Qtum’s arm virtual machine allows users to execute applications in a decentralized manner.
It is one of a number of platforms — along with Ethereum (ETH), EOS (EOS), Neo (NEO) and others – that support the operation of decentralized applications (DApps). DApp-enabled platforms take advantage of smart contracts (self-executing programs that are run by blockchain network nodes) to operate the business- and customer-oriented applications without the need for middlemen and the inefficiencies associated with those.
In order to execute these applications, Qtum uses its arm virtual machine environment. A virtual machine is a piece of software that simulates an operating system within another, physical machine that runs on the actual hardware.
These virtual machines are used in the IT industry to save on physical space, time and management cost by running several operating systems on a single computer server. In addition, they are a vital instrument for risk management — if one of the virtual operating systems encounters problems and stops working, others can safely continue their independent operation.
Blockchain-based DApp platforms like Qtum have to rely on virtual machines for all of their distributed computing requirements due to the fact that their networks are comprised of numerous independent individual nodes. These physically distributed network members contribute their computational power to an abstract virtual computer, which then runs the decentralized apps.
The “arm” part of Qtum’s arm virtual machine refers to its computer architecture, that is, the particular way in which it receives and processes instructions. Qtum originally ran on the x86 architecture and later transitioned to the arm version in 2021. Arm and x86 belong to two different families of instruction set architectures: reduced instruction set computer (RISC) and complex instruction set computer (CISC), respectively.
In comparison to CISC, RISC architectures (to which the arm virtual machine belongs) require a larger number of individually simpler instructions to run an application — the result being increased computational efficiency at the cost of the increased complexity of writing programs.
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