Google LLC (20240303502). QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE simplified abstract

QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE

Organization Name

Google LLC

Inventor(s)

Yuezhen Niu of El Segundo CA (US)

Hartmut Neven of Malibu CA (US)

Vadim Smelyanskiy of Mountain View CA (US)

Sergio Boixo Castrillo of Rancho Palos Verdes CA (US)

QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240303502 titled 'QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE

Simplified Explanation

The patent application describes a method for training a Quantum Generative Adversarial Network (QGAN) to learn a target quantum state by adjusting parameters iteratively until convergence.

• Entangling operation is performed on the discriminator network input to measure fidelity.
• Minimax optimization of the QGAN loss function is done to update parameters.
• The QGAN loss function depends on the measured fidelity of the discriminator network input.

Key Features and Innovation

• Training a QGAN to learn a target quantum state.
• Iteratively adjusting parameters until convergence.
• Performing an entangling operation to measure fidelity.
• Minimax optimization of the QGAN loss function.
• Dependence of the QGAN loss function on measured fidelity.

Potential Applications

This technology can be applied in quantum computing, quantum machine learning, and quantum state estimation.

Problems Solved

• Efficient training of QGANs to learn specific quantum states.
• Enhancing the capabilities of quantum machine learning algorithms.
• Improving quantum state estimation accuracy.

Benefits

• Accurate learning of target quantum states.
• Enhanced performance of quantum machine learning models.
• Increased efficiency in quantum state estimation.

Commercial Applications

Quantum computing software development, quantum machine learning platforms, quantum state estimation services.

Prior Art

Readers can explore prior research on QGANs, quantum machine learning, and quantum state estimation algorithms.

Frequently Updated Research

Stay updated on advancements in quantum computing, quantum machine learning, and quantum state estimation techniques.

Questions about Quantum Generative Adversarial Networks

How do QGANs differ from classical GANs?

QGANs are specifically designed to operate in the quantum computing domain, utilizing quantum principles for training and generating data.

What are the challenges in training QGANs effectively?

Training QGANs requires careful parameter tuning and optimization to ensure convergence and accurate learning of quantum states.

Original Abstract Submitted

methods and apparatus for learning a target quantum state. in one aspect, a method for training a quantum generative adversarial network (qgan) to learn a target quantum state includes iteratively adjusting parameters of the qgan until a value of a qgan loss function converges, wherein each iteration comprises: performing an entangling operation on a discriminator network input of a discriminator network in the qgan to measure a fidelity of the discriminator network input, wherein the discriminator network input comprises the target quantum state and a first quantum state output from a generator network in the qgan, wherein the first quantum state approximates the target quantum state; and performing a minimax optimization of the qgan loss function to update the qgan parameters, wherein the qgan loss function is dependent on the measured fidelity of the discriminator network input.