  Major obstacles remain to the implementation of macroscopic quantum
computing: hardware problems of noise, decoherence, and scaling; software
problems of error correction; and, most important, algorithm construction.
Finding truly quantum algorithms is quite difficult, and many of these genuine
quantum algorithms, like Shor's prime factoring or phase estimation, require
extremely long circuit depth for any practical application, which necessitates
error correction. In contrast, we show that machine learning can be used as a
systematic method to construct algorithms, that is, to non-algorithmically
"program" quantum computers. Quantum machine learning enables us to perform
computations without breaking down an algorithm into its gate "building
blocks", eliminating that difficult step and potentially increasing efficiency
by simplifying and reducing unnecessary complexity. In addition, our
non-algorithmic machine learning approach is robust to both noise and to
decoherence, which is ideal for running on inherently noisy NISQ devices which
are limited in the number of qubits available for error correction. We
demonstrate this using a fundamentally non-classical calculation:
experimentally estimating the entanglement of an unknown quantum state. Results
from this have been successfully ported to the IBM hardware and trained using a
hybrid reinforcement learning method.
