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 as usual, I'll be your host for this episode.
(00:17):
In previous episodes, we've talked a little bit about the hardware that might make up future quantum computers, but we haven't gone into much detail about how it works. We've spoken about qubits -- quantum bits -- in quite abstract terms without really describing what a qubit is, what one is made of or how they work. Building robust and reliable qubits is actually a huge challenge, and it's one of the most important things to get right before we can have large scale quantum computers. Today's guest works on solving this critical challenge using superconducting quantum circuits to construct these fundamental building blocks of quantum computing. It's a great pleasure to be joined today by Dr. Yvonne Gao, an assistant professor at the National University of Singapore, and a principal investigator at the Center for Quantum Technologies in Singapore. Hi Yvonne, and thank you for joining us here today.
Yvonne Gao (01:04):
Thank you for having me. It's a pleasure to be here.
Steven Thomson (01:06):
So before we get into the details of how you build superconducting qubits, not to mention your extensive community work, let's first talk about your journey to this point, and let's go right back to the very beginning. What first got you interested in quantum physics?
Yvonne Gao (01:20):
There are two flows to my answer for this. One is the physics part. The quantum came a lot later, but for the physics part...actually in high school, that was the laziest subject for me because I realized I didn't have to memorize a lot of things. If I understood it correctly, I usually can figure out how to answer my questions and do the homeworks. So that was really nice for a 14, 15 year old to not have to memorize pages after pages of definitions. So I think I got lucky with the fact that it was something that lined up with my interest and also I could be quite good at it without putting a lot of brute force hard work into it. So I just kind of went with it and learned more and more along the way. And then the quantum part came when I was an undergraduate in Oxford.
(02:10):
My tutor, actually, he is one of the earliest generations of experimental quantum physicists. He worked with NMR technology, so nuclear magnetic resonance, and he was one of the teams that realized the first two cubic gates on that platform. So through my interactions, I think that's where the interest in quantum physics started to develop because I realized that you could really translate these very abstract concepts like Hamiltonians, electrons that you can't touch or see easily into tangible experiments in the lab and actually make them do the things that you want to do and demonstrate the effects that we've only learned about in textbooks on paper. So I think that's where I really decided that this is something I want to spend a lot more time learning about and get my hands on and tinker with.
Steven Thomson (02:57):
So it was the practical aspect that appealed to you then, and it was the ability to go into the lab and really do experiments on this stuff rather than the theoretical side?
Yvonne Gao (03:05):
Oh, definitely, definitely. I've always loved working with my hands and just take things apart, tinkering with them and making smart changes here and there to see what effects that will lead into, and I definitely enjoy doing that as part of my day-to-day life.
Steven Thomson (03:20):
So then at what point did you decide that you wanted a career working in quantum physics?
Yvonne Gao (03:25):
I didn't really think super far actually. When I was a student, I think I always had this problem of getting bored very easily. So after talking with a lot of my friends who left physics after undergraduate, and I realized that many of the potential career paths can be a little boring, they repeat themselves after a while. You've seen, you get to see a lot of it in the first few years and then it kind of starts to repeat. So I thought research could be really good because we're, at least from what I was told at that age, we're always solving new problems and problems that there are no known or certain answers for. So I thought that could be interesting and probably never gets boring if nobody else, nobody knows the answer. So that's actually how I decided to do a PhD in this field, and I guess I just thought if I do well in it, there's probably a good career path after and I'm really glad things worked out pretty well.
Steven Thomson (04:27):
They have indeed. You've been very successful. Can you give us a quick summary of your career to date and tell us how you got where you are now?
Yvonne Gao (04:36):
Yeah, of course. I did a pretty standard training for a physics student or someone who's interested in knowing more about physics. I did my undergraduate in England in a just standard physics education, and then I went to the US - went to Yale for a PhD in experimental physics focusing specifically on superconducting quantum devices. And since then I actually did a slightly unusual leap for an early career scientist. I didn't get a full post-doctoral training after my PhD. I came back to Singapore - as part of my scholarship commitment, actually - and I joined a national lab and realised that perhaps in a big organization doing more managerial aspects of science was not exactly my cup of tea. So I very quickly applied for the early career fellowships and opportunities in Singapore and thankfully got several of them so that I was able to have all the resources that needed to set up my own group about a year and a little bit after I finished my PhD. So, I always tell my students that I was forced to grow up a bit too quickly to have them manage my own team, setting up the nuts and bolts from scratch. But overall, this experience has been really, really exciting and very interesting, quite a learning journey for me so far.
Steven Thomson (06:04):
Yeah, definitely. That's a very rapid rise. If you weren't doing your current job, what else might you be doing? Are there any other careers that appeal to you along the way?
Yvonne Gao (06:15):
Yeah, here and there. I think after my PhD, I did work kind of in collaboration with a local quantum computing startup called Horizon Quantum, and I really enjoyed being in a small dynamic team working on the latest technologies. It's still research, just not academic research. So that could have been something that I would very much enjoy pursuing if I had not come back to academia and started a research team. I do have to say, I think one thing that put me back was the fact that in academia we are, we have more of the luxury of doing curiosity led work. So I could take an intellectual detour with my team that, oh, that could be interesting. Let's just look into it for a couple of months without worrying too much about what do I have to deliver at the end of it? Do I have to live up to a promise? The conclusion or the outcome of these small investigations could be simply, well, we're learning something interesting out of it and it doesn't have to be something super tangible. And I really like that about academic research.
Steven Thomson (07:30):
Yeah, that's something I hear a lot, that you have the freedom to pursue these little ideas, these kind of mad ideas that might not go anywhere in a commercial context, but yes, you still get to chase them up and learn something.
Yvonne Gao (07:41):
Definitely. And I think it's through these little experiences where we might very well end up not achieving a tangible result that we actually gather the most useful feedback because then we learn why it didn't work out and how we have failed in a particular attempt. And it really all goes back into making our primary focus and primary experiments work out much, much more rapidly and more smoothly.
Steven Thomson (08:04):
It's nice to hear you say that because people often don't acknowledge that failure can be a learning experience. You can learn a lot more from failure sometimes than you can learn from success.
Yvonne Gao (08:13):
Absolutely.
Steven Thomson (08:14):
Yeah, we see publications, we see successful flashy experiments and results a lot of the time, but people don't often talk about all the failures that they had to go through to achieve that kind of insight.
Yvonne Gao (08:26):
No, definitely. And I think it's something that I think now that I'm on the other side, we're mentoring students that we should be more actively encouraging our students and our community to do because we failed 10...especially for experiments, we fail like 10, 20 times before we get the hero device and everything lined up to make these beautiful experiments. And it's really through these prior attempts that we learn all the useful knowledge to put everything together. And I think the goal is there, and one trick I always tell my students to do is if something they're interested in learning about is a publication in one of the top journals, most likely the paper itself will not have too much information because they're so short. So the most important thing to do is to go figure out who the authors are, who is a PhD student who's about to graduate, and then read their thesis because that's where all those extra attempts and frustrations and the really valuable information about the mistakes they've made are documented and that's what helps us the most.
