Beyond Ones and Zeroes

Illustration by Rebecca Sidi

By Siddharth Rout

Your TikTok feed, the Apple Pay ‘beep’ at checkout, or even the swipe-access system at EVERY single door: all of it is built on ones and zeroes. However, when faced with immense complexity, even supercomputers based on this architecture hit a wall, struggling to process problems with too many variables. The solution to this isn’t a faster computer, but a whole different kind. 

So, what’s the difference? Classical computer bits, similar to coins, can only exist in one of two states: heads or tails. Qubits are more like this coin spinning mid-air. They exist in a state of being multiple things at once (superposition): a zero, a one, or a linear combination of both. Mathematically, this is written as

∣ψ⟩=α∣0⟩+β∣1⟩

where α and β are probability amplitudes (the probability that a qubit is either 0 or 1) that satisfy the equation ∣α∣2+∣β|2=1. Since each qubit can be simultaneously 0 or 1, a system of n qubits can represent 2n possible states at once; a system of 2 qubits can represent all of 00, 01, 10, 11 at the same time. 

This allows quantum computers to explore vast solution spaces that classical machines must process one at a time; in 2019, Google’s Sycamore was able to solve a problem over a billion times faster than our most powerful classical supercomputers!

Parallelism gives quantum computers remarkable potential beyond just theory. One of its most promising applications is in drug discovery. Researchers have already begun applying this idea to some of the most notoriously complex bottlenecks in drug design, including predicting protein structures and their folding mechanisms. In a 2024 study, researchers successfully modelled the structure of a catalytic loop in the Zika virus NS3 helicase. By translating the energy equations of the molecule’s electrons into quantum instructions, the team demonstrated how quantum methods could complement classical deep-learning approaches like AlphaFold2. With this, we can not only reduce the length of the drug development timeline, but also understand exactly how certain molecules cause symptoms of disease. 

Chemists are also looking to use this technology to simulate binding affinity, which helps predict how strongly a drug will attach to a target protein. A recent paper from IonQ and Accenture Labs outlined a workflow to calculate the binding affinity of amyloid-beta, a protein implicated in Alzheimer’s disease. They demonstrated how quantum algorithms could, in theory, calculate the ground-state energy of metal-protein interactions and estimated the resources needed to perform such calculations, even though they weren’t able to run the specific calculations needed on current hardware. 

The applicability of quantum simulation extends across physical scales, from molecular interactions to the cosmos. Astrophysicists are beginning to explore how quantum computers could simulate cosmic phenomena such as the formation of black holes and the early expansion of the universe mere microseconds after the Big Bang. 

In 2022, researchers made headlines by simulating a traversable wormhole, and just a few months ago, Professor Khatiwada and her team at Illinois Tech started work on a detector that uses superconducting qubits to sense tiny vibrations induced by dark matter. These projects highlight why quantum computers are so promising for cosmology: the universe itself operates under quantum mechanics. Classical models must approximate these effects into binary yeses and nos, but newer methods of approaching the same problems can reproduce the chaotic effects that govern our universe. 

Many of the universe’s most extreme environments are places where the laws of classical physics collapse. To understand them, we need tools that operate on the same quantum principles. While we will not have quantum laptops that finish your p-sets instantly anytime soon, the technology is no longer a far-fetched idea. It promises a future where we can design life-saving medicines and unravel cosmic mysteries with a fraction of the effort. The world runs on uncertainty, and the next breakthroughs will come from learning to embrace complexity, not reduce it.