Quip Network @quipnetwork is a distributed computing platform developed by Postquant Labs, aiming to combine quantum computing with classical computing resources to build a globally shared quantum computing network. The project is supported by Portal Ventures and OrangeDAO, and currently has more than 137,000 followers on X.

Quip Network co-founder Colton Dillion said that quantum computers may crack today’s Bitcoin-level encryption algorithms with a certain probability before 2028, and this risk has prompted the project to explore post-quantum security solutions.
In the asset layer, Quip Network allows users to wrap existing wallets through the QUIP mechanism, adding post-quantum signature protection without migrating assets.
The project’s computational layer is a decentralized quantum-classical hybrid computing marketplace. Participants can contribute idle quantum processors, GPUs, or CPU resources to solve various optimization problems, such as logistics planning and financial modeling, among others.
During development of the testnet, the project collaborated in consultation with D-Wave Quantum Inc., and used the D-Wave Advantage2 annealing quantum computer to handle optimization tasks. It adopts a Proof of Useful Work mechanism, so that computing power serves real computational needs.
One common analogy is: Quip Network is like HotelTonight reactivating unsold hotel rooms, trying to open up idle quantum computing power for researchers and developers to use.
The testnet launched in early April 2026, and there are currently more than 13,000 people participating. On the testnet, participants can submit computing tasks, contribute computing power, and carry out verification; by completing benchmark problems, they receive QUIP token incentives. The project plans to deploy the related functions on EVM and SVM chains in later phases.
Overall, Quip Network is still in the early testnet stage and is an experimental project. The actual performance and security of quantum computing still need to be further validated through large-scale testing.
@quipnetwork #Quip