Steven Thomson (09:33):
That's really good advice. Yeah.
Yvonne Gao (09:36):
It's a life hack that probably shouldn't need to be there, but the way we publish unfortunately pushes us to do that a little bit.
Steven Thomson (09:48):
Yeah, definitely. So then talking about your research, can you give us a bit of an overview? What's the big picture goal of the field that you work in and where does your work fit into this big picture?
Yvonne Gao (09:59):
Yeah, definitely. So the whole field of superconducting quantum circuits actually has really evolved in the past two decades or so into this wide spectrum of building hardware and theory to look at on one hand very fundamental signs of entanglement, of decoherence, of condensed matter physics, and on the other hand, the extreme other end of building scalable quantum hardware for quantum computation. So the field uses these very bespoke kind of quantum devices that we can engineer, we can fabricate, and we can more or less tailor the parameters with the intention, with the intended target applications in mind. And our work now takes advantage of these engineered devices, these very tunable architectures of our hardware to study both the interesting fundamental science aspects as well as to think about perhaps the useful things related to information processing. More specifically, we are looking to not using qubits, but possibly continuous variables, so large Hilbert space involved in bosonic elements or sometimes we think about them as a potential to encode q-dits, so multi-dimensional qubits.
(11:30):
So how we fit in is basically looking into this area of quantum information processing that's realistic in a world where we have noise and decoherence effects. So we have to encode the information that's actually robust to these realistic errors. So in theory, I think we can do these beautiful devices with very quantum correction codes to make sure they're efficient. But in practice we very often have to fight against local noise, such as just losing some energy to the environment. And to do this in practice requires a lot of hardware overhead typically. And what we're looking into is something that offers the potential to be a little bit more efficient and making the experimental list life a little easier so that we can use fewer hardware pieces and still encode information in a way that has the capacity and the complexity to eventually do quantum computing. It was somewhat a long-winded answer, I think.
Steven Thomson (12:32):
No, that was great. So I think you touched on that a little bit there, but what's the biggest challenge in your field at the moment?
Yvonne Gao (12:43):
Yeah, that's a really good question. I think there are several. I think on the more field specific point of view, the quantum error correction aspect is definitely one of the most important challenges we're trying to solve as a field, both in the more discrete variables, the more textbook like examples of using qubits, how do we make quantum error correction codes out of them, as well as in the continuous variable versions where we use the bosonic elements and try to think about more creative ways of encoding information that takes advantage of the symmetry properties in our bosonic quantum elements. But perhaps from…as an experimentalist, something closer to my heart and more practical is the challenge of making things more reliable and reproducible. So at a moment, a lot of us actually have the ability to make very good devices, but making many good devices is quite difficult.
(13:44):
So we can make small things maybe once or twice. Good things, good devices that gave us beautiful experiments. But if I want to reliably make that over and over again for all the experiments that I have in mind and do that on demand, that becomes quite tricky. And that's because there is a lot of variability in the way we put the material on the substrate, the way we design the devices and the way the process works in cleaning...all the chemical processes that's involved in cleaning and making these devices. So I think these variabilities at the moment are one of the hardest challenges to resolve really from the experimental point of view because we can't just rely on hero devices to make one or two really wonderful experiments. We actually need them to really be made on demand and always have similar performance, performance values. So that's something that I would love to see more efforts go into and to learn more from the community out there.
Steven Thomson (14:49):
Why are they still so difficult to produce? Is it just because these are very new technologies and the processes and the pathways are still being established?
Yvonne Gao (15:01):
Yes. I think this...if you ask different people in the field, we're going to have very different answers. For me, I think one of the reasons is because these have been mostly made by physicists, not engineers and material scientists or chemists. For physicists, we think about this in a way that's very analytical. So I think, okay, this is the process. This is a pattern, this should give me the desired result. And sometimes we get there and we're happy with it because we can do the next step using whatever we have. So that's kind of the beauty of the platform. We don't have to be perfect to do interesting things. So that's nice. And for many years we've been doing that, but to really pin down the processes and little material quirks, we need the help of chemists, material scientists, process engineers, fabrication specialists, and I think that's just starting now. In the past handful of years or so, we are having these more collaborative approaches to really listen to other experts on how to make this in a more consistent way that is actually reproducible.
Steven Thomson (16:11):
Great. So it's a really, a very interdisciplinary challenge then, something that involves a lot of different expertise from different fields.
Yvonne Gao (16:16):
Absolutely. I think we have to learn a lot from our colleagues across the different areas because some of the challenges we're trying to solve now, like cleaning a surface, this has been studied by material scientists for a long time. It's just we've not talked to them and they've not talked to us. And I think a lot of this knowledge is there. We just haven't connected the dots yet.
Steven Thomson (16:40):
How do you go about breaking down the barriers between these communities then? How do you find out that material scientists already know how to solve these challenges?
Yvonne Gao (16:51):
Yeah, that's a question we ask ourselves all the time because it's really not easy. One reason why it's really difficult is because we speak very different languages. We call the same thing very different names. So when we speak to another colleague in a different field, it's almost like we need a translator in between. So I think personally, my way of doing this is to just be very proactive and go with a very open mind. I just basically go with the intention of me not knowing anything. I just ask them to tell me their research as if they were explaining to an undergraduate. And I think that really, really helps. Another way that I've been getting some help doing this is my team is very diverse. I have students from material science background, from CS background, from electrical engineering background instead of just a traditional physics training. And they actually bring in these contexts, these information...the literatures that they went through in their Master's studies are actually really helpful for us to learn and read about. So I think the diversity in the quantum computing and the superconducting circuit field is starting to help resolve this issue in an organic way.
Steven Thomson (18:10):
Fantastic. So if I were to try to summarize your research in a single very oversimplified phrase, I might pick superconducting quantum qubits. [Editor's note: The word 'qubit' already contains 'quantum' - it’s a ‘quantum bit’. These should more properly just be called 'superconducting qubits'. Steven had only just had his morning coffee when recording this episode and clearly it hadn't kicked in yet...!]
(18:21):
Can you break this phrase down for us? What does it mean? What are superconducting quantum qubits?
Yvonne Gao (18:26):
Yes, I would try that. So there are a few elements to it. One is we can start backwards with qubits, right? Qubits are this contrived and rather abstract definition of a quantum bit of information. And what that means is it can be any conceptually viable definition of something that can be in superposition, right? Superposition, just meaning being in two orthogonal states at the same time, or two clearly distinctive states at the same time. So this is all very, very abstract. So anything can be a qubit if it could follow the definitions of...if it follows the behaviors of superposition and eventually entanglement, et cetera. So how the superconducting part comes in is to narrow it down to one particular hardware. So why is this superconducting? That's because we are building qubits out of electrical circuits, and normally electrical circuits would necessarily have some losses because there is friction, there is resistance, and the way to remove that is to bring everything to a stage where we can conduct electricity, we can conduct current without experiencing any friction or any losses. So ideally this can be achieved through superconductors, which are by definition able to pass current without any dissipation. So that's why we're building these electrical circuits using superconducting materials and by cooling them down to these superconducting states. Our goal is to, well, our hope at least is to remove as much of the dissipation and noise as possible from our system so that we can really narrow down and zoom in on the very small quantum effects that's present in the hardware.
