Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session T70: Quantum Machine Learning II
11:30 AM–2:30 PM,
Thursday, March 9, 2023
Room: Room 409
Sponsoring
Unit:
DQI
Chair: Ruslan Shayludin, JPMorgan Chase
Abstract: T70.00015 : Generative Modeling with Quantum Neurons*
2:18 PM–2:30 PM
Presenter:
Kaitlin M Gili
(University of Oxford)
Authors:
Kaitlin M Gili
(University of Oxford)
Mykolas Sveistrys
(FU Berlin)
Rohan Kumar
(University of Chicago)
Aliza Siddiqui
(Louisiana State University)
Chris J Ballance
(University of Oxford)
Collaboration:
No team acknowledgement
generative machine learning models that embed non-linear activations into the evolution of the
statevector. However, some of the most successful classical generative models, such as those
based on neural networks, involve highly non-linear dynamics for quality training. Here, we
explore the effect of these dynamics in quantum generative modeling by introducing a model that
adds non-linear activations via a neural network structure onto the standard Born Machine
framework - the Quantum Neuron Born Machine (QNBM). Further, we investigate the QNBM’s
performance relative to network size, and demonstrate that it performs best without a hidden
layer. We then compare the QNBM to the classical Restricted Boltzmann Machine (RBM) on a
wide range of probability distributions, and see that the QNBM is able to outperform this model
consistently. Lastly, we compare the QNBM to the state-of-the-art Quantum Circuit Born Machine
(QCBM), and demonstrate that it achieves a 3x smaller error rate. We therefore provide evidence
that suggests that non-linearity is a useful resource in quantum generative models, and we put
forth the QNBM as a new model with good generative performance and potential for quantum
advantage.
*Army research Office QuaCGR Fellowship
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