It is well past 2:00 am in Mumbai and I have just wrapped up a virtual club meeting with some of my peers in the US. Late nights like this have been the norm over the past month that I’ve been back at home in India—I have been operating in a diffuse time-zone between Mumbai, New Haven, Chicago, and California, as I attempt to work remotely on research, college club activities, and other projects. As a college student entering my final year at Yale, the idea of living at home with my parents is not quite what I had planned for the summer; but these days plans change quite quickly.
Like most people, I did not anticipate that we would have a global pandemic in 2020. For me, the first real inkling came on a Saturday night in late February. It was well past 9:00 pm in New Haven and I had only just started packing a suitcase for my 6:00 am flight the next morning. I was eagerly looking forward to skipping the next week of classes at Yale to travel to Denver, Colorado, where I was set to present my undergraduate research at an international physics conference hosted by the American Physical Society (APS). By 10:00 pm, I had just about finished with the suitcase when I checked my phone to find a flurry of forwarded emails—“URGENT: Due to rapidly escalating health concerns relating to the spread of the coronavirus disease (COVID-19), the 2020 APS March Meeting in Denver, CO, has been cancelled.”
In the days that followed, events and spring break plans fell in quick succession like a series of dominos. Over the “break,” I myself had planned to attend an IBM quantum computing hackathon and then study for the GRE. Both got cancelled. Soon afterwards—somewhat ironically, on the Ides of March—Yale sent its students home, shifted classes online, and closed down research labs, including my own lab. Of course, this was simultaneously happening at all universities and workplaces, and with that came the unprecedented economic downturn that has since come to define the narrative of the pandemic. The effect on STEM (science, technology, engineering and mathematics) research was twofold: firstly, it led to budget uncertainties about funding and the government had to step in to support colleges and universities; even Yale, with its multi-billion-dollar endowment, has announced budget cuts this year. But secondly, the lockdown and switch towards remote work have affected many labs and projects across the world that require researchers to be physically present. Slightly closer to home, several of my own friends and classmates, whose summer research involved lab work, were forced to reconsider their plans.
It’s within the larger context above that I raise a curious point. Despite budget cuts all-around, there are three STEM subfields in particular that seem to have remained largely unaffected: high-performance computing (HPC), artificial intelligence and machine learning (AI/ML), and my own field of research, quantum information science (QIS). These are what you could call hot topics at the moment: with large amounts of funding coming from academia, industry, and the US government, it seems all but certain that these fields will safely make it through the pandemic. Of course, there were adjustments to be made—research activities had to be moved online over the last few months. Still, this shift feels less extreme, and in at least one quantum computing lab at the University of Maryland—which already had remote/autonomous measurement capability—it even led to “the best data [...] ever seen”. In my case, I had planned to work on an experimental project this summer, but when that was no longer possible, I elected to focus on theoretical aspects of the project instead. Likewise, friends whose summer research or internships involved coding or simulation (in any capacity) have also been able to continue largely unabated—these are jobs that can easily shift to being remote.
Thus, for a variety of reasons, the fields that are best suited to getting through the pandemic and this era of remote work, are also the ones which have the most support at the moment. And yet, for all this interest, a lot of people are not aware exactly what these frontier technologies are. Thus, in this article, I am going to tell you a little bit more about my own field of quantum information, and my experience working in this space.
What is Quantum Computing?
When Prime Minister Narendra Modi famously emphasised the need for a “quantum jump” in the Indian economy, ‘quantum’ was meant to be synonymous with ‘large’. But the word quantum has usually always referred to things that are quite small. Within physics, quantum mechanics is the study of electrons, atoms, and light—as well as their interactions—at the smallest scales. The rules that govern these objects are quite different from the laws of physics that we are generally familiar with. Particles can exist in two different states “at once” and can exhibit bizarre correlations and other quantum effects. And yet, this theory explains how atoms and molecules interact at the lowest level—the rest of physics, chemistry, and biology then follows!
Now, it turns out that simulating quantum systems on a computer today is notoriously difficult. Even the world's most advanced supercomputers cannot simulate a simple caffeine molecule in your morning cup of coffee!
In 1980, physicist Richard Feynman posed the question of whether it would be possible to build a computer out of individual atoms. Such a computer would obey the laws of quantum mechanics, and could thus more easily simulate other quantum systems. This is an incredibly powerful idea: by modelling how molecules interact, such a computer could—in principle—speed up the process of drug discovery, or even help develop personalised medicine that is optimised for your biochemistry at a molecular level. It would also help answer more fundamental physics research questions.
