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In this example, the logical information is a single bit in the one state and the physical information are the three duplicate bits. Creating a physical state that represents the logical state is called encoding and determining which logical state is encoded in the physical state is called decoding .
Once the logical qubit is encoded, errors on the physical qubits can be detected via stabilizer measurements. A lookup table that maps the results of the stabilizer measurements to the types and locations of the errors gives the control system of the quantum computer enough information to correct errors.
Data corruption can occur at any level in a system, from the host to the storage medium. Modern systems attempt to detect corruption at many layers and then recover or correct the corruption; this is almost always successful but very rarely the information arriving in the systems memory is corrupted and can cause unpredictable results.
[1] [2] Data redundancy can also be used as a measure against silent data corruption; for example, file systems such as Btrfs and ZFS use data and metadata checksumming in combination with copies of stored data to detect silent data corruption and repair its effects. [3]
An example of a data-integrity mechanism is the parent-and-child relationship of related records. If a parent record owns one or more related child records all of the referential integrity processes are handled by the database itself, which automatically ensures the accuracy and integrity of the data so that no child record can exist without a parent (also called being orphaned) and that no ...
Let us define an [,] stabilizer quantum error-correcting code to encode logical qubits into physical qubits. The rate of such a code is k / n {\displaystyle k/n} . Its stabilizer S {\displaystyle {\mathcal {S}}} is an abelian subgroup of the n {\displaystyle n} -fold Pauli group Π n {\displaystyle \Pi ^{n}} .
Physical data model options. [1] A physical data model (or database design) is a representation of a data design as implemented, or intended to be implemented, in a database management system. In the lifecycle of a project it typically derives from a logical data model, though it may be reverse-engineered from a given database implementation.
A logical journal stores only changes to file metadata in the journal, and trades fault tolerance for substantially better write performance. [9] A file system with a logical journal still recovers quickly after a crash, but may allow unjournaled file data and journaled metadata to fall out of sync with each other, causing data corruption.