We can read a tumor's entire genome in an afternoon. We still cannot tell the woman sitting in front of us whether her treatment will work. The reason is stranger than you think.
Feature · Part 1 of 2 · 4 min read
by Dr Vishala Bodetti, AI Medical Lead
Sarah is forty-two. For twenty years, her job has been making sure buildings don't fall down. She never guesses — every beam she designs is checked and double-checked before it's trusted. She doesn't hope a floor will hold. She knows it will
Then she found the lump. Stage III triple-negative breast cancer. Her oncologist offers a plan: Regimen A works in about sixty per cent of women with her tumour profile. If three cycles do nothing, they try Regimen B.
Sarah asks the only question that matters. Will it work on me? The honest answer-the one her doctor is too kind to say outright - is that nobody in the building knows.
Her physician is excellent. Oncology spent a century earning that sixty per cent. But sixty per cent is a fact about a crowd, and Sarah is not a crowd. She is one woman with one tumour, either in the sixty or the forty — and the only way to find out is to spend three months living the answer.
No engineer would open a bridge to traffic without knowing if it holds. In oncology, we start treatment without knowing if it will — and wait three months to find out
This is not negligence. It is the frontier. We have sequencing, imaging, and a diagnostics industry worth billions, and we are still blind to the one thing Sarah needs: how her mutations will meet this molecule. We treat people with averages because averages are the sharpest instrument in the drawer. The question is why—and the answer has almost nothing to do with biology.
THE PROBLEM IS NOT MEDICINE. IT IS ARITHMETIC.
A tumor is not a lump. It is an ecosystem—billions of cells, each carrying its own edits to the code, competing like species on an island. Somewhere in that population sits a cell already immune to the drug Sarah hasn't been given yet.
Inside a single cell, tens of thousands of proteins fold and bind and signal every instant. Most mutations mean nothing. A few decide everything. To model one medium-sized protein exhaustively would take a computer built from more atoms than exist in the observable universe.
And here is the strange part: that cascade is not chemistry. Chemistry is the summary we write afterward. Underneath, it is quantum mechanics—electrons in superposition, particles correlated in ways with no equivalent in the visible world. Every classical computer that simulates a molecule translates a quantum system into ordinary arithmetic, and something is always lost. Our machines aren't too slow. They are the wrong kind of thing.
“Nature isn't classical, dammit — and if you want to make a simulation of nature, you'd better make it quantum mechanical.” — Richard Feynman, 1981. It took the world forty years to admit he was right.
BUILD A MACHINE OUT OF THE WEIRDNESS
A coin on a table is heads or tails. Committed. That is an ordinary bit — every computer you've touched is built from billions of them.
Now spin the coin. While it spins, it is not secretly heads, waiting to be seen. It is genuinely both — a blend with no equivalent in anything you can hold. That is a qubit. Entangle two of them, and they stop being separate objects; they become one system with a shared description. That description doubles with every qubit added: three hundred of them describe more configurations than there are atoms in the known universe, in a device that fits on a desk.
That is why simulating a molecule on an ordinary computer is hopeless — you are trying to write that number down by hand. And it is why a quantum machine changes the game: it does not write the number down. It builds a small, controllable system with the same kind of state space as the molecule, and watches what it does.
A quantum computer does not calculate the molecule. It agrees, briefly, to be the molecule — and then you ask it how it feels.
It will not read an MRI better. It will not fix a billing system. It is a specialized instrument for exactly one class of problem: things whose difficulty comes from being quantum in the first place. Which is, inconveniently, what every molecule in Sarah's body happens to be.
BRINGING TOMORROW'S SCIENCE TO TODAY'S PATIENTS
What stands between Sarah and a real answer is not a missing molecule. It is arithmetic — the gap between how complicated her tumour is and how much complication our instruments can hold. Mortality is a fact. Arithmetic is an engineering problem. Engineering problems get smaller.
Right now, in five laboratories, the answer is taking shape: a scanner that watches a tumour eat instead of measuring how big it is; a sensor built from a flaw in a diamond, listening to a single cell; gold that sits inert inside a tumour until light tells it to cook it; particles that diagnose and treat in the same motion; a machine that could collapse a decade of drug discovery into an afternoon.
Not one is a cure. Every one is real, funded, and further along than you'd guess — though not, for the most part, close enough for Sarah.
That is Part Two Five Ways Physics Is Coming for Cancer (to be published on 22nd Sep. Bookmark this page and return)
