{"id":89173,"date":"2024-09-30T09:00:00","date_gmt":"2024-09-30T16:00:00","guid":{"rendered":"https:\/\/developer.nvidia.com\/blog\/?p=89173"},"modified":"2025-07-22T16:33:12","modified_gmt":"2025-07-22T23:33:12","slug":"advancing-quantum-algorithm-design-with-gpt","status":"publish","type":"post","link":"https:\/\/developer.nvidia.com\/blog\/advancing-quantum-algorithm-design-with-gpt\/","title":{"rendered":"Advancing Quantum Algorithm Design with GPTs"},"content":{"rendered":"\n

AI techniques like large language models (LLMs) are rapidly transforming many scientific disciplines. Quantum computing<\/a> is no exception. A collaboration between NVIDIA, the University of Toronto, and Saint Jude Children\u2019s Research Hospital is bringing generative pre-trained transformers (GPTs) to the design of new quantum algorithms<\/a>, including the Generative Quantum Eigensolver (GQE)<\/a> technique. <\/p>\n\n\n\n

The GQE technique is the latest in a wave of so-called AI for Quantum<\/a> 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. <\/p>\n\n\n\n

The CUDA-Q platform has been instrumental in developing GQE. Training and using GPT models in quantum computing requires hybrid access to CPUs, GPUs, and QPUs. The CUDA-Q focus on accelerated quantum supercomputing<\/a> makes it a fully hybrid computing environment perfectly suited for GQE. <\/p>\n\n\n\n

According to GQE co-author Alan Aspuru-Guzik<\/a>, these abilities position CUDA-Q as a scalable standard.<\/p>\n\n\n\n

Learning the grammar of quantum circuits<\/i><\/a><\/h2>\n\n\n\n

Conventional LLMs<\/a> 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.<\/p>\n\n\n\n

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.<\/p>\n\n\n

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Figure 1. Comparing GQE to a LLM<\/em><\/figcaption><\/figure><\/div>\n\n\n

Figure 1 shows that GQE is analogous to a LLM. Instead of adding individual words to construct a sentence, unitary operations are added to generate a quantum circuit.<\/p>\n\n\n\n

GQE-enabled algorithms.<\/i><\/a><\/h2>\n\n\n\n

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.<\/p>\n\n\n

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Figure 2. Comparison between the GQE and VQE<\/em><\/figcaption><\/figure><\/div>\n\n\n

All optimized parameters are handled classically in the GPT model and are updated based on the expected values of the generated circuits. This enables optimization to occur in a more favorable deep neural network landscape and offers a potential route to avoiding the barren plateaus<\/a> that impede variational algorithms. This also eliminates the need for the many intermediate circuit evaluations required in techniques like reinforcement learning.<\/p>\n\n\n\n

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:<\/p>\n\n\n\n