How can quantum computing be useful for Machine Learning
If you’ve heard of quantum computing, you might be excited about the possibility of applying it to machine learning applications. How can you harness this emerging technology? I work at Springboard, and we recently launched a machine learning bootcamp that includes a job guarantee. We want to make sure our graduates are exposed to cutting-edge machine learning applications — so we put together this article as part of our research into the intersection of quantum computing and machine learning.
Let’s start by examining the difference between quantum computing and classical computing. In classical computing, your data is stored in physical bits and it is binary and mutually exhaustive: a bit is either in a 0 state or in a 1 state and it cannot be both at the same time. Quantum computing uses the physical properties of smaller-scale physics interactions among molecules so that the quantum bits (called “qubits” for short) can be a linear combination of both a classical 0 and 1 state — allowing for much more data to be stored in a qubit than in a regular bit.
Quantum computing does suffer from slowdowns, since quantum molecules are entangled with one another, and direct physical observations of the quantum system they are placed in (i.e., trying to get classical results from a quantum computer). But it can process larger amounts of data faster and reduce space/time considerations for many classical computing tasks — including those related to machine learning.
Let’s now look at some specific instances where quantum computing can help.
1- Quantum annealers and minimization of loss functions with quantum tunnelling
The first thing to distinguish is between universal gate quantum computers, which can perform every (50 or so of which are described in the preceding link) and quantum annealers, which are simplified versions of quantum-capable computers suited to one purpose.
You might be familiar with quantum annealers, such as those from . This article goes in-depth in explaining the differences between . Basically, quantum annealers are “souped-down” versions of universal quantum computers that specialize in finding super-local minima and closer approximations to a global minima than a classical computer.
Quantum annealers work by having . The magnets influence one another and in order for the system as a whole to save energy, they will flip into a coordinated orientation that minimizes energy use. In a classical setting, the magnets get trapped into low-energy settings before being able to find lower minima, but with quantum properties such as tunneling, they can skip those large energy cost settings — which allows for functions to more easily descend from a local minima into either a global minima or a closer local minima to the global minima.
When it comes to cost functions, this can mean the difference between a gradient descent function being stuck in a sub-optimal setting to one where it is optimal or near-optimal, especially on complex non-convex error surfaces.
They can be a solid solution to a complex machine learning optimization problem if you need a “good enough” answer in a pinch, in a situation that would ordinarily require tons of classical computing power.
