AI Techniques Like Large Language Models Are Rapidly Transforming Many Scientific Disciplines
A collaboration between NVIDIA, the University of Toronto, and Saint Jude Children’s Research Hospital is bringing generative pre-trained transformers (GPTs) to the design of new quantum algorithms, including the Generative Quantum Eigensolver (GQE) technique.
The GQE Technique Is the Latest in a Wave of So-Called AI for Quantum Techniques
Developed with the NVIDIA CUDA-Q platform, GQE is the first method enabling you to use your own GPT model for creating complex quantum circuits.
Learning the Grammar of Quantum Circuits
Conventional LLMs can be a useful analogy for understanding GQE. In general, the goal of an LLM is to take a vocabulary of many words; train a transformer model with text samples to understand things like meaning, context, and grammar; and then sample the trained model to produce words, which are then strung together to generate a new document.
Where LLMs deal with words, GQE deals with quantum circuit operations. GQE takes a pool of unitary operations (vocabulary) and trains a transformer model to generate a sequence of indices corresponding to unitary operations (words) that define a resulting quantum circuit (document). The grammar for generating these indices is a set of rules trained by minimizing a cost function, which is evaluated by computing expectation values using previously generated circuits.
GQE-Enabled Algorithms
In the era of noisy, small-scale quantum (NISQ) computers, quantum algorithms are limited by several hardware constraints. This has motivated the development of hybrid quantum-classical algorithms like the Variational Quantum Eigensolver (VQE), which attempts to circumvent these limitations by offloading onerous tasks to a conventional computer.
The GQE method is the first hybrid quantum-classical algorithm leveraging the power of AI to accelerate NISQ applications. GQE extends NISQ algorithms in several ways:
- Ease of optimization: GQE builds quantum circuits without quantum variational parameters.
- Quantum resource efficiency: By replacing quantum gradient evaluation with sampling and backpropagation, GQE is expected to provide greater utility with fewer quantum circuit evaluations.
- Customizability: The GQE is very flexible and can be modified to incorporate a priori domain knowledge, or applied to target applications outside of chemistry.
- Pretrainability: The GQE transformer can be pretrained, eliminating the need for additional quantum circuit evaluations.
Results from GPT-QE
For the inaugural application of GQE, the authors built a specific model inspired by GPT-2 (referred to explicitly as GPT-QE) and used it to estimate the ground state energies of a set of small molecules.
The operator pool of vocabulary was built from chemically inspired operations such as excitations and time evolution steps that were derived from a standard ansatz known as ‘unitary coupled-clusters with single and double excitations’ (UCCSD). An ansatz is an approach to parameterizing quantum circuits.
Opportunities to Extend the GQE Framework
This collaboration is a first step towards understanding the broad opportunities for how GPT models can enable quantum supercomputing applications.
Future research will hone exploring different operator pools for GQE and optimal strategies for training. This includes a focus on pretraining, a process where existing datasets can be used to either make the transformer training more efficient or aid in the convergence of the training process.
Conclusion
The GQE is a novel example of how GPT models and AI in general can be used to enable many aspects of quantum computing.
Frequently Asked Questions (FAQs)
Q: What is the GQE technique?
A: The GQE technique is a novel method for designing quantum algorithms using generative pre-trained transformers (GPTs).
Q: What is the CUDA-Q platform?
A: The CUDA-Q platform is a hybrid computing environment that enables accelerated quantum supercomputing.
Q: What are the benefits of using GQE?
A: GQE offers several benefits, including ease of optimization, quantum resource efficiency, customizability, and pretrainability.
Q: What are the potential applications of GQE?
A: GQE has the potential to be applied to a wide range of quantum computing applications, including quantum chemistry and geometric quantum machine learning.
