Google llc (20240303502). QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE simplified abstract
Contents
- 1 QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Quantum Generative Adversarial Networks
- 1.13 Original Abstract Submitted
QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE
Organization Name
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.
- Performing an entangling operation on the discriminator network input to measure fidelity.
- Minimax optimization of the QGAN loss function to update parameters based on fidelity measurements.
Key Features and Innovation
- Training a QGAN to learn a target quantum state through iterative adjustments.
- Utilizing an entangling operation and minimax optimization for parameter updates.
Potential Applications
- Quantum computing research and development.
- Quantum machine learning applications.
- Quantum state preparation and optimization.
Problems Solved
- Improving the efficiency and accuracy of learning target quantum states.
- Enhancing the capabilities of quantum generative models.
Benefits
- Faster and more precise learning of quantum states.
- Advancement in quantum computing and machine learning technologies.
Commercial Applications
Quantum Machine Learning Optimization: Enhancing quantum machine learning algorithms for various industries.
Prior Art
Research in quantum machine learning and generative adversarial networks.
Frequently Updated Research
Ongoing studies on quantum generative models and quantum state learning algorithms.
Questions about Quantum Generative Adversarial Networks
How do Quantum Generative Adversarial Networks differ from classical GANs?
Quantum GANs operate on quantum data and utilize quantum principles, while classical GANs work with classical data and algorithms.
What are the potential challenges in implementing Quantum Generative Adversarial Networks in practical quantum computing applications?
Challenges may include scalability, noise, and hardware limitations in quantum systems.
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.