Steven Thomson (20:24):
It feels like building qubits from electrical circuits is almost closer in spirit to existing classical computers as compared to other methods like trapped ion setups, for example, where it feels like the technology is so different to what we've had before that a lot of things still need to be developed. It's kind of appealing that you're still using electrical circuits, which I guess we understand well, but you're using them in this kind of quantum realm to do something new in something interesting.
Yvonne Gao (20:52):
Yes, yes, that's exactly right. I think the way we're taught quantum mechanics is usually through a single electron or a single atom. So that makes the frameworks of some of these other platforms very intuitive for a very traditionally trained physicist. But when it comes to building the hardware for quantum computers, I agree with you that quantum circuits are actually more intuitive when we talk to engineers, when we talk to classical computer scientists because they can find direct analogs almost to what they do. So for instance, we use capacitors and inductors just as they would do in classical computing circuits. We also use nonlinear inductors, which effectively are some sort of diodes in the classical world, or switches. So in that sense, there are counterparts that we can very easily find between the classical and quantum circuits, and that definitely helps when we talk to people who have classical CS background and to explain these things across.
Steven Thomson (21:57):
I see. Okay. So why are superconducting qubits such a promising candidate for future quantum computers as compared to these other technologies? Okay, it's nice that they are more intuitive, I guess, to people from maybe computer science or engineering backgrounds, but do they have any fundamental advantages over trap ions, for example, or any of these other candidate technologies?
Yvonne Gao (22:20):
I think this is very much a personal opinion. At the moment, I think superconducting circuits are probably one of the most mature platforms. The reason why it's been very appealing to get to the stage where we have hundreds of qubits in commercially available platforms is because of this ability to engineer the device. So we can just print classical circuits, we can print quantum circuits in a way that's very tailored to what we want to show with it. So this gives us a lot of ability to put them in different regimes on the same hardware. You can have parts that are strongly interacting and parts that are weakly interacting, and they can serve very different purposes. Some are to store information, so more like the memory elements. Some could be for processing information, doing fast gates, for example, to turn on and off the logic.
(23:13):
And some could be for just extraction of information, so for the measurement. So the different parts can be designed with different purposes in mind and all go on the same hardware, in the same fabrication process. So this is very appealing and I think it gave us the first step into really making these larger intermediate scale devices where we can learn about the behaviors of near term quantum processors. And to be honest, going forward, if I have to envision what a quantum computer may look like, it might not be made out of superconducting circuits. It could be a superconducting devices as well as something else, or it could be something that we haven't even thought of today. But what I would say really is the key value at the moment for superconducting devices is it gives us a fairly accessible hardware with our existing engineering capabilities to reach this hundreds of qubits regime where we can no longer really simulate these devices classically. So we just have to build it to learn how it will fail, what problems we might run into, what kind of new physics does it require us to understand to be able to scale up further. So it really provides this very nice test bed for us to understand and troubleshoot and debug to optimize for the next version that may look completely different from what we see now.
Steven Thomson (24:38):
I see. So because the technology is quite mature, you can build these large systems that theorists struggle to simulate. So you really are in the wild west, you're doing something completely new that is quite unknown.
Yvonne Gao (24:51):
Yes, and I think that's also the only way for us to learn the failure modes because we can't dream up how when 50 or a hundred qubits interact with each other, what kind of weird coupling there might be. These are the things you just no longer can know a priori. You just have to measure it and figure it out.
Steven Thomson (25:07):
I see. So a lot of the people that we've spoken to on this podcast so far have been theorists and over the last few years with the Coronavirus pandemic and so on and lockdowns and travel bans, I think it's probably been easier to be a theorist because we've been able to do a lot of work still from home. What has the impact of the pandemic been on your group's research activities? Because I guess you started your group either just before the pandemic or perhaps even after it started, so that must have been very disruptive for you.
Yvonne Gao (25:38):
Yeah, I started right with the pandemic actually. So I started in March 2020 and we started having issues in April. So I had a month in my office and I had to go home. Fortunately for me, the impact was not too pronounced because in the beginning there was a lot of purchasing and setting up to do that didn't require me to be physically in the lab. So it took me the first three to four months of placing the orders, checking out all the necessary infrastructure aspects so I could do a lot of this remotely during the lockdown. What did affect us a little bit was the admission of our students and the hiring of the postdocs. I had a really good run of meeting the people, finding out the people were interested, having good discussions with them, and many of them were actually delayed in the process of just getting into the country.
(26:43):
So I was very fortunate that they stuck with me and then decided to wait it out and then came in when the situation stabilized more. But I can imagine that could be an issue for a lot of experimentalist teams where you do need people hands on deck and setting things up, but just people cannot come in and without a team it would not be possible to set things up in the lab. So it worked out pretty well for us, but that had been quite a big challenge for a lot of my friends and other early career colleagues in the same period.
Steven Thomson (27:17):
I can imagine that particularly early career postdocs, for example, who don't have tenure and who are depending on publications and they have to get these publications before their funding runs out, that must have been a very difficult period for them.
Yvonne Gao (27:29):
Yeah, absolutely. I think a lot of people had to, I think that this is sometimes...perhaps going back a little bit to how we run experiments, some groups that suffered a little less had their experiments automated more, and I think that pushed people to be more thoughtful in setting it up, which is I guess a silver lining. But at the same time, I do think that people...experimentalists definitely experienced a lot of setbacks in this period, just physically because they were not able to go in to change their setup or interact with the team in person to make decisions on certain big experimental changes. And in Singapore, something that was quite nice that was done was they recognized and extended many early career scientists contracts and delayed the funding deadlines by about a year for most people to address this issue that at this stage where you are the most vulnerable in terms of having this six months or one year to make up for last time. And I think that was much appreciated amounts to people around the same age group.
Steven Thomson (28:49):
So talking of the challenges faced by early career researchers, as we mentioned already, you've had a very rapid rise to your current assistant professor position, and along the way you won the Young Scientist Award in 2021 from the Singapore National Academy of Science, but were there any unexpected challenges that you faced on your journey to becoming a PI and are there any ways that universities and research agencies could better support early career researchers during this difficult period?
Yvonne Gao (29:17):
Yes, definitely. I think for me, there were two aspects. One was the fact that a lot of these awards and grants and fellowships for early career scientists, they bunch too much together. So it's almost like all the different granting agencies are waiting for the other one to first make the move. Once I got my first grant, the next two or three were much, much easier because very often they ask you to justify your experience in managing a grant before they gave you a grant. And if you were just a PhD student or a young postdoc in certain groups, you just don't have this kind of opportunity to do that, and therefore it becomes a chicken and egg problem. So I think there needs to be some structural changes or at least recognitions of the fact that very often at the early career stages, depending on the country we were in, the environment of the group we were in, we may not have the same kind of opportunities when it comes to managing funding or leading research efforts.