Soon after Feynman, the computer scientists came in and showed that such a quantum computer could be used to speed up many known algorithms: by exploiting the fact that quantum particles can exist (in a crude sense) in “many states” at once, these devices have access to a massive amount of parallel computing power that is unreachable with today’s classical computers. Current conventional computers are built from bits—a string of 1s and 0s that encode how the information is stored. Every file or photo you have saved, every word you type, every operation you do on a computer ultimately gets stored and processed as a sequence of bits. Now, by contrast, quantum computers have quantum bits—or qubits—that obey different laws of physics; they are constructed, for example, by individual atoms or spins. This allows information to be encoded in a much more complex way, and as such, makes quantum computation a fundamentally new type of computing.
In the last 20 years, these devices have gone from concept to reality—small quantum computers are routinely being built today. Likewise, on the applications side, it is now believed that these devices will be useful in solving hard computational problems in quantum physics and chemistry (e.g. simulating quantum systems and designing new materials), medicine (e.g. drug discovery), computer science (e.g. searching databases), and mathematical optimisation. This last area will lend itself to applications in machine learning, finance and portfolio optimisation, and more. In a way, it is difficult to predict all of the use cases—they will likely emerge as we keep building. Indeed, some of my professors at Yale have called our times “the second quantum revolution.” (The first revolution was the initial discovery of the theory of quantum mechanics in the 1930s, which led to the development of lasers, MRI machines, semiconductor devices and the transistor—i.e. the basis for all phones, computers, and electronics—among other things!)
My Journey in the Field
I first heard about this new field of quantum after reading popular science articles and watching YouTube videos in grades 8 and 9. I was instantly hooked. It seemed like an incredibly promising area to get into that combined my two big interests of physics and computer science. Nowadays, there is a lot of opportunity: I was able to learn the necessary math background via freely available quantum mechanics lectures online. Later, after finishing my 10th grade exams, I got a chance to join a quantum computing lab at the Tata Institute of Fundamental Research, in Mumbai, where I learned even more and got to work on an independent research project over two years while in school. By the time I graduated from high school, I had already decided that I would pursue a career in QIS.
As an undergraduate, I have been very fortunate. Yale is an epicenter at the forefront of quantum research in the world, and as such many of my professors are also some of the biggest names in the field. Over the last three years, I have worked on several research projects in experiment and applied theory at different physics labs and at the Yale Quantum Institute. These experiences also helped me get an internship at Rigetti Quantum Computing, one of the leading startups in this space, for a summer, and then part-time all through my third year, which I did while simultaneously balancing research and classes at Yale. I have also attended several workshops, conferences, and hackathons, all related to quantum—and so have gotten the chance to really dive deep into this upcoming field that I love. It’s exciting and fast-paced: there are new developments and advances each year, and I am glad to be able to play some small part.
As it turns out, I am not the only person that has developed an interest in quantum over the last few years. Large companies like IBM, Google, Microsoft, Honeywell, and Intel all have large labs dedicated to making this frontier technology a reality. Other firms like Amazon as well as several VCs are also investing heavily. And like Rigetti, where I worked, there are many hardware and software-based startups developing different aspects of this ecosystem. The promise of quantum-related advances in computing, sensing, secure communication networks and new materials have also brought in big money from the US government through a $1.2 billion National Quantum Initiative. The governments in China, the EU, and India—it appears—have also launched similar national funding initiatives.
In a way, the hype and the buzz today seem reminiscent of the atmosphere in the early 1990s when personal computers and the internet were coming up—at least from what I’ve heard from those that were there. My mother, in particular, was in the semiconductor industry for a time and even helped to develop and patent the fabrication technology for Intel’s early Pentium chips. According to her, there are similarities between our time now and hers then.
Hype versus Hope
Despite the numerous applications of quantum computing technology, it is important that we not be swayed too far by hype. QIS certainly has the potential to transform society—and has now transitioned from a neat idea to a very real, tangible technology. IBM even gives free access to run programs on their quantum computers via the cloud! This progress and promise has guided my decision to pursue a research career in this space—I intend to now do a PhD after I finish my undergrad degree, and then work across academia and industry. Many of my friends and colleagues are doing the same. That said, large-scale commercial quantum technologies will only fully develop in the next 5-10 years or more (I personally bet on “more”). So, it’s not something that will give you a return on investment tomorrow, per se. Building a fault-tolerant quantum computer is an incredibly difficult engineering/physics challenge, as is theoretical work needed to develop useful algorithms for these devices. And likewise, although quantum computers will have a major impact on medicine and drug/vaccine design, they won’t be immediately useful for addressing COVID-19, as some articles have incorrectly suggested. Again, these applications will require time to mature.
