# GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models

@inproceedings{Alcazar2021GEOEC, title={GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models}, author={Javier Alcazar and Mohammad Ghazi Vakili and Can Berk Kalayci and Alejandro Perdomo-Ortiz}, year={2021} }

We introduce a new framework that leverages machine learning models known as generative models to solve optimization problems. Our Generator-Enhanced Optimization (GEO) strategy is ﬂexible to adopt any generative model, from quantum to quantum-inspired or classical, such as Generative Adversarial Networks, Variational Autoencoders, or Quantum Circuit Born Machines, to name a few. Here, we focus on a quantum-inspired version of GEO relying on tensor-network Born machines, and referred to…

## 3 Citations

### Decomposition of Matrix Product States into Shallow Quantum Circuits

- Computer Science
- 2022

This work compares a range of novel and previously-developed algorithmic protocols for decomposing matrix product states of arbitrary bond dimension into low-depth quantum circuits consisting of stacked linear layers of two-qubit unitaries and proposes a proposed decomposition protocol to form a useful ingredient within any joint application of TNs and PQCs.

### Do Quantum Circuit Born Machines Generalize?

- Computer ScienceArXiv
- 2022

This work investigates the QCBM’s learning process of a cardinality-constrained distribution and sees an increase in generalization performance while increasing the circuit depth, and demonstrates the QCBMs’ ability to generalize to high-quality, desired novel samples.

### Generalization and Overfitting in Matrix Product State Machine Learning Architectures

- Computer ScienceArXiv
- 2022

It is speculated that generalization properties of MPS depend on the properties of data: with one-dimensional data (for which the MPS ansatz is the most suitable) MPS is prone to overﬁtting, while with more complex data which cannot be parameterized by MPS exactly, over-tting may be much less signiﬂcant.

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