In 2017, while in line for a coffee, I fell into a conversation with a geographer who was particularly excited about quantum computing. I was in Toronto at the time, attending the Congress for the Humanities and Social Sciences, a meta-conference where these sorts of random connections often occur. The geographer explained that his wife, also an academic, was working on a particular problem to do with quantum computing. The details of the problem were completely lost on a guy who spent his time researching spooky literature and since then I have completely forgotten what this problem was.
What I do remember, though, was the sheer enthusiasm this geography researcher had for the coming technological shift. At the time, he was in the midst of applying to Facebook to use their data for a demographics project. He told me that the volume of data and the speed at which it was being produced outstripped his ability to process, let alone to do the kinds of population simulations that he hoped to do. He would have to significantly limit his scope. Quantum computing, he told me, would not be so limited. And its analysis would differ not only in scope but in kind. The quantum computing he hoped for – the concept he had seen glimmers of in his wife’s research – would not just analyze connections; it would analyze “ecologies” – the dense web of relations, complications, and multi-nodal nuances that comprise truly complex systems. The nature of quantum computing’s qubits, its ability to hold parameters in multiple states or possibilities at once, lay at the heart of this difference in application. I came away from that conversation with only the sketchiest understanding of quantum computing. What stuck in my mind was the geographer’s phrase: “analyze ecologies.” The example presented by Kietzmann et al. in which a traditional and a quantum computer search for a single Xed out page in a library feels similar to this concept. It speaks to a radical shift in how our technology consumes and develops the volumes of data that we discussed last week.
The article positions quantum computing as a possible tool for mapping and analyzing vastly complicated problems like climate change. Pairing it with the two interviews we listened this week, suggests a parallel use. These interviews demonstrate the extraordinary complexity of apparently simple and known industries. Emily Bobis’ interview points to the benefits of all the latent data “experienced” by a car on a road. The enormous volume of this data and the velocity at which it is generated leaves us with an archive, or lake, that often goes largely unutilized. By analyzing this data, by carefully attending to the immediacy of cars’ measurements, CompassIoT can offer new insights on everything from driver experience to road quality. For me, the genius here is that the car bears witness to its surroundings such that not every car needs a chip. CompassIoT can read the behaviours of other cars from the expanse of one car’s data in broader traffic.
Boyd Cohen’s Iomob provides two further perspectives on this same industry. Iomob offers a network that integrates the APIs of various mobility services, connecting individuals with various means of transportation without taking on ownership of a vehicle. Iomob also offers an app that measures a user’s daily reduction of carbon production and rewards the user through a tradeable coin. Each of these initiatives from CompassIoT and Iomob does business by building on overlooked connections between data points. CompassIoT analyzes and sells chassis stability information to municipal governments as road-quality surveys. Iomob knits together APIs to build sustainable transportation options. They connect crypto culture with sustainably-minded users and even sell that data back to green-minded workplaces.
While the article by Kietzmann et al. describes the eventual ability of quantum computing to tackle the near-infinite complexity of an immense concern like climate change, these interviews suggest complexity at a more modest scale. They demonstrate that even an industry as established as transportation can have a whole web of interrelations that are simply waiting to be recognized, analyzed, and employed to build a more efficient, more sustainable future. To understand the implications of connecting what appear to be separate branches of this ecology, we require a more complex system that can map and process relations in parallel and with nuance. It therefore seems to me that quantum computing shows the most potential in being able to handle such a complex reservoir of information. Quantum computing handles data on a significantly complex scale. In theory, quantum computing is a possible answer to the systemic rigidity that Dr. Kietzmann observes and might be capable of analyzing the complex ecology of transportation interests and relationships efficiently and with nuance.
References:
Kietzmann, J., Demetis, D. S., Eriksson, T., and Dabirian, A. (2021) Hello Quantum! How Quantum Computing Will Change the World. IT Professional. 106-111.