Instanodes is now offering post-quantum account engineering for both Ethereum Virtual Machine (EVM)-compatible networks and Solana, responding to escalating concerns about the future security of blockchain transactions. Research published this year by Google Quantum AI in March and IonQ in September detailed updated resource estimates for fault-tolerant quantum computers and their potential to compromise current cryptographic methods.

Instanodes, which already supports more than 50 blockchain networks with its RPC endpoints and node infrastructure, aims to help businesses evaluate and fortify account security against these emerging quantum threats. The company stated that these developments highlight the need to evaluate existing blockchain security solutions and prepare for potential cryptographic vulnerabilities.

Ethereum and Solana Account Security via Hash-Based Signatures

Instanodes has deployed post-quantum account implementations for both Ethereum and Solana, using hash-based digital signatures to authorize accounts and reduce reliance on potentially vulnerable elliptic-curve cryptography. These implementations are specifically designed for integration into existing wallet, custody, and protocol architectures, adapting verification mechanisms to each blockchain’s unique execution environment. On Ethereum, the company is integrating post-quantum signature verification into programmable smart-account architectures like ERC-4337, which allows for custom signature validation logic, Instanodes says.

While Solana continues to rely on Ed25519 for native transaction signatures, Instanodes’ approach on that blockchain involves custom program-level signature verification and asset-control mechanisms. This layered approach provides businesses with a pathway to evaluate alternative cryptographic methods for account security and proactively prepare infrastructure for the increasing capabilities of quantum computing.

Instanodes is now offering quantum-readiness assessments to help businesses identify vulnerabilities within their cryptographic dependencies and create structured migration roadmaps. These assessments, alongside custom cryptographic engineering services, are designed to address the specific needs of exchanges, custodians and blockchain protocols. Beyond account implementations and assessments, Instanodes provides a range of blockchain infrastructure services, including RPC endpoints, dedicated nodes, validator infrastructure and dedicated blockchain environments across more than 50 networks. Instanodes intends these expanded services to help organizations navigate the transition towards quantum-resistant security measures and safeguard digital assets against future threats.

  • Post-Quantum Accounts: Hash-based signature implementations for EVM-compatible networks and Solana, designed for integration with wallet, custody and protocol architectures
  • Quantum-Readiness Assessments: Reviews of cryptographic dependencies, identification of quantum-vulnerable components and structured migration roadmaps
  • Custom Cryptographic Engineering: Development of hash-based signature systems, on-chain verification mechanisms, ERC-4337 smart-account implementations and custom Solana programs
  • Blockchain Infrastructure: RPC endpoints, dedicated nodes, validator infrastructure and dedicated blockchain environments across more than 50 networks

Source: https://www.prnewswire.com/news-releases/instanodes-introduces-post-quantum-accounts-for-ethereum-and-solana-302927775.html

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Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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