TY - JOUR
T1 - Error-mitigated data-driven circuit learning on noisy quantum hardware
AU - Hamilton, Kathleen E.
AU - Pooser, Raphael C.
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.
AB - Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.
KW - Error mitigation
KW - Quantum machine learning
UR - http://www.scopus.com/inward/record.url?scp=85114068006&partnerID=8YFLogxK
U2 - 10.1007/s42484-020-00021-x
DO - 10.1007/s42484-020-00021-x
M3 - Article
AN - SCOPUS:85114068006
SN - 2524-4906
VL - 2
JO - Quantum Machine Intelligence
JF - Quantum Machine Intelligence
IS - 1
ER -