Book Image

Dancing with Qubits

By : Robert S. Sutor
5 (1)
Book Image

Dancing with Qubits

5 (1)
By: Robert S. Sutor

Overview of this book

Quantum computing is making us change the way we think about computers. Quantum bits, a.k.a. qubits, can make it possible to solve problems that would otherwise be intractable with current computing technology. Dancing with Qubits is a quantum computing textbook that starts with an overview of why quantum computing is so different from classical computing and describes several industry use cases where it can have a major impact. From there it moves on to a fuller description of classical computing and the mathematical underpinnings necessary to understand such concepts as superposition, entanglement, and interference. Next up is circuits and algorithms, both basic and more sophisticated. It then nicely moves on to provide a survey of the physics and engineering ideas behind how quantum computing hardware is built. Finally, the book looks to the future and gives you guidance on understanding how further developments will affect you. Really understanding quantum computing requires a lot of math, and this book doesn't shy away from the necessary math concepts you'll need. Each topic is introduced and explained thoroughly, in clear English with helpful examples.
Table of Contents (16 chapters)
Preface
13
Afterword

1.2 I’m awake!

What if we could do chemistry inside a computer instead of in a test tube or beaker in the laboratory? What if running a new experiment was as simple as running an app and having it complete in a few seconds?

For this to really work, we would want it to happen with full fidelity. The atoms and molecules as modeled in the computer should behave exactly like they do in the test tube. The chemical reactions that happen in the physical world would have precise computational analogs. We would need a fully faithful simulation.

If we could do this at scale, we might be able to compute the molecules we want and need. These might be for new materials for shampoos or even alloys for cars and airplanes. Perhaps we could more efficiently discover medicines that are customized to your exact physiology. Maybe we could get better insight into how proteins fold, thereby understanding their function, and possibly creating custom enzymes to positively change our body chemistry.

Is this plausible? We have massive supercomputers that can run all kinds of simulations. Can we model molecules in the above ways today?

tikz JPG figure

Let’s start with C8H10N4O2 -- 1,3,7-Trimethylxanthine. This is a very fancy name for a molecule which millions of people around the world enjoy every day: caffeine. An 8 ounce cup of coffee contains approximately 95 mg of caffeine, and this translates to roughly 2.95 × 1020 molecules. Written out, this is

295,000,000,000,000,000,000 molecules.

A 12 ounce can of a popular cola drink has 32 mg of caffeine, the diet version has 42 mg, and energy drinks often have about 77 mg. [11]

Question 1.2.1

How many molecules of caffeine do you consume a day?

These numbers are large because we are counting physical objects in our universe, which we know is very big. Scientists estimate, for example, that there are between 1049 and 1050 atoms in our planet alone. [4]

To put these values in context, one thousand = 103, one million = 106, one billion = 109, and so on. A gigabyte of storage is one billion bytes, and a terabyte is 1012 bytes.

Getting back to the question I posed at the beginning of this section, can we model caffeine exactly in a computer? We don’t have to model the huge number of caffeine molecules in a cup of coffee, but can we fully represent a single molecule at a single instant?

Caffeine is a small molecule and contains protons, neutrons, and electrons. In particular, if we just look at the energy configuration that determines the structure of the molecule and the bonds that hold it all together, the amount of information to describe this is staggering. In particular, the number of bits, the 0s and 1s, needed is approximately 1048:

10,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000.
From what I said above, this is comparable to 1% to 10% of the number of atoms on the Earth.

This is just one molecule! Yet somehow nature manages to deal quite effectively with all this information. It handles the single caffeine molecule, to all those in your coffee, tea, or soft drink, to every other molecule that makes up you and the world around you.

How does it do this? We don’t know! Of course, there are theories and these live at the intersection of physics and philosophy. We do not need to understand it fully to try to harness its capabilities.

We have no hope of providing enough traditional storage to hold this much information. Our dream of exact representation appears to be dashed. This is what Richard Feynman meant in his quote at the beginning of this chapter: ‘‘Nature isn’t classical.’’

However, 160 qubits (quantum bits) could hold 2160 ≈ 1.46 × 1048 bits while the qubits were involved in computation. To be clear, I’m not saying how we would get all the data into those qubits and I’m also not saying how many more we would need to do something interesting with the information. It does give us hope, however.


IF YOU CAN SEE THIS, AN IMAGE IS MISSING

Richard Feynman at the California Institute of Technology in 1959. Photo is in the public domain.


In the classical case, we will never fully represent the caffeine molecule. In the future, with enough very high quality qubits in a powerful enough quantum computing system, we may be able to perform chemistry in a computer.

To learn more

Quantum chemistry is not an area of science in which you can say a few words and easily make clear how quantum computers might eventually be used to compute molecular properties and protein folding configurations, for example. Nevertheless, the caffeine example above is an example of quantum simulation.

For an excellent survey of the history and state of the art of quantum computing applied to chemistry as of 2019, see Cao et al. [2] For the specific problem of understanding how to scale quantum simulations of molecules and the crossover from High Performance Computers (HPC), see Kandala et al. [10]