atomqc 2025.10.11

atomqc

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 here.

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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.

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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, choosing the operator with the largest energy gradient at each step.
• 📈 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.

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Installation

AtomQC targets Python ≥ 3.8 and builds on jarvis-tools and qiskit.

From source

pip install atomqc

or

git clone https://github.com/atomgptlab/atomqc.git
cd atomqc
pip install -e .

With conda (reproducible environment)

A pinned environment is provided in environment.yml:

conda env create -f environment.yml
conda activate my_atomqc

Core dependencies

jarvis-tools, qiskit, qiskit-aer, qiskit-algorithms, numpy, scipy, pandas, scikit-learn, matplotlib. For real IBM hardware also install qiskit-ibm-runtime.

The Qiskit API has changed substantially over time. The example scripts in atomqc/scripts/ were written against the older qiskit.aqua API, while the AtomGPT web app (see below) uses the modern qiskit>=1.2 / qiskit-algorithms primitives. Match the Qiskit version to the entry point you intend to run.

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Quick start

Run a single-k-point VQE on FCC aluminum (JVASP-816) and compare against classical diagonalization:

from jarvis.db.figshare import get_wann_electron, get_hk_tb
from jarvis.io.qiskit.inputs import HermitianSolver

# 1. Fetch the Wannier tight-binding Hamiltonian for Al from JARVIS-DFT
w, ef, atoms = get_wann_electron(jid="JVASP-816")

# 2. Build H(k) at a chosen k-point
hk = get_hk_tb(w=w, k=[0.5, 0.5, 0.0])

# 3. Solve for eigenvalues with VQE and with exact NumPy diagonalization
HS = HermitianSolver(hk)
vqe_energy, vqe_result, vqe = HS.run_vqe()      # min eigenvalue via VQE
classical_vals, classical_vecs = HS.run_numpy() # exact reference

print("VQE ground state:", vqe_energy)
print("Classical minimum:", min(classical_vals.real))

Full bandstructure (VQD)

from jarvis.db.figshare import get_wann_electron
from jarvis.io.qiskit.inputs import get_bandstruct

w, ef, atoms = get_wann_electron(jid="JVASP-816")
out = get_bandstruct(w=w, atoms=atoms, line_density=1, savefig=True)
# out["eigvals_q"]  -> quantum (VQD) eigenvalues along the k-path
# out["eigvals_np"] -> classical reference eigenvalues

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Testing

A small, fast test suite covers the core Hamiltonian → qubit → VQE pipeline:

pip install pytest
pytest atomqc/tests/

The tests in atomqc/tests/test_qiskit.py build a small synthetic Hermitian H(k), decompose it into Pauli strings, and check that VQE reproduces the exact NumPy ground state. They run offline in a few seconds (no JARVIS downloads), which is also what CI runs on every push.

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Repository layout

atomqc/
├── atomqc/
│   ├── __init__.py                 # version
│   ├── scripts/
│   │   ├── aluminum_example.py     # VQE on Al with several classical optimizers
│   │   ├── circuit_comparison.py   # compare ansätze from QuantumCircuitLibrary
│   │   ├── compare_elect_vqe.py    # batch electron VQE vs NumPy over JARVIS jids
│   │   └── compare_phonons_vqe.py  # batch phonon VQE vs NumPy over JARVIS jids
│   ├── tests/
│   │   └── test_qiskit.py          # fast offline tests: H(k) -> Pauli -> VQE
│   └── data/
│       ├── electron_vqe_np_jid.csv # benchmark: electron VQE vs classical
│       └── phonon_vqe_np_jid.csv   # benchmark: phonon VQE vs classical
├── environment.yml                 # pinned conda environment
├── setup.py
└── README.md

Example scripts

Script	What it does
aluminum_example.py	Runs VQE on the Al Hamiltonian sweeping COBYLA, L-BFGS-B, SLSQP, CG, SPSA optimizers and plots convergence.
circuit_comparison.py	Benchmarks the six ansätze in QuantumCircuitLibrary at several k-points.
compare_elect_vqe.py	Loops over JARVIS jids, computes min/max electronic eigenvalues with VQE and NumPy, and dumps JSON.
compare_phonons_vqe.py	Same as above for phonon Hamiltonians.

The CSV files under atomqc/data/ hold pre-computed VQE-vs-classical benchmark results referenced in the paper.

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AtomGPT.org Quantum Computation Explorer

A hosted, interactive version of these workflows runs at atomgpt.org/quantum. It lets you pick a material, choose a back end, and run VQE / ADAPT-VQE at a single k-point or compute a full VQD bandstructure — all from the browser, with live circuit diagrams, statevector / Bloch-sphere visualizations, and the Hamiltonian matrix shown alongside the results.

SlaKoNet-VQD Coming soon!

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Citation

If you use AtomQC in your research, please cite:

@article{choudhary2021quantum,
  title   = {Quantum computation for predicting electron and phonon properties of solids},
  author  = {Choudhary, Kamal},
  journal = {Journal of Physics: Condensed Matter},
  volume  = {33},
  number  = {38},
  pages   = {385501},
  year    = {2021},
  doi     = {10.1088/1361-648X/ac1154}
}

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References & links

• 📄 Paper: J. Phys.: Condens. Matter 33, 385501 (2021)
• 📄 Related: J. Comput. Chem. (2025), doi:10.1002