(30:21):
And although they're important, we should be evaluating candidates more holistically instead of just looking at how many grants you've had before or how many PhD students you've helped mentor, because these hard metrics could really be very different depending on the environment where we are trained, where we are mentored. Another aspect is that I think from the other side of the aisle for PhD advisors or post-doctoral advisors, I think we really need to look into not just equipping our people with the scientific skills, but also some management skills. Because when I started my own team, I realized that nobody's ever taught me how to do proper accounting, and suddenly I have millions of dollars that I have to manage, and it's taking a lot of time, and a lot of us in this position have to go read help books, how do you efficiently manage the funding?
(31:23):
And it's not the most exciting thing in the world, and it's not rocket science, so we could figure it out. But I think having the platform to already practice this as a senior grad student or young postdoc to be given the autonomy and to be empowered to try this out would be very helpful. The same thing goes for mentoring younger students, although it's not exactly the responsibility of a postdoc to do that or a senior graduate student to do that. I think it's really valuable for us to be better in thinking about science from the big picture to have the opportunity to do that. I was very fortunate that during my PhD, my advisor and co-advisors were extremely supportive and gave us a lot of freedom in running our own projects in training the new students that came in. And I think that really, really helped me when I started my own team. Without that, I think that I would have been much more intimidated by this process.
Steven Thomson (32:23):
Yeah, definitely. They sound like some really good points. Points that'd be very helpful for other early career researchers, I think.
Yvonne Gao (32:29):
This part is really, I think one of the most challenging transitions one has to face in science and it could be very precarious sometimes, and I would really need more support and just more awareness of the challenges that early career scientists face.
Steven Thomson (32:46):
Definitely. On the subject of challenges faced by people in academia, one question that I ask every guest on this podcast is that physics has historically been a field dominated by white cisgender man for a very, very long time, and things are hopefully changing for the better, but there is still clearly a very long way to go before we reach any kind of level playing field. So in your experience, having worked in several different countries, are things changing? Have you seen attitudes to diversity change in the different countries that you've worked in? And now that you're in a leadership position, how do you approach ensuring that your team is as diverse as possible and that they feel welcomed, and that your team is welcoming to as diverse a range of people as possible?
Yvonne Gao (33:31):
Yes. So I think in my personal experience, having studied in different parts of the world, I do think that even just during my educational process, things are getting better, we are having more conversations about this issue, we're having more resources to connect with like-minded people or talk to people who do research on this to learn more about it, talk to policy makers to see what things we could do better. So it is definitely changing for the better. One thing that always holds me back in feeling too optimistic is when I do go for events, dialogues or outreach activities that's targeting the underrepresented populations or to bring awareness about the challenges the underrepresented groups would face, I most often see other underrepresented community members. So I think one deeply rooted and perhaps very misleading concept that we still see a lot in the field that kind of puts me on the ground to tell me that it's not a solved problem yet, is that I think a lot of the majority demographic thinks that the problem we are seeing today is something that is only for the minority groups and should be solved by the minority groups.
(35:00):
So using the women in science example, women in physics example, very often when I do a panel on women in science or an event or talk, I end up talking to a room full of other women, very engaged, extremely educated. We have wonderful conversations, sharing our experiences, encouraging each other to continue to push for changes, but we can't help but wonder at the end always, where are the men, right? Given that we're only perhaps 20% so we can talk all day, but we're still not going to be able to get the momentum that's necessary to make real tangible changes for the field. So I feel like the hardest part of the challenge now is probably what's left to the end for us to figure out how to do, is to reach the people beyond the very engaged and educated, typically underrepresented communities in the field to really mobilize the majority so that we can make real changes.
(36:08):
On the microscopic level, I think for me, what I do in my team to make it as diverse and inclusive is to be very open about it. So we talk about such issues and concerns and the pros and cons of encouraging diversity in one way or the other collectively as a group so everybody would have a chance to contribute the ideas and their experiences as part of their dialogue. I also make very clear and intentional statements about the fact that we want to promote and empower people from different walks of life, different backgrounds, different social and sexual orientations. And I think saying it out loud is important, even though it's not like a real policy change or a word I can give, but just saying it makes people think. And if more of us make statements like this, perhaps we can get more people to think about it and to engage in a conversation. So it takes, I think, an intentional effort to just say, make the statement when it comes to giving a talk, when it comes to nominating my students for awards, or when it comes to just talking to other people about hiring, about academic culture, making a statement definitely helps.
Steven Thomson (37:29):
Yeah, definitely. In fact, that's one thing I noticed when preparing for this interview. I was looking through your group website and you very clearly had a diversity and social justice statement on the website. That's not something I've seen. Sometimes you see this on a job advert, right? You see in small print at the bottom, we welcome minority applicants, but I think I've almost never seen that on someone's group website very clearly right at the top of the page saying, we are a diverse and inclusive environment and we want everyone from all backgrounds.
Yvonne Gao (37:56):
Yeah, I think this is really the outcome of a collective effort by the people in my group. We talked about how we can best make our ideas and hopes known and what kind of words to use that would really reach people and engage with them. And a lot of my students contributed in fine tuning this, in helping me draft that statement. So I'm really glad that it made an impression, and I would really love to see it more, to the extent that it's no longer anything that stands out, right? It's just a normal thing to have. I think that that would be the ideal situation.
Steven Thomson (38:36):
Definitely. Okay. 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?
Yvonne Gao (38:47):
I think it would be to be more bold and explore different things. So I think I've always been quite fortunate in the sense that I knew what I really enjoyed doing and I was quite good at it, so I just kept doing it. But I think as a young…as a teenager or young adult in my late teens or early twenties, it wouldn't have hurt to venture out of this comfort zone a little more and explore some other things. It could really have broadened my scope more. And who knows, it might lead to other interesting adventures beyond what I do now day to day, but I am really, really happy with getting paid and then having the platform to voice my opinions for doing something that I genuinely just find interesting.
Steven Thomson (39:38):
Fantastic. All right. I think that's a great place to end off this episode. So if our audience would like to learn a little bit more about you, is there any way they can find you on the internet, social media, anywhere like that?
Yvonne Gao (39:51):
Yes. So I am on Twitter and my team is also on Twitter at @qcrew_sg, and we also have a group website called quantumcrew.org that has lots of information about the team, about our environment and the research that we do. And also the fun things that we do together, very importantly.
Steven Thomson (40:14):
Great. Well, we'll be sure to lease some links to those on our own website insidequantum.org. Thank you very much, Dr. Yvonne Gao for your time today.
Yvonne Gao (40:22):
Thank you.
Steven Thomson (40:23):
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 podcast. It really helps to get our guests' stories out to as wide an audience as possible. I hope you 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!
