that work well with this noise model. And then once you’ve tested that and optimised that, you can then take that algorithm and then run it on the actual hardware and ideally it will be a good match for the hardware and it will give you the results that you’re looking for.

🟣 Alex Moylett (32:10): Yes, that is the ideal.

🟒 Steven Thomson (32:12): I see. I, so I was going to ask that your work kind of lives at this intersection between computer science and physics, but I guess it’s also at the intersection between research for the sake of, I guess fundamental research just to see what these systems can do and also industrial use cases, particularly if you’re interfacing here with customers and companies who want to use your code. So you’re kind of at two different intersections at once here. That sounds like it must be kind of challenging to navigate both the combination of physics and computer science and then also this sort of orthogonal combination of academia research and industrial applications. Has it been challenging to navigate these different environments? Do you have to have a different head to talk to the research people as to the industrial people or does it all feel like they’re all contributing towards the same goal? So it’s all one and the same.

🟣 Alex Moylett (33:08): There are definitely some challenges to it from just between the physics and computer science side. One of the big difficulties is the language barrier between these two different disciplines. So this is something I have experience with back in my PhD - my supervisor came from a theoretical physics background, and so finding this kind of common language for the two of us to communicate in was already a bit of a hurdle in getting started. There are other difficulties as well with moving from an academic environment to an industrial environment. So first of all, an industrial environment is more collaborative in some ways. You’re not just an individual working on your own projects for the benefits of your PhD thesis. You are now working in a small team on, say, a larger project which may lead to research paper, but also may equivalent just be for the benefit of a customer or the heart or an end user. And so you’ve got this mixture of trying to get used to a more collaborative environment, plus trying to think about not just your own interests but the interests of other people. And there are also other difficulties as well in that you are more collaborative within your own company, but then you need to be more careful about being around people who work for a competitor.

🟒 Steven Thomson (34:59): It’s an interesting point there, the difference between collaborating within a company but also having to, I suppose, safeguard your company secrets from people in other companies who might have genuinely useful advice or interesting insights, but there’s this sort of conflict of interest, I suppose, this kind of barrier between you. That’s an interesting dynamic I’d never really, really thought about in industry.

🟣 Alex Moylett (35:22): And it’s also interesting because a lot of the people who work at these different companies, because it’s such a small community, we still know each other personally. I know people who work as our competitors from who I first met during my PhD who may have studied at the university - at the same university as me - or been to the same conferences. And so there’s this kind of friendly atmosphere, but at the same time we need to be careful about not saying too much.

🟒 Steven Thomson (35:56): Yeah, definitely. I mean, I guess I’ve heard in academia people talk about you don’t want to give away your secrets, you don’t want to be scooped, but I suppose the dynamic you’re talking about there is slightly different when it’s, it’s almost not your secrets anymore, I guess it’s your company’s secrets and your client’s secrets and so on. So there’s a, I guess an extra level of double think there when you want to talk about some cool discovery, but go through all those hoops. Can I tell this person, can I share this information? Yeah, that’s interesting to hear.

(36:25): Yeah, it’s also interesting there that you focused a lot on the collaborative nature of it and I guess the act of working with other people in a company is something that you emphasise there more so than when you’re in academia. I think that’s good to hear because I guess it’s people that make the industry, it’s people that make also true in research I suppose. But I suppose the people who you work with who make the job worth doing and interesting on a daily basis.

(36:57): And I think that leads nicely into one of the questions that I like to ask every guest on this podcast, which is that physics and I guess possibly also computer science, but physics certainly has historically been a field dominated by white cisgender men for an extremely long time, and there’s clearly a long way to go until we reach a level playing field. In your experience, having worked in both industry and academia and with a bit of experience in computer science and in physics, have you seen things changing at all between the different disciplines or between the years that you’ve been involved in the fields?

🟣 Alex Moylett (37:35): I think there’s definitely been some progress, yes. So I think there’ve been some definite improvements in the field, not just within these particular fields, but within academia as a whole. So one that particularly comes to mind is as a trans person in the field, one of my most cited papers during my PhD was published under my former name, and it was always a kind of gripe for the rest of my PhD and the start of my time in industry that I wasn’t able to change that for several years. And it’s only been in the last couple of years where journals and the arXiv have updated their policies to allow people under certain circumstances to change their name.

🟒 Steven Thomson (38:30): I wasn’t even aware that was an option. I thought once you publish something under one name, it’s fixed forever. So now you are able to go back and update these older papers?

