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Researchers outline a protocol for performing a popular quantum-classical machine-learning algorithm with a so-called measurement-based quantum computer, which could allow for more resource-efficient calculations, explains Katherine Wright, Deputy Editor of Physics.
A graph-based approach to quantum computing could make better use of qubit resources than typical circuit-based approaches.
Photo: L. Dellantonio/University of Waterloo
Much like toddlers turning two, researchers working on quantum computers have reached that awkward “in-between” phase: They are beginning to understand the full potential of what they can do, but achieving that potential is—tantalizingly—just out of reach. Quantum computers have issues executing arbitrarily long computations because of hardware limitations, and poorly executed algorithms can easily deliver unexpected—and often incorrect—outcomes.
One route that researchers are taking to guide quantum computers through
this early phase is to run their algorithms on hybrid devices that
merge a quantum toddler with its more mature sibling—a classical
computer. Using machine-learning techniques, researchers can pair
classical and quantum processors in feedback loops to solve hard
optimization problems. Now, Ryan Ferguson and Luca Dellantonio at the
University of Waterloo, Canada, and colleagues outline how to perform
one popular quantum machine-learning algorithm on a hybrid system where
the quantum processor is “measurement based” [1]. Dellantonio says that their proposal could allow better use of photonic platforms in hybrid computers.
The type of machine-learning algorithm that the team studies is called a variational quantum eigensolver, or the catchy “VQE” for short. VQE algorithms calculate the ground-state energy of a molecule and are specifically designed for hybrid computers, delegating tasks between the quantum and classical processors...
In the team’s protocol, the quantum calculations are done with a so-called graph state, which is a multiqubit state that is typically depicted by a network diagram with vertices and edges. The vertices represent individual qubits and the edges link qubits that interact. First, an “ansatz” graph state is created, which represents the initial guess for the ground state of the system of interest. The initial graph state is expanded by adding qubits and then measuring them. The results of those measurements are fed into the classical computer. A new ansatz graph state is then created, and the process is repeated.
Source: Physics
