Interested in why QEC is necessary for quantum computers to function and what makes it difficult?
Read more about this in my blog post on Riverlane’s website!
https://www.riverlane.com/blog/why-do-quantum-computers-need-qec
At Riverlane, we are working on making scalable QEC stack. At its centre the stack needs an efficient and fast decoder that can efficiently scale up to thousands of qubits while maintaining the high data throughput. In our recent paper, we present Collision Clustering - a decoding algorithm implemented in both field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware and demonstrate the required speed and scalability.
A real-time, scalable, fast and resource-efficient decoder for a quantum computer
To make Quantum Error Correction useful, we need a low-latency and high-throughput decoders tightly integrated with the control of a Quantum Computer. In our recent work, we demonstrate such a system on a scalable architecture and measure its throughput and latency. This work makes an important step, and gathers a lot of useful data towards building a fault-tolerant full-stack Quantum Computer
Read more details in our paper published on ArXiv: Demonstrating real-time and low-latency quantum error correction with superconducting qubits
Faul-tolerant computation had a long-standing scalability problem that we address in our recent paper: Parallel window decoding enables scalable fault tolerant quantum computation
Read more about this in my blog post on Riverlane’s website:
I started working for Riverlane in September 2021. We work on making quantum computing useful sooner by developing a Quantum Error Correction stack.
Quantum Computers are very sensitive systems and it is unavoidable that they constantly make errors. Therefore, to make them practically useful, we need systems that correct those errors. These are crucial to making useful quantum computers scalable and practically useful. In Riverlane, I am a part of the Logic team that works on solving these problems by building a Quantum Error Correction stack .
During 2019, I was freelancing for the company Dreams AI as a data scientist working on machine learning projects involving Optical Character Recognition (OCR), Natural Language Processing (NLP), and low dimensional data embedding. The projects I was working on proved successful and I was invited to come to Hong Kong to take them further.
During my time in Hong Kong (from 15th March to 26th May 2020), I worked on two projects. Firstly, a system for scanning and processing receipts to automatise workflow of accounting agencies. In this project, I had an opportunity to work as a backend developer, learning how to set up APIs, best programming practices, and collaborating with frontend developers to make a webapp. However, I spent most of my time on a Speaker Verification project. The final goal was to be able to tell if the two audio tapes of people speaking are coming from the same speaker or not, to be used as a verification layer in certain web applications. There, I learned about audio processing and audio-focused machine learning models. I used a combination of convolutional and recurrent neural networks to find the level of similarity between voices in any recordings.
