European institutions to develop neural networks for quantum error correction
As 2022 is expected to see more quantum computing use cases, more and more organizations are looking to implement the technology in their organization. Although quantum computing sounds exciting, the reality is that it is not as simple as it claims.
In fact, researchers believe there is not only much left to discover in quantum computing, but also several challenges to overcome. This is why in most cases; quantum computing is still being used for testing use cases instead of being implemented. Working with quantum computing would require a comprehensive hardware and software platform capable of running the most complex quantum algorithms and experiments.
And this is where the Quantum Orchestration Platform (QOP) comes in. Created by Quantum Machines, the platform fundamentally redefines the quantum control stack architecture for quantum processor control and operations. The comprehensive hardware and software platform is capable of running even the most complex algorithm.
This includes quantum error correction, multi-qubit calibration, etc. By helping to realize the full potential of any quantum processor, the QOP enables unprecedented advancement and acceleration of quantum technologies as well as the ability to scale to thousands of qubits.
Despite this, there are still two main challenges in quantum computing: quantum error correction and optimal control. In order to overcome these challenges, Quantum Machines and Alice&Bob, one of Europe’s leading developers of quantum processors, together with Europe’s top quantum computing research groups, have launched a project to establish and commercialize a radically new approach to control. quantum based on neural networks.
The three-year project will focus on developing a quantum controller that integrates real-time neural networks capable of generating commands. The use of neural networks is expected to improve the precision and performance of quantum processors and significantly reduce the classical control resources needed, which is a real bottleneck for scaling error correction and optimal control methods.
The expected results of the project are:
- The deployment of a universal quantum controller with a user-friendly interface and accompanying open-source code libraries for implementing the new approach on a variety of quantum processors and devices.
- The public availability of a cloud-based quantum processor with a single user interface enables the programming and execution of a rich variety of real-time neural networks. This will allow researchers to explore this new approach towards practical quantum computing and quantum sensing, even if they don’t have direct access to quantum hardware.
According to Dr. Yonatan Cohen, CTO of Quantum Machines, the future viability of practical quantum computing is highly dependent on achieving consistent and efficient error correction.
“We expect that the neural networks developed under ARTEMIS will help improve our control over more qubits, even in the face of environmental decoherence, to facilitate the real-world deployment of quantum computers. “, commented Dr. Cohen.
For Dr. Théau Peronnin, CEO of Alice&Bob, the company’s roadmap is based on lean inspiration. They aim to reduce the minimum quantum resources needed to build a fault-tolerant quantum computer.
“By making control more efficient, ARTEMIS advances this philosophy outside of the cryostat and brings the reality of practical quantum computing one step closer,” added Dr Peronnin.
The project will use the combined expertise of participating companies and institutions in the fields of microwave engineering, machine learning, control theory, experimental quantum physics, design and realization of commercial products and industrial-grade quantum computers to realize the full potential of this project.
“We expect neural networks to help identify new quantum control strategies,” said Benjamin Huard, a professor at the Ecole Normale Supérieure de Lyon in France. “In particular, we expect a considerable improvement to discover the optimal control laws in imperfect experimental contexts. We are excited to bring together such a strong consortium to experimentally test these ideas and create useful tools for quantum computing.
The ARTEMIS project aims to establish and commercialize a radically new approach to quantum control based on neural networks. It will use reinforcement learning on real-time experimental observations to overcome today’s major challenges in quantum computing – quantum error correction and optimal control.