Over-hyped claims like the one above connect back to a larger issue in the field, and one that I am personally quite invested in. For this quantum computing ecosystem to flourish, it will need talented researchers and students entering the field. Sure, this means more quantum physicists and computer scientists, but also mathematicians, software programmers, hardware and electrical engineers, web interface developers, business people, and a whole lot of others that are familiar enough with the technology to then help build applications and use-cases for other fields. Moreover, there is a crucial issue of ensuring that the field is diverse and that all students, regardless of background, have access and the opportunity to learn.
My own interest in quantum education and outreach started during my second year of college. One of my professors had been invited to a meeting at IBM about teaching quantum computing at colleges. He couldn’t attend and nominated me to go instead—I was a teaching assistant for his class. On that late January afternoon, I travelled to IBM’s T.J. Watson research facility in the middle of a snowy forest in upstate New York to attend the meeting. When I arrived, I was ushered into a boardroom and, to my surprise, seated opposite professors, IBM senior research staff, and even the VP of IBM Quantum. Thankfully, I had the sense to wear a nice button-down shirt! The meeting was to brainstorm IBM’s plans to develop an open-source quantum computing curriculum. Everyone emphasised how important it was to make this information accessible! Thanks to my own experience learning the subject using online resources as a student, I was able to contribute to the meeting and even suggested a few ideas—though I was initially mistaken for a professor, despite being only a 19-year-old undergrad at the time!
This experience really got me thinking about the value of developing good educational resources for quantum. If QIS really is to have a transformative effect, we’ll need to make sure that people can keep up and develop the skills needed to join the workforce—and make it inclusive, diverse, and equitable for all. IBM has done an incredible job with this. In the year since that meeting, their team has spearheaded the creation of an open-source “Qiskit textbook”, which is a great free resource for getting started in quantum. They also hosted a Global Summer School online, with 5,000 attendees. It is initiatives like these that will help bring students into the fold. I myself have started working with a team of MIT undergraduate and graduate students to develop a year-long quantum computing course for The Coding School, in partnership with IBM. The Coding School is a non-profit dedicated to equipping the next generation of students from all backgrounds with the coding skills needed to thrive in the 21st century.
A New Era of Technology and Science
Even during COVID-19, we have seen steady interest in subfields like quantum information, AI/ML, and high-performance computing—all areas of frontier science that are being pursued both in academia and commercially in industry. Though I did not focus on the latter two here, they are also immensely important and will change society in very deep ways. Like with quantum, we need to ensure people have the skills and training to meaningfully engage with these technologies. A slight difference, though, is that artificial intelligence is here today and can be used to tackle some of the world's most challenging problems—among them, the coronavirus.
It’s important to remember, however, that while these frontier fields are hot topics today, this was not always the case. Machine learning started as an academic research pursuit over 50 years ago and only relatively recently became a commercial technology. This reminds me of a saying that I quite like: that science and technology follow at each other’s feet in lockstep. Only by investing in the scientific research can we reap the benefits of new technology that emerges in the process. Likewise, advances in technology can themselves lead to new discoveries in science. Quantum computing currently resides between these two spheres of science and technology—there is scientific work to be done, but also the underpinnings of commercialisation as the field transitions out of the lab. High-performance computing and AI/ML are slightly further along in this process. And while it’s great that these few commercial and pre-commercial areas are in the spotlight at the moment, we also need to make sure that basic scientific research is given the support it needs too—especially as we continue through the pandemic.
Amrita Chowdhury: “How do we consider the ROI on science and tech? And develop the resilience to stay the course?”
Amrita has been closely involved with tech. She started out in breakthrough innovation in Silicon Valley, and is today an entrepreneur in the Deep Tech space in India after a few detours along the way.
Listen to her take, drawing on her own experiences and Shoumik's.
Highlights from her 9-minute audiogram:
"Science and tech are being called upon to deliver creative solutions that impact humanity. But there are four important points to consider," she says.
1. How do companies and countries take long bets on certain technologies and what lasting impact does that have on those industries and on a country's competitive edge?
2. How do we consider return on investment in science and technology? And how do we develop the resilience to stay the course? Because technology has a long gestation period before it becomes transformative. And there will be multiple dead ends along the way.
3. How do we reimagine how science and technology is taught? Our education system teaches us to be consumers of tech, not creators of tech. We need lateral thinkers.
4. How do we communicate the role of science and technology to retain young talent in the field?
Bookmark the series: A show every Saturday at 7.30 pm on Facebook Live, supported by a column + audiogram.
And join Shoumik and Amrita Chowdhury on Facebook Live on Saturday August 29, at 7.30 pm IST, for Episode 6 of Talkin’ ‘Bout My Generation.
Still curious? Read N Dayasindhu's story on how grand challenges that bring together government, universities and industry, can solve near future problems using technologies innovatively. And why Indian technology projects haven’t made global impact.