18281497. QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE simplified abstract (Google LLC)
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 18281497 titled 'QUANTUM GENERATIVE ADVERSARIAL NETWORKS WITH PROVABLE CONVERGENCE
Simplified Explanation
The patent application describes methods and apparatus for training a quantum generative adversarial network (QGAN) to learn a target quantum state by adjusting parameters iteratively until convergence.
- Entangling operation performed on discriminator network input to measure fidelity.
- Minimax optimization of QGAN loss function to update parameters.
- QGAN loss function dependent on measured fidelity of discriminator network input.
Key Features and Innovation
- Training a QGAN to learn a target quantum state.
- Iterative adjustment of parameters until convergence.
- Entangling operation to measure fidelity.
- Minimax optimization of QGAN loss function.
- Dependency of QGAN loss function on measured fidelity.
Potential Applications
- Quantum computing.
- Quantum machine learning.
- Quantum state preparation.
Problems Solved
- Efficient training of QGAN to learn specific quantum states.
- Improved fidelity measurement in quantum systems.
Benefits
- Enhanced accuracy in learning target quantum states.
- Faster convergence in training quantum networks.
- Potential for advancements in quantum computing and machine learning.
Commercial Applications
- Quantum computing research and development.
- Quantum machine learning algorithms.
- Quantum state engineering services.
Prior Art
Prior research on quantum generative adversarial networks and quantum state learning.
Frequently Updated Research
Ongoing studies on quantum machine learning algorithms and quantum state preparation techniques.
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, unlike classical GANs that work with classical data.
What are the challenges in training quantum generative adversarial networks?
Challenges include maintaining quantum coherence, optimizing quantum loss functions, and dealing with noise 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.
