A classical-quantum hybrid oracle architecture for Boolean oracle
identification in the noisy intermediate-scale quantum era
(2019)cite arxiv:1905.05751Comment: 10 pages, 5 figures (including Supplementary Information).Аннотация
Quantum algorithms have the potential to be very powerful. However, to
exploit quantum parallelism, some quantum algorithms require an embedding of
large classical data into quantum states. This embedding can cost a lot of
resources, for instance by implementing quantum random-access memory (QRAM). An
important instance of this is in quantum-enhanced machine learning algorithms.
We propose a new way of circumventing this requirement by using a
classical-quantum hybrid architecture where the input data can remain
classical, which differs from other hybrid models. We apply this to a
fundamental computational problem called Boolean oracle identification, which
offers a useful primitive for quantum machine learning algorithms. Its aim is
to identify an unknown oracle amongst a list of candidates while minimising the
number of queries to the oracle. In our scheme, we replace the classical oracle
with our hybrid oracle. We demonstrate both theoretically and numerically that
the success rates of the oracle query can be improved in the presence of noise
and also enables us to explore a larger search space. This also makes the model
suitable for realisation in the current era of noisy intermediate-scale quantum
(NISQ) devices. Furthermore, we can show our scheme can lead to a reduction in
the learning sample complexity. This means that for certain sizes of learning
samples, our classical-quantum hybrid learner can complete the learning task
faithfully whereas a classical learner cannot.
Описание
A classical-quantum hybrid oracle architecture for Boolean oracle identification in the noisy intermediate-scale quantum era