Rapidly Achieving Chemical Accuracy with Quantum Computing Enforced Language Model

TL;DR

This paper introduces QiankunNet-VQE, a transformer-based quantum-enforced language model that efficiently learns quantum states, achieving chemical accuracy in ground state energy calculations by integrating classical and quantum computing resources.

Contribution

It presents a novel transformer-based model enforced with quantum computing, overcoming VQE limitations and enabling practical quantum advantage in quantum chemistry calculations.

Findings

Achieves accuracy competitive with classical methods using up to 12 qubits.

Overcomes variational quantum eigensolver limitations without error mitigation.

Provides a new approach to solve many-electron Schrödinger equation efficiently.

Abstract

Finding accurate ground state energy of a many-body system has been a major challenge in quantum chemistry. The integration of classic and quantum computers has shed new light on resolving this outstanding problem. Here we propose QiankunNet-VQE, a transformer based language models enforced with quantum computing to learn and generate quantum states. It has been implemented using up to 12 qubits and attaining an accuracy level competitive with state-of-the-art classical methods. By leveraging both quantum and classical resources, this scheme overcomes the limitations of variational quantum eigensolver(VQE) without the need for cumbersome error mitigation. Moreover, QiankunNet-VQE provides a different route to achieve a practical quantum advantage for solving many-electron Schr\"odinger equation without requiring extremely precise preparation and measurement of the ground-state wavefunction on quantum computer.