Bulletin of the American Physical Society
2023 APS March Meeting
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session N73: Quantum Generative Models
11:30 AM–2:30 PM,
Wednesday, March 8, 2023
Room: Room 405
Sponsoring
Unit:
DQI
Chair: Kathleen Hamilton, Oak Ridge National Laboratory
Abstract: N73.00007 : Generative Learning with Quantum Models*
12:42 PM–1:18 PM
Presenter:
Kaitlin M Gili
(University of Oxford)
Author:
Kaitlin M Gili
(University of Oxford)
Collaboration:
no team acknowledgement
and are difficult to tackle even with some of the best classical ML approaches that we have
available. By introducing quantum models for these tasks, we have already seen promising results
for quantum advantage with respect to the model’s expressibility, trainability, and most recently,
generalization performance. While being the golden standard for ML and a widely investigated
topic in supervised learning, generalization in the unsupervised regime has previously been
difficult to evaluate and thus, overlooked in the investigations of quantum generative models.
Here, we provide a summary of progress towards advantage with respect to some of the
most prominent quantum generative models - the Quantum Circuit Born Machine (QCBM) and its
quantum-inspired version, the Tensor Network Born Machine (TNBM). We first present a
framework that provides a robust assessment of classical and quantum generative models for
their generalization capabilities. Subsequently, we utilize the framework to conduct the first deepdive
investigation into the QCBM’s and TNBM’s generalization performance, using models with
varied model ansatze and training dataset sizes. We present an advantage of TNBMs over stateof-
the-art classical models such as Generative Adversarial Networks (GANs) and we show for the
first time that QCBMs exhibit good generalization performance with increasing circuit depth, on
instances with training sets corresponding to a small fraction of the overall data space.
Additionally, we see that both, quantum-inspired tensor-network models and QCBMs are able to
effectively learn bias in its training data and generate unseen samples that are higher quality than
those in the training set. Lastly, we discuss the near-term opportunities and challenges for
quantum generative models as we scale and move towards practical applications.
*Army Research Office QuaCGR Fellowship
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