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AtomQC

Atomistic Calculations on Quantum Computers

AtomQC is a toolkit for running materials-science electronic-structure and lattice-dynamics calculations on quantum computers and quantum simulators. It maps the Wannier tight-binding Hamiltonians (WTBH) of real materials — taken from the JARVIS-DFT database — onto qubits and solves for their eigenvalues using variational quantum algorithms such as the Variational Quantum Eigensolver (VQE), ADAPT-VQE, and the Variational Quantum Deflation (VQD) method. From these eigenvalues it reconstructs electronic and phonon bandstructures that can be compared directly against classical (NumPy) diagonalization.

The approach is described in:

K. Choudhary, "Quantum computation for predicting electron and phonon properties of solids", J. Phys.: Condens. Matter 33, 385501 (2021). doi:10.1088/1361-648X/ac1154

Note

This project was originally developed under the github.com/usnistgov organization. New updates and developments are now carried out at github.com/atomgptlab/atomqc.

Why quantum computing for materials?

Predicting the electronic and vibrational properties of solids reduces to finding the eigenvalues of a Hamiltonian matrix \(H(k)\) at each point \(k\) in the Brillouin zone. For a compact basis such as maximally-localized Wannier functions, these matrices are small enough that their qubit-mapped versions can be diagonalized on today's noisy quantum hardware and simulators, making materials a practical testbed for near-term quantum algorithms.

AtomQC provides the glue between:

  • JARVIS-DFT WTBHs (get_wann_electron, get_wann_phonon, get_hk_tb) — the physics inputs,
  • Qiskit quantum algorithms and simulators — the quantum back end, and
  • jarvis-tools HermitianSolver / get_bandstruct — the solver layer that maps \(H(k)\) to Pauli operators, runs the variational circuits, and assembles bandstructures.

Features

  • 🔬 Electronic & phonon eigenvalues of real materials from JARVIS-DFT WTBHs.
  • ⚛️ VQE with a library of hardware-efficient ansätze (QuantumCircuitLibrary).
  • ⚙️ ADAPT-VQE — iteratively grows a compact ansatz from a Pauli excitation pool.
  • 📈 VQD bandstructures along high-symmetry \(k\)-paths via get_bandstruct.
  • 🧮 Classical cross-check against exact NumPy diagonalization for every run.
  • 🖥️ Multiple back ends — exact statevector, Qiskit Aer simulators, and real IBM Quantum hardware (with an API token).
  • 🌐 Live web app — interactive VQE / ADAPT-VQE / VQD explorer at atomgpt.org/quantum.

Where to next?

  • Installation — set up the environment.
  • Quick start — run your first VQE and bandstructure.
  • Methods — the physics and algorithms behind AtomQC.
  • Example scripts — the batch/benchmark scripts shipped with the package.
  • Web app — the hosted AtomGPT Quantum Explorer.