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 as usual, I'll be your host for this episode.
(00:17):
In previous episodes, we've talked a little bit about the hardware that might make up future quantum computers, but we haven't gone into much detail about how it works. We've spoken about qubits -- quantum bits -- in quite abstract terms without really describing what a qubit is, what one is made of or how they work. Building robust and reliable qubits is actually a huge challenge, and it's one of the most important things to get right before we can have large scale quantum computers. Today's guest works on solving this critical challenge using superconducting quantum circuits to construct these fundamental building blocks of quantum computing. It's a great pleasure to be joined today by Dr. Yvonne Gao, an assistant professor at the National University of Singapore, and a principal investigator at the Center for Quantum Technologies in Singapore. Hi Yvonne, and thank you for joining us here today.
Yvonne Gao (01:04):
Thank you for having me. It's a pleasure to be here.
Steven Thomson (01:06):
So before we get into the details of how you build superconducting qubits, not to mention your extensive community work, let's first talk about your journey to this point, and let's go right back to the very beginning. What first got you interested in quantum physics?
Yvonne Gao (01:20):
There are two flows to my answer for this. One is the physics part. The quantum came a lot later, but for the physics part...actually in high school, that was the laziest subject for me because I realized I didn't have to memorize a lot of things. If I understood it correctly, I usually can figure out how to answer my questions and do the homeworks. So that was really nice for a 14, 15 year old to not have to memorize pages after pages of definitions. So I think I got lucky with the fact that it was something that lined up with my interest and also I could be quite good at it without putting a lot of brute force hard work into it. So I just kind of went with it and learned more and more along the way. And then the quantum part came when I was an undergraduate in Oxford.
(02:10):
My tutor, actually, he is one of the earliest generations of experimental quantum physicists. He worked with NMR technology, so nuclear magnetic resonance, and he was one of the teams that realized the first two cubic gates on that platform. So through my interactions, I think that's where the interest in quantum physics started to develop because I realized that you could really translate these very abstract concepts like Hamiltonians, electrons that you can't touch or see easily into tangible experiments in the lab and actually make them do the things that you want to do and demonstrate the effects that we've only learned about in textbooks on paper. So I think that's where I really decided that this is something I want to spend a lot more time learning about and get my hands on and tinker with.
Steven Thomson (02:57):
So it was the practical aspect that appealed to you then, and it was the ability to go into the lab and really do experiments on this stuff rather than the theoretical side?
Yvonne Gao (03:05):
Oh, definitely, definitely. I've always loved working with my hands and just take things apart, tinkering with them and making smart changes here and there to see what effects that will lead into, and I definitely enjoy doing that as part of my day-to-day life.
Steven Thomson (03:20):
So then at what point did you decide that you wanted a career working in quantum physics?
Yvonne Gao (03:25):
I didn't really think super far actually. When I was a student, I think I always had this problem of getting bored very easily. So after talking with a lot of my friends who left physics after undergraduate, and I realized that many of the potential career paths can be a little boring, they repeat themselves after a while. You've seen, you get to see a lot of it in the first few years and then it kind of starts to repeat. So I thought research could be really good because we're, at least from what I was told at that age, we're always solving new problems and problems that there are no known or certain answers for. So I thought that could be interesting and probably never gets boring if nobody else, nobody knows the answer. So that's actually how I decided to do a PhD in this field, and I guess I just thought if I do well in it, there's probably a good career path after and I'm really glad things worked out pretty well.
Steven Thomson (04:27):
They have indeed. You've been very successful. Can you give us a quick summary of your career to date and tell us how you got where you are now?
Yvonne Gao (04:36):
Yeah, of course. I did a pretty standard training for a physics student or someone who's interested in knowing more about physics. I did my undergraduate in England in a just standard physics education, and then I went to the US - went to Yale for a PhD in experimental physics focusing specifically on superconducting quantum devices. And since then I actually did a slightly unusual leap for an early career scientist. I didn't get a full post-doctoral training after my PhD. I came back to Singapore - as part of my scholarship commitment, actually - and I joined a national lab and realised that perhaps in a big organization doing more managerial aspects of science was not exactly my cup of tea. So I very quickly applied for the early career fellowships and opportunities in Singapore and thankfully got several of them so that I was able to have all the resources that needed to set up my own group about a year and a little bit after I finished my PhD. So, I always tell my students that I was forced to grow up a bit too quickly to have them manage my own team, setting up the nuts and bolts from scratch. But overall, this experience has been really, really exciting and very interesting, quite a learning journey for me so far.
Steven Thomson (06:04):
Yeah, definitely. That's a very rapid rise. If you weren't doing your current job, what else might you be doing? Are there any other careers that appeal to you along the way?
Yvonne Gao (06:15):
Yeah, here and there. I think after my PhD, I did work kind of in collaboration with a local quantum computing startup called Horizon Quantum, and I really enjoyed being in a small dynamic team working on the latest technologies. It's still research, just not academic research. So that could have been something that I would very much enjoy pursuing if I had not come back to academia and started a research team. I do have to say, I think one thing that put me back was the fact that in academia we are, we have more of the luxury of doing curiosity led work. So I could take an intellectual detour with my team that, oh, that could be interesting. Let's just look into it for a couple of months without worrying too much about what do I have to deliver at the end of it? Do I have to live up to a promise? The conclusion or the outcome of these small investigations could be simply, well, we're learning something interesting out of it and it doesn't have to be something super tangible. And I really like that about academic research.
Steven Thomson (07:30):
Yeah, that's something I hear a lot, that you have the freedom to pursue these little ideas, these kind of mad ideas that might not go anywhere in a commercial context, but yes, you still get to chase them up and learn something.
Yvonne Gao (07:41):
Definitely. And I think it's through these little experiences where we might very well end up not achieving a tangible result that we actually gather the most useful feedback because then we learn why it didn't work out and how we have failed in a particular attempt. And it really all goes back into making our primary focus and primary experiments work out much, much more rapidly and more smoothly.
Steven Thomson (08:04):
It's nice to hear you say that because people often don't acknowledge that failure can be a learning experience. You can learn a lot more from failure sometimes than you can learn from success.
Yvonne Gao (08:13):
Absolutely.
Steven Thomson (08:14):
Yeah, we see publications, we see successful flashy experiments and results a lot of the time, but people don't often talk about all the failures that they had to go through to achieve that kind of insight.
Yvonne Gao (08:26):
No, definitely. And I think it's something that I think now that I'm on the other side, we're mentoring students that we should be more actively encouraging our students and our community to do because we failed 10...especially for experiments, we fail like 10, 20 times before we get the hero device and everything lined up to make these beautiful experiments. And it's really through these prior attempts that we learn all the useful knowledge to put everything together. And I think the goal is there, and one trick I always tell my students to do is if something they're interested in learning about is a publication in one of the top journals, most likely the paper itself will not have too much information because they're so short. So the most important thing to do is to go figure out who the authors are, who is a PhD student who's about to graduate, and then read their thesis because that's where all those extra attempts and frustrations and the really valuable information about the mistakes they've made are documented and that's what helps us the most.
