Entangling Quantum Generative Adversarial Networks.

Area of Science:

• Quantum Computing
• Machine Learning
• Artificial Intelligence

Background:

• Generative Adversarial Networks (GANs) are prevalent machine learning techniques for data generation.
• Existing quantum GANs face limitations in achieving optimal convergence and practical application.
• Quantum circuits offer unique capabilities for enhancing generative models.

Purpose of the Study:

• To propose a novel quantum generative adversarial network architecture, the entangling quantum GAN (EQ-GAN).
• To overcome limitations of previous quantum GANs by leveraging quantum entanglement.
• To demonstrate a provably optimal Nash equilibrium in a fully quantum GAN framework.

Main Methods:

• Developed a new EQ-GAN architecture utilizing entangling operations within quantum circuits.
• Performed the first multiqubit experimental demonstration of a fully quantum GAN on a Google Sycamore superconducting quantum processor.
• Conducted numerical simulations to confirm error mitigation capabilities up to 18 qubits.

Main Results:

• The EQ-GAN successfully converges to a provably optimal Nash equilibrium.
• Demonstrated error mitigation of uncharacterized errors on superconducting quantum hardware.
• Achieved successful simulations of the EQ-GAN up to 18 qubits, confirming its scalability and effectiveness.
• Prepared an approximate quantum random access memory using the EQ-GAN.
• Utilized the EQ-GAN for training quantum neural networks with variational datasets.

Conclusions:

• The EQ-GAN represents a significant advancement in quantum generative modeling, overcoming prior limitations.
• Experimental validation on quantum hardware confirms the EQ-GAN's efficacy in error mitigation and achieving optimal equilibrium.
• The EQ-GAN shows promise for diverse quantum machine learning applications, including quantum memory and neural network training.