🟣 Alex Moylett (38:40): Yeah, it’s something I wasn’t aware of either until about a year or two back. It’s still not a perfect process. So my understanding is with the arXiv, they have to go into the database and manually edit the LaTeX document to change your name in it, but it’s a nice kind of step forward and is something which doesn’t just affect trans people as well. Plenty of people have changed names in academia for one reason or another, and it’s always a difficult decision to make because you are making it that much harder for someone to go and look up your research backlog. There are definitely still things that I think can be improved. So particularly given the global nature of research and development, there are still some difficulties with say what places I’m able to visit. So one example that I often bring up is if I want to go to a conference in Australia, for example, if I were to travel via the United Arab Emirates, that would actually be a risk of me being held at the border. So I think there have been some improvements, but I personally…I think we are making good progress.

🟒 Steven Thomson (40:25): Well, it’s good to hear that there is progress happening and I think the point you mentioned there is important. I’ve also seen a number of trans people I know in the United States now making lists of states where it is safe to visit, states where if there is a conference, it is safe to travel to, and states where it really poses a personal risk. And yes, obviously that’s not a calculation anyone should have to make for professional reasons. I mean, we should all be able to experience the same professional opportunities and networking opportunities and so on. And I think it’s an important point for people to be made aware of who maybe haven’t thought about it already. I guess particularly in places like the US where the laws change from state to state and particularly in countries where any demographic might be at risk to travel there or travel via there. I think it’s certainly an important point to bring up.

🟣 Alex Moylett (41:15): And I think it’s important to emphasise how often these things are closer to home than people realise as well.

🟒 Steven Thomson (41:25): Yeah, definitely. Definitely. Well, I hope that the more people who are made aware and have these conversations, I hope that will lead to material change in the future, hopefully sooner rather than later. One final question to wrap up with then. If you could go back in time and give yourself just one piece of advice, what would it be?

🟣 Alex Moylett (41:56): I think my main piece of advice would be don’t panic.

🟒 Steven Thomson (42:01): That’s good advice.

🟣 Alex Moylett (42:03): Yeah. My career path has never been the most well planned out or set in stone thing, and there’ve been points where I’ve definitely stressed about that, but it has, I am pretty confident in saying it has worked out in the end, I’m very happy in the position that I am. And yeah, I think that it has all kind of worked out in the end.

🟒 Steven Thomson (42:34): It’s always interesting to hear when people give advice of that nature that’s sort of like you never could have necessarily planned or guessed where you would end up, but somehow you, by accident, by design, by whatever, somehow you’ve managed to end up in the right place where you want to be and where you’re doing something, doing something that you enjoy. And it’s always nice to hear when people manage to do that.

🟣 Alex Moylett (42:57): Yeah, I mean this is definitely not where I expected to end up. I initially studied computer science because I like video games.

🟒 Steven Thomson (43:06): A few of our guests have said similar things actually.

🟣 Alex Moylett (43:10): And so yeah, definitely did not expect this to be the path my career took.

🟒 Steven Thomson (43:19): Alright, well I think that is a good note to wrap things up on. So if our audience would like to learn a little bit more about you, is there anywhere they can find you on the internet, social media, anywhere like that?

🟣 Alex Moylett (43:31): I’m not that active on social media nowadays, to be honest. You can find me on LinkedIn, but I can’t guarantee a quick response. You can also find me via the company website riverlane.com, and that’s mostly it. I do have a Mastodon as well, so probably easiest if I just link that.

🟒 Steven Thomson (44:00): Yeah, yeah, we can leave links on our own websites and wherever we share this episode.

🟣 Alex Moylett (44:07): Yeah, so yeah, LinkedIn, Mastodon or the company’s website are the best places to find me.

🟒 Steven Thomson (44:16): Okay, perfect. Then we will be sure to leave links to those on our own website insidequantum.org. Thank you very much, Dr. Alex Moylett for your time here today.

🟣 Alex Moylett (44:25): Thank you.

🟒 Steven Thomson (44:26): Thank you also to the Unitary Fund for supporting this podcast. If you’ve enjoyed today’s episode, please consider liking, sharing and subscribing wherever you’d like to listen to your podcasts. It really helps us to get our guest stories out to as wide an audience as possible. I hope you’ll join us again for our next episode. And until then, this has been insideQuantum. I’ve been Dr. Steven Thomson and thank you very much for listening. Goodbye!