Communications of the ACM
New Questions About Many Queries For Quantum Computing
Researchers in Canada have raised new concerns about the advantages of the bucket brigade model for algorithms using super-polynomial oracle queries, such as Grover's quantum searching algorithm.
Credit: University of Waterloo
Researchers in Canada have raised new concerns about the advantages of the bucket brigade model for algorithms using super-polynomial oracle queries, such as Grover's quantum searching algorithm.
One of the main advantages is that it requires exponentially less active, potentially noisy processes to complete queries, allowing for more efficient energy consumption and more robust implementation.
"The bucket brigade model...activates routing nodes only along the active path in the computation, it reduces the chances of error, and increases the speedup," says Vlad Gheorghiu, a postdoctoral fellow at the University of Waterloo's Institute for Quantum Computing (IQC). "It works well for algorithms that make a relatively small number of queries [i.e., polynomial], some of which may be used in quantum machine learning."
A team from the IQC used the simplest form of noise, a bit flip error model, to test its robustness. The researchers found a greater number of noisy queries and the need for active quantum error correction offset the main benefits of the model approach.
They observed for algorithms that make a smaller number of queries such as quantum machine learning, the scheme with a polynomially small error rate is still useful.
From University of Waterloo
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