Relativistic Quantum Simulation under Periodic and Dirichlet Boundary Conditions: A First-Quantised Framework for Near-Term Devices

We present a new recipe for relativistic quantum simulation using the first quantisation approach, under periodic (PBC) and Dirichlet (DBC) boundary conditions. The wavefunction is discretised across a finite grid represented by system qubits, and the squared momentum operator is expressed using the finite-difference method based on quantum translation operations. The relativistic kinetic energy is approximated through a perturbative expansion of the total kinetic Hamiltonian, incorporating higher-order momentum terms. The approach would allow variational optimisation of appropriate ansatz states to estimate both non-relativistic and relativistic ground-state energies on a quantum computer. This work offers a practical route to simulating relativistic effects on near-term quantum devices, supporting future developments in quantum physics and chemistry.

Publication Details

Journal
ArXiv.org
Published
2025-09-26
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Relativistic Quantum Simulation under Periodic and Dirichlet Boundary Conditions: A First-Quantised Framework for Near-Term Devices

ArXiv.org
Machine Learning in Materials Science
article

Relativistic Quantum Simulation under Periodic and Dirichlet Boundary Conditions: A First-Quantised Framework for Near-Term Devices

article en

Abstract

We present a new recipe for relativistic quantum simulation using the first quantisation approach, under periodic (PBC) and Dirichlet (DBC) boundary conditions. The wavefunction is discretised across a finite grid represented by system qubits, and the squared momentum operator is expressed using the finite-difference method based on quantum translation operations. The relativistic kinetic energy is approximated through a perturbative expansion of the total kinetic Hamiltonian, incorporating higher-order momentum terms. The approach would allow variational optimisation of appropriate ansatz states to estimate both non-relativistic and relativistic ground-state energies on a quantum computer. This work offers a practical route to simulating relativistic effects on near-term quantum devices, supporting future developments in quantum physics and chemistry.

ArXiv.org
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Machine Learning in Materials Science
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