Researchers at the California Institute of Technology (Caltech) have demonstrated that quantum computing could be useful for speeding up the solutions to "semidefinite programs," a widely used class of optimization problems that includes linear programs.
The Caltech study describes a new quantum algorithm that could accelerate such solutions, sometimes on an exponential level.
The researchers say the new quantum algorithm would significantly expedite semidefinite programs that are used to learn unknown quantum states.
Caltech professor Fernando Brandao says this type of quantum learning problem is encountered by researchers studying large quantum systems in a variety of different fields, such as superconducting qubits, or quantum information units similar to computer bits that would function according to superconducting technology.
Brandao notes the semidefinite programs are employed to deliver a description of how the quantum matter is behaving, which enables the researchers to better understand the strange states of the subatomic realm.
From California Institute of Technology
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