About the project
The project brings ideas from the observation of "in-context learning" in large language models into quantum computing. The aim is to design transformer-inspired quantum circuit architectures that brings in-context choice of families of measurement operators for shadow tomography. This contributes to hybrid NISQ quantum-classical algorithms.
In quantum computing, encoding and extracting information from a quantum state that undergoes unitary evolution has been the principal bottleneck in demonstrating quantum advantage. The introduction of shadow tomography - randomised measurements to compute expectation values of observables to create a quantum channel to be inverted by a classical machine learning method - has provided evidence for the potential for sample-efficient data driven methods in hybrid quantum-classical near-term noisy intermediate-scale quantum (NISQ) algorithms. In parallel developments in classical machine learning, the widespread adoption of large language models (LLMs) has showcased the capacity of chatbots to adapt their outputs upon exposure to prompts of input-output pair patterns without having to alter the weights of the trained model, a phenomenon dubbed "in-context learning".
This project aims to architect parameterised quantum circuits that take inspiration from attention-based transformers.
The goal of this project would be to use such provide in-context guidance for actively choosing specific observable measurement families - local or entangled - for shadow tomography in machine learning applications.
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