Steven Thomson (09:33):
That's really good advice. Yeah.
Yvonne Gao (09:36):
It's a life hack that probably shouldn't need to be there, but the way we publish unfortunately pushes us to do that a little bit.
Steven Thomson (09:48):
Yeah, definitely. So then talking about your research, can you give us a bit of an overview? What's the big picture goal of the field that you work in and where does your work fit into this big picture?
Yvonne Gao (09:59):
Yeah, definitely. So the whole field of superconducting quantum circuits actually has really evolved in the past two decades or so into this wide spectrum of building hardware and theory to look at on one hand very fundamental signs of entanglement, of decoherence, of condensed matter physics, and on the other hand, the extreme other end of building scalable quantum hardware for quantum computation. So the field uses these very bespoke kind of quantum devices that we can engineer, we can fabricate, and we can more or less tailor the parameters with the intention, with the intended target applications in mind. And our work now takes advantage of these engineered devices, these very tunable architectures of our hardware to study both the interesting fundamental science aspects as well as to think about perhaps the useful things related to information processing. More specifically, we are looking to not using qubits, but possibly continuous variables, so large Hilbert space involved in bosonic elements or sometimes we think about them as a potential to encode q-dits, so multi-dimensional qubits.
(11:30):
So how we fit in is basically looking into this area of quantum information processing that's realistic in a world where we have noise and decoherence effects. So we have to encode the information that's actually robust to these realistic errors. So in theory, I think we can do these beautiful devices with very quantum correction codes to make sure they're efficient. But in practice we very often have to fight against local noise, such as just losing some energy to the environment. And to do this in practice requires a lot of hardware overhead typically. And what we're looking into is something that offers the potential to be a little bit more efficient and making the experimental list life a little easier so that we can use fewer hardware pieces and still encode information in a way that has the capacity and the complexity to eventually do quantum computing. It was somewhat a long-winded answer, I think.
Steven Thomson (12:32):
No, that was great. So I think you touched on that a little bit there, but what's the biggest challenge in your field at the moment?
Yvonne Gao (12:43):
Yeah, that's a really good question. I think there are several. I think on the more field specific point of view, the quantum error correction aspect is definitely one of the most important challenges we're trying to solve as a field, both in the more discrete variables, the more textbook like examples of using qubits, how do we make quantum error correction codes out of them, as well as in the continuous variable versions where we use the bosonic elements and try to think about more creative ways of encoding information that takes advantage of the symmetry properties in our bosonic quantum elements. But perhaps from…as an experimentalist, something closer to my heart and more practical is the challenge of making things more reliable and reproducible. So at a moment, a lot of us actually have the ability to make very good devices, but making many good devices is quite difficult.
(13:44):
So we can make small things maybe once or twice. Good things, good devices that gave us beautiful experiments. But if I want to reliably make that over and over again for all the experiments that I have in mind and do that on demand, that becomes quite tricky. And that's because there is a lot of variability in the way we put the material on the substrate, the way we design the devices and the way the process works in cleaning...all the chemical processes that's involved in cleaning and making these devices. So I think these variabilities at the moment are one of the hardest challenges to resolve really from the experimental point of view because we can't just rely on hero devices to make one or two really wonderful experiments. We actually need them to really be made on demand and always have similar performance, performance values. So that's something that I would love to see more efforts go into and to learn more from the community out there.
Steven Thomson (14:49):
Why are they still so difficult to produce? Is it just because these are very new technologies and the processes and the pathways are still being established?
Yvonne Gao (15:01):
Yes. I think this...if you ask different people in the field, we're going to have very different answers. For me, I think one of the reasons is because these have been mostly made by physicists, not engineers and material scientists or chemists. For physicists, we think about this in a way that's very analytical. So I think, okay, this is the process. This is a pattern, this should give me the desired result. And sometimes we get there and we're happy with it because we can do the next step using whatever we have. So that's kind of the beauty of the platform. We don't have to be perfect to do interesting things. So that's nice. And for many years we've been doing that, but to really pin down the processes and little material quirks, we need the help of chemists, material scientists, process engineers, fabrication specialists, and I think that's just starting now. In the past handful of years or so, we are having these more collaborative approaches to really listen to other experts on how to make this in a more consistent way that is actually reproducible.
Steven Thomson (16:11):
Great. So it's a really, a very interdisciplinary challenge then, something that involves a lot of different expertise from different fields.
Yvonne Gao (16:16):
Absolutely. I think we have to learn a lot from our colleagues across the different areas because some of the challenges we're trying to solve now, like cleaning a surface, this has been studied by material scientists for a long time. It's just we've not talked to them and they've not talked to us. And I think a lot of this knowledge is there. We just haven't connected the dots yet.
Steven Thomson (16:40):
How do you go about breaking down the barriers between these communities then? How do you find out that material scientists already know how to solve these challenges?
Yvonne Gao (16:51):
Yeah, that's a question we ask ourselves all the time because it's really not easy. One reason why it's really difficult is because we speak very different languages. We call the same thing very different names. So when we speak to another colleague in a different field, it's almost like we need a translator in between. So I think personally, my way of doing this is to just be very proactive and go with a very open mind. I just basically go with the intention of me not knowing anything. I just ask them to tell me their research as if they were explaining to an undergraduate. And I think that really, really helps. Another way that I've been getting some help doing this is my team is very diverse. I have students from material science background, from CS background, from electrical engineering background instead of just a traditional physics training. And they actually bring in these contexts, these information...the literatures that they went through in their Master's studies are actually really helpful for us to learn and read about. So I think the diversity in the quantum computing and the superconducting circuit field is starting to help resolve this issue in an organic way.
Steven Thomson (18:10):
Fantastic. So if I were to try to summarize your research in a single very oversimplified phrase, I might pick superconducting quantum qubits. [Editor's note: The word 'qubit' already contains 'quantum' - it’s a ‘quantum bit’. These should more properly just be called 'superconducting qubits'. Steven had only just had his morning coffee when recording this episode and clearly it hadn't kicked in yet...!]
(18:21):
Can you break this phrase down for us? What does it mean? What are superconducting quantum qubits?
Yvonne Gao (18:26):
Yes, I would try that. So there are a few elements to it. One is we can start backwards with qubits, right? Qubits are this contrived and rather abstract definition of a quantum bit of information. And what that means is it can be any conceptually viable definition of something that can be in superposition, right? Superposition, just meaning being in two orthogonal states at the same time, or two clearly distinctive states at the same time. So this is all very, very abstract. So anything can be a qubit if it could follow the definitions of...if it follows the behaviors of superposition and eventually entanglement, et cetera. So how the superconducting part comes in is to narrow it down to one particular hardware. So why is this superconducting? That's because we are building qubits out of electrical circuits, and normally electrical circuits would necessarily have some losses because there is friction, there is resistance, and the way to remove that is to bring everything to a stage where we can conduct electricity, we can conduct current without experiencing any friction or any losses. So ideally this can be achieved through superconductors, which are by definition able to pass current without any dissipation. So that's why we're building these electrical circuits using superconducting materials and by cooling them down to these superconducting states. Our goal is to, well, our hope at least is to remove as much of the dissipation and noise as possible from our system so that we can really narrow down and zoom in on the very small quantum effects that's present in the hardware.
Steven Thomson (20:24):
It feels like building qubits from electrical circuits is almost closer in spirit to existing classical computers as compared to other methods like trapped ion setups, for example, where it feels like the technology is so different to what we've had before that a lot of things still need to be developed. It's kind of appealing that you're still using electrical circuits, which I guess we understand well, but you're using them in this kind of quantum realm to do something new in something interesting.
Yvonne Gao (20:52):
Yes, yes, that's exactly right. I think the way we're taught quantum mechanics is usually through a single electron or a single atom. So that makes the frameworks of some of these other platforms very intuitive for a very traditionally trained physicist. But when it comes to building the hardware for quantum computers, I agree with you that quantum circuits are actually more intuitive when we talk to engineers, when we talk to classical computer scientists because they can find direct analogs almost to what they do. So for instance, we use capacitors and inductors just as they would do in classical computing circuits. We also use nonlinear inductors, which effectively are some sort of diodes in the classical world, or switches. So in that sense, there are counterparts that we can very easily find between the classical and quantum circuits, and that definitely helps when we talk to people who have classical CS background and to explain these things across.
Steven Thomson (21:57):
I see. Okay. So why are superconducting qubits such a promising candidate for future quantum computers as compared to these other technologies? Okay, it's nice that they are more intuitive, I guess, to people from maybe computer science or engineering backgrounds, but do they have any fundamental advantages over trap ions, for example, or any of these other candidate technologies?
Yvonne Gao (22:20):
I think this is very much a personal opinion. At the moment, I think superconducting circuits are probably one of the most mature platforms. The reason why it's been very appealing to get to the stage where we have hundreds of qubits in commercially available platforms is because of this ability to engineer the device. So we can just print classical circuits, we can print quantum circuits in a way that's very tailored to what we want to show with it. So this gives us a lot of ability to put them in different regimes on the same hardware. You can have parts that are strongly interacting and parts that are weakly interacting, and they can serve very different purposes. Some are to store information, so more like the memory elements. Some could be for processing information, doing fast gates, for example, to turn on and off the logic.
(23:13):
And some could be for just extraction of information, so for the measurement. So the different parts can be designed with different purposes in mind and all go on the same hardware, in the same fabrication process. So this is very appealing and I think it gave us the first step into really making these larger intermediate scale devices where we can learn about the behaviors of near term quantum processors. And to be honest, going forward, if I have to envision what a quantum computer may look like, it might not be made out of superconducting circuits. It could be a superconducting devices as well as something else, or it could be something that we haven't even thought of today. But what I would say really is the key value at the moment for superconducting devices is it gives us a fairly accessible hardware with our existing engineering capabilities to reach this hundreds of qubits regime where we can no longer really simulate these devices classically. So we just have to build it to learn how it will fail, what problems we might run into, what kind of new physics does it require us to understand to be able to scale up further. So it really provides this very nice test bed for us to understand and troubleshoot and debug to optimize for the next version that may look completely different from what we see now.
Steven Thomson (24:38):
I see. So because the technology is quite mature, you can build these large systems that theorists struggle to simulate. So you really are in the wild west, you're doing something completely new that is quite unknown.
Yvonne Gao (24:51):
Yes, and I think that's also the only way for us to learn the failure modes because we can't dream up how when 50 or a hundred qubits interact with each other, what kind of weird coupling there might be. These are the things you just no longer can know a priori. You just have to measure it and figure it out.
Steven Thomson (25:07):
I see. So a lot of the people that we've spoken to on this podcast so far have been theorists and over the last few years with the Coronavirus pandemic and so on and lockdowns and travel bans, I think it's probably been easier to be a theorist because we've been able to do a lot of work still from home. What has the impact of the pandemic been on your group's research activities? Because I guess you started your group either just before the pandemic or perhaps even after it started, so that must have been very disruptive for you.
Yvonne Gao (25:38):
Yeah, I started right with the pandemic actually. So I started in March 2020 and we started having issues in April. So I had a month in my office and I had to go home. Fortunately for me, the impact was not too pronounced because in the beginning there was a lot of purchasing and setting up to do that didn't require me to be physically in the lab. So it took me the first three to four months of placing the orders, checking out all the necessary infrastructure aspects so I could do a lot of this remotely during the lockdown. What did affect us a little bit was the admission of our students and the hiring of the postdocs. I had a really good run of meeting the people, finding out the people were interested, having good discussions with them, and many of them were actually delayed in the process of just getting into the country.
(26:43):
So I was very fortunate that they stuck with me and then decided to wait it out and then came in when the situation stabilized more. But I can imagine that could be an issue for a lot of experimentalist teams where you do need people hands on deck and setting things up, but just people cannot come in and without a team it would not be possible to set things up in the lab. So it worked out pretty well for us, but that had been quite a big challenge for a lot of my friends and other early career colleagues in the same period.
Steven Thomson (27:17):
I can imagine that particularly early career postdocs, for example, who don't have tenure and who are depending on publications and they have to get these publications before their funding runs out, that must have been a very difficult period for them.
Yvonne Gao (27:29):
Yeah, absolutely. I think a lot of people had to, I think that this is sometimes...perhaps going back a little bit to how we run experiments, some groups that suffered a little less had their experiments automated more, and I think that pushed people to be more thoughtful in setting it up, which is I guess a silver lining. But at the same time, I do think that people...experimentalists definitely experienced a lot of setbacks in this period, just physically because they were not able to go in to change their setup or interact with the team in person to make decisions on certain big experimental changes. And in Singapore, something that was quite nice that was done was they recognized and extended many early career scientists contracts and delayed the funding deadlines by about a year for most people to address this issue that at this stage where you are the most vulnerable in terms of having this six months or one year to make up for last time. And I think that was much appreciated amounts to people around the same age group.
Steven Thomson (28:49):
So talking of the challenges faced by early career researchers, as we mentioned already, you've had a very rapid rise to your current assistant professor position, and along the way you won the Young Scientist Award in 2021 from the Singapore National Academy of Science, but were there any unexpected challenges that you faced on your journey to becoming a PI and are there any ways that universities and research agencies could better support early career researchers during this difficult period?
Yvonne Gao (29:17):
Yes, definitely. I think for me, there were two aspects. One was the fact that a lot of these awards and grants and fellowships for early career scientists, they bunch too much together. So it's almost like all the different granting agencies are waiting for the other one to first make the move. Once I got my first grant, the next two or three were much, much easier because very often they ask you to justify your experience in managing a grant before they gave you a grant. And if you were just a PhD student or a young postdoc in certain groups, you just don't have this kind of opportunity to do that, and therefore it becomes a chicken and egg problem. So I think there needs to be some structural changes or at least recognitions of the fact that very often at the early career stages, depending on the country we were in, the environment of the group we were in, we may not have the same kind of opportunities when it comes to managing funding or leading research efforts.
