EnQode: Quantum Machine Learning Just Got Easier.
Read the scientific paper here, or take it easy with a no math explanation.
Fast Amplitude Embedding for Quantum Machine Learning
So let's imagine that you want to teach a robot how to recognise cats, but there's a problem—the robot only understands the robotic language.
Every time you show it a cat picture, you have to translate it into this robot language first.
Sounds exhausting, right?
That's similar to the problem quantum computers have when learning from regular data.
Scientists have to "translate" normal numbers into a format quantum computers can understand, and the process is often slow, complicated, and full of errors.
Classical computers store numbers as bits (tiny switches that are either 0 or 1).
But quantum computers use qubits, which can be 0, 1, or a mix of both at the same time (thanks to superposition).
That means we can’t just copy and paste normal numbers into a quantum system—we have to encode them differently.
Choosing a Quantum-Friendly Format
There are several ways to do this, but one of the most popular is Amplitude Embedding.
Think of a seesaw.
On a normal seesaw, a person is either sitting on one side (0) or the other (1).
But in a quantum seesaw, they can float between both sides at once. ??
Amplitude Embedding takes a list of numbers and turns them into probabilities that decide how much each quantum state (qubit) should "lean" towards 0 or 1.
In a bigger system, we’d do this with hundreds or thousands of numbers at once, spreading the information across multiple qubits.
Building the Quantum Circuit
Once we have our numbers in amplitude form, we need to physically encode them into a quantum circuit.
? Quantum Gates: These are like LEGO blocks that manipulate qubits, setting up the correct amplitudes.
? Rotation & Entanglement: Some gates tilt the qubits (to set the amplitudes), while others link qubits together (so they work as a team).
? SWAP Gates: Sometimes, quantum hardware isn’t perfectly connected, so we use SWAP gates to move information around—kind of like shuffling seats in a crowded cinema.
All of this is done before any actual computation happens!
Exhausting.
It’s like setting up a game board before playing the game. ??
Sometimes with thousands of pieces.
The Problem With This Approach
Here’s where things get messy:
? Too Many Gates – The more numbers you encode, the more operations you need.
This makes circuits deep and complicated.
? Noise & Errors – Quantum computers aren’t perfect.
Extra gates mean extra errors, causing information to get scrambled.
? Inconsistent Depth – Some numbers require longer circuits than others, leading to different error rates depending on the data.
This is exactly why EnQode may be so important—it reduces circuit depth, standardises encoding, and makes sure every number experiences the same low level of noise. ??
Why Is This a Big Deal?
Quantum computers are supposed to be the super-geniuses of the computing world.
They can solve problems faster than regular computers, but they have one big weakness: they get confused easily.
Most existing ways of translating data into a quantum-friendly format create messy, complicated instructions.
This means:
Scientists at Rice University and Santa Clara University came up with EnQode, a faster, smarter way to translate data.
Instead of trying to make the translation perfect, EnQode groups similar pieces of data together and finds the best way to represent them.
It’s like teaching a kid to recognise different dog breeds by showing them just a few key examples instead of a thousand pictures.
How Does EnQode Work?
Think of EnQode like a travel guide for quantum computers.
Instead of making them visit every tourist attraction (which takes forever), it finds the most important landmarks and focuses only on those.
Why Is This So Cool?
With EnQode, quantum computers:
? Use 90% fewer steps to understand data.
? Make 14 times fewer mistakes than before.
? Run much faster, even on today’s noisy quantum computers.
Instead of getting overwhelmed, they can focus on solving the actual problem, whether that’s finding a new medicine, making AI smarter, or optimising financial investments.
So, What’s the Big Picture?
Quantum computers are the future, but they need better ways to handle normal data. EnQode solves a major bottleneck by making quantum machine learning:
It’s like switching from writing an entire phone book by hand to using copy-paste—way more efficient and it will help improve the ease of use for Quantum Computers! ??
Quantum is most certainly inbound - with breakthroughs like this happening every week being commercialised into new software and services.
About me
Helping leaders in Cybersecurity, Quantum, and AI drive high-impact growth, stronger valuations, and better exits.
?? Part of the world's largest Quantum Cybersecurity community (700+ members), connecting top experts in Quantum, AI, and Cybersecurity.
?? C-suite executive with a proven track record in scaling tech, finance, and asset finance businesses across EMEA & APAC.
?? Former network engineer with deep expertise in computational Root Cause Analysis & Causal Reasoning, applied in military and telecom environments.
?? Member of the Institute of Directors, European Corporate Governance Institute, and Royal United Services Institute for Defence & Security.
?? Need to accelerate growth? Let’s connect. ?? Book a call.
cybersecurity, quantum technologies
1 天前Everyone talks about the hardware problems of quantum computers. The software is almost never addressed. The solution tendered here is one of the first steps in abstracting the code to a higher, more universal, level as is done with personal computers.
cybersecurity, quantum technologies
1 天前Great job Steve! Thank you!
Cybersecurity & Penetration Testing Specialist | Web Vulnerability Analyst | Web Developer
2 天前Insightful