GAN Post-Processing for Quantum Sampling Efficiency
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Solution Overview
Problem
Sampling from a quantum processor can be slow and computationally expensive, necessitating the use of classical post-processing methods that may negate the benefits of initial quantum processor samples.
Innovation Solution
Applying machine learning techniques, specifically generative adversarial networks (GANs), to simulate and post-process samples generated by a quantum processor, improving sampling efficiency by initializing and adjusting generator and discriminator parameters to minimize divergence between generative and target distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantum processor sampling is used, then sampling accuracy is improved, but sampling speed deteriorates
Solution Approach 1:
The patent creates a generative model that copies the sampling distribution of the quantum processor. The generator learns to produce samples that replicate the quantum processor's output distribution, enabling classical systems to generate quantum-like samples without actual quantum hardware, thus improving speed while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary training of the generative adversarial network using initial quantum processor samples. By pre-training the generator and discriminator with quantum-generated data, the system establishes an accurate generative model beforehand, allowing subsequent sampling to be performed rapidly by the trained classical model without repeated quantum processor usage.
2Reliability
If quantum processor sampling is used, then sample quality is improved, but computational cost deteriorates
Solution Approach 1:
The patent replaces expensive quantum processor computations with a trained generative model. Once the generator is trained to replicate quantum sampling distributions, it can produce high-quality samples using efficient classical computations, dramatically reducing computational cost while preserving sample quality.
Solution Approach 2:
The patent uses a classical generative model as a disposable alternative to expensive quantum processor sampling. After training, the classical model can generate unlimited samples at minimal computational cost, replacing the need for repeated expensive quantum processor usage.
3Speed
If classical post-processing methods are used, then sampling speed is improved, but sample accuracy deteriorates
Solution Approach 1:
The patent trains the generative model to accurately copy the quantum processor's sampling distribution. By using quantum-generated samples as training data, the classical generator learns to replicate the precise distribution characteristics, maintaining accuracy while enabling fast classical sampling.
Solution Approach 2:
The patent implements feedback through the adversarial training process where the discriminator evaluates generated samples against real quantum samples. This feedback mechanism guides the generator to improve its output distribution until it accurately matches the quantum processor's sampling distribution, ensuring high sample accuracy.
Data Source
AI summary
A hybrid computing system comprising a quantum computer and a digital computer employs a digital computer to use machine learning methods for post-processing samples drawn from the quantum computer. Post-processing samples can include simulating samples drawn from the quantum computer. Machine learning methods such as generative adversarial networks (GANs) and conditional GANs are applied. Samples drawn from the quantum computer can be a target distribution. A generator of a GAN generates samples based on a noise prior distribution and a discriminator of a GAN measures the distance between the target distribution and a generative distribution. A generator parameter and a discriminator parameter are respectively minimized and maximized.