(30:21):
And although they're important, we should be evaluating candidates more holistically instead of just looking at how many grants you've had before or how many PhD students you've helped mentor, because these hard metrics could really be very different depending on the environment where we are trained, where we are mentored. Another aspect is that I think from the other side of the aisle for PhD advisors or post-doctoral advisors, I think we really need to look into not just equipping our people with the scientific skills, but also some management skills. Because when I started my own team, I realized that nobody's ever taught me how to do proper accounting, and suddenly I have millions of dollars that I have to manage, and it's taking a lot of time, and a lot of us in this position have to go read help books, how do you efficiently manage the funding?
(31:23):
And it's not the most exciting thing in the world, and it's not rocket science, so we could figure it out. But I think having the platform to already practice this as a senior grad student or young postdoc to be given the autonomy and to be empowered to try this out would be very helpful. The same thing goes for mentoring younger students, although it's not exactly the responsibility of a postdoc to do that or a senior graduate student to do that. I think it's really valuable for us to be better in thinking about science from the big picture to have the opportunity to do that. I was very fortunate that during my PhD, my advisor and co-advisors were extremely supportive and gave us a lot of freedom in running our own projects in training the new students that came in. And I think that really, really helped me when I started my own team. Without that, I think that I would have been much more intimidated by this process.
Steven Thomson (32:23):
Yeah, definitely. They sound like some really good points. Points that'd be very helpful for other early career researchers, I think.
Yvonne Gao (32:29):
This part is really, I think one of the most challenging transitions one has to face in science and it could be very precarious sometimes, and I would really need more support and just more awareness of the challenges that early career scientists face.
Steven Thomson (32:46):
Definitely. On the subject of challenges faced by people in academia, one question that I ask every guest on this podcast is that physics has historically been a field dominated by white cisgender man for a very, very long time, and things are hopefully changing for the better, but there is still clearly a very long way to go before we reach any kind of level playing field. So in your experience, having worked in several different countries, are things changing? Have you seen attitudes to diversity change in the different countries that you've worked in? And now that you're in a leadership position, how do you approach ensuring that your team is as diverse as possible and that they feel welcomed, and that your team is welcoming to as diverse a range of people as possible?
Yvonne Gao (33:31):
Yes. So I think in my personal experience, having studied in different parts of the world, I do think that even just during my educational process, things are getting better, we are having more conversations about this issue, we're having more resources to connect with like-minded people or talk to people who do research on this to learn more about it, talk to policy makers to see what things we could do better. So it is definitely changing for the better. One thing that always holds me back in feeling too optimistic is when I do go for events, dialogues or outreach activities that's targeting the underrepresented populations or to bring awareness about the challenges the underrepresented groups would face, I most often see other underrepresented community members. So I think one deeply rooted and perhaps very misleading concept that we still see a lot in the field that kind of puts me on the ground to tell me that it's not a solved problem yet, is that I think a lot of the majority demographic thinks that the problem we are seeing today is something that is only for the minority groups and should be solved by the minority groups.
(35:00):
So using the women in science example, women in physics example, very often when I do a panel on women in science or an event or talk, I end up talking to a room full of other women, very engaged, extremely educated. We have wonderful conversations, sharing our experiences, encouraging each other to continue to push for changes, but we can't help but wonder at the end always, where are the men, right? Given that we're only perhaps 20% so we can talk all day, but we're still not going to be able to get the momentum that's necessary to make real tangible changes for the field. So I feel like the hardest part of the challenge now is probably what's left to the end for us to figure out how to do, is to reach the people beyond the very engaged and educated, typically underrepresented communities in the field to really mobilize the majority so that we can make real changes.
(36:08):
On the microscopic level, I think for me, what I do in my team to make it as diverse and inclusive is to be very open about it. So we talk about such issues and concerns and the pros and cons of encouraging diversity in one way or the other collectively as a group so everybody would have a chance to contribute the ideas and their experiences as part of their dialogue. I also make very clear and intentional statements about the fact that we want to promote and empower people from different walks of life, different backgrounds, different social and sexual orientations. And I think saying it out loud is important, even though it's not like a real policy change or a word I can give, but just saying it makes people think. And if more of us make statements like this, perhaps we can get more people to think about it and to engage in a conversation. So it takes, I think, an intentional effort to just say, make the statement when it comes to giving a talk, when it comes to nominating my students for awards, or when it comes to just talking to other people about hiring, about academic culture, making a statement definitely helps.
Steven Thomson (37:29):
Yeah, definitely. In fact, that's one thing I noticed when preparing for this interview. I was looking through your group website and you very clearly had a diversity and social justice statement on the website. That's not something I've seen. Sometimes you see this on a job advert, right? You see in small print at the bottom, we welcome minority applicants, but I think I've almost never seen that on someone's group website very clearly right at the top of the page saying, we are a diverse and inclusive environment and we want everyone from all backgrounds.
Yvonne Gao (37:56):
Yeah, I think this is really the outcome of a collective effort by the people in my group. We talked about how we can best make our ideas and hopes known and what kind of words to use that would really reach people and engage with them. And a lot of my students contributed in fine tuning this, in helping me draft that statement. So I'm really glad that it made an impression, and I would really love to see it more, to the extent that it's no longer anything that stands out, right? It's just a normal thing to have. I think that that would be the ideal situation.
Steven Thomson (38:36):
Definitely. Okay. 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?
Yvonne Gao (38:47):
I think it would be to be more bold and explore different things. So I think I've always been quite fortunate in the sense that I knew what I really enjoyed doing and I was quite good at it, so I just kept doing it. But I think as a young…as a teenager or young adult in my late teens or early twenties, it wouldn't have hurt to venture out of this comfort zone a little more and explore some other things. It could really have broadened my scope more. And who knows, it might lead to other interesting adventures beyond what I do now day to day, but I am really, really happy with getting paid and then having the platform to voice my opinions for doing something that I genuinely just find interesting.
Steven Thomson (39:38):
Fantastic. All right. I think that's a great place to end off this episode. So if our audience would like to learn a little bit more about you, is there any way they can find you on the internet, social media, anywhere like that?
Yvonne Gao (39:51):
Yes. So I am on Twitter and my team is also on Twitter at @qcrew_sg, and we also have a group website called quantumcrew.org that has lots of information about the team, about our environment and the research that we do. And also the fun things that we do together, very importantly.
Steven Thomson (40:14):
Great. Well, we'll be sure to lease some links to those on our own website insidequantum.org. Thank you very much, Dr. Yvonne Gao for your time today.
Yvonne Gao (40:22):
Thank you.
Steven Thomson (40:23):
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 podcast. It really helps to get our guests' stories out to as wide an audience as possible. I hope you 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!
