Quantum computing aims to harness the properties of quantum physics to solve real world problems. Its next job may be to help us understand reality itself.

Growing up, the idea of death frightened me. My coping strategy was to distract myself from it, or tell myself that I’d think about it later on in life. This worked fairly well. Minus the occasional night terror. Or that time when I found myself on the family couch, bear-hugging my mom in an existential panic (bless her heart). Today, this fear still crops up, but I’ve gained some new tools to deal with it. As my therapist tells me: feel your fear. And beneath it, often you’ll find another feeling. For me, underneath this fear lies excitement. And underneath this excitement, is for me, a question: What is reality? What is this thing I’ve been born into and, presumably, am so afraid of leaving behind?

As luck would have it, some very smart people have been investigating this question for a very long time. Physicists, in particular, have devised smart theories, many of them validated through ingenious scientific experiments, as to what the nature of our reality actually is. It’s what brought us “every action has an equal and opposite reaction,” or Isaac Newton’s laws of motion. Or “a particle can be a wave, and a wave a particle,” or quantum physics as developed by luminaries such as Niels Bohr, Max Planck and Albert Einstein. Discoveries that spawned new theories about the nature of our reality, such as that it: represents 4 out of 11 possible dimensions; is constantly splintering into copies of itself; or that it’s actually generated by our own consciousness (insert head explosion emoji).

Over the last 50 years, however, our understanding of “reality” – or the world around us as explained through fundamental physics – has slowed. While past discoveries enable many of the technologies we depend on today, the long-term, open-source nature of this research means few institutions have been willing to pony up the investment required to push this field along. Caltech physicist Sean Carrol thinks that today there are fewer than 100 physicists actively working on advancing our understanding of fundamental physics. Is this slower pace of discovery an accurate reflection of our curiosity for the world around us? Thankfully, another path is emerging.

Of the many challenges to testing new theories in fundamental physics, two big ones are time, and cost. The time required to design a test for theory. The cost required to build the experiment and run it. While many effective experiments can be done in the low-million dollar range, the ones that yield the most interesting results can cost much more. The Large Hadron Collider, for example, built to help us discover new particles, took decades to plan and cost a whopping $4.75 billion to build.

Simulations, however, offer a potential workaround. They’re quicker to set up, cheaper to build, and could potentially be as useful to researchers as experiments conducted in the “real” world. The challenge until now has been that our simulations have been – necessarily – basic. Accurately simulating interactions between atoms in matter as small as a molecule is computationally overwhelming, even for our most powerful supercomputers. Simply put, our simulations have not been able to mimic real world experiments. Enter Quantum Computing.

Quantum Computing is a fundamentally different approach to building a computer. At its core, the job of a computer is to process long strings of bits encoded as 0s and 1s. A classical computer (the ones we use today) processes these bits via billions of transistors embedded in a silicon chip. A quantum computer, on the other hand, relies on “quantum bits” – or the induced properties of subatomic particles. These “qubits” have the special property of being able to represent a 0, a 1, or any value in between – at the same time. Because of this feature, they eliminate some of the constraints of (binary) classical computing systems and enable enormous computational outputs in parallel.

Quantum computers may help us run the types of hyper-realistic physics simulations that up until now have been impossible, at a fraction of the cost of conducting those experiments “in real life.” In fact, the very act of building stable, useful quantum computers might give us new insights into quantum mechanics itself.

In addition to Quantum Computers, Artificial Intelligence (AI) may also have a role to play. Today, one common way of building AI is through layered neural networks which, through ingesting large amounts of data (say tagged photos of cats), use increasing levels of abstraction to develop an understanding of how this data “works.” Well, what if instead of making sense of cat photos, we asked this AI to make sense of unexplained natural phenomena, such as Dark Matter? Two physicists at MIT, Tailin Wu and Max Tegmark, have started doing just that. They’ve endowed a machine learning algorithm with four common analytical strategies employed by scientists, and asked it to make sense of increasingly realistic simulations of the physical world. Paired with a quantum computer, we can imagine a rich environment in which an AI might help us make sense of the world around us.

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The rise of Artificial Intelligence comes with a long list of potential dangers. I’m especially wary of how AI can be paired with content to influence our behaviors. AI that – as historian Yuval Noah Harari puts it – “knows us better than we know ourselves.” As with any new technology, at their core Quantum Computers and Artificial Intelligence are tools, which we know from experience can be used just as easily to build, as they can to destroy. The ability for Quantum Computing and AI to help us make gains in areas that are important to us, such a developing better treatments for disease, making our cities less congested and modeling climate change will, I hope, set a clear example of the ways we want to apply these technologies, and a clearer contrast to the ways in which we don’t. And, in the process, maybe even shed light on a question that has sparked the curiosity of humanity for generations: what is the nature of reality?

// As published on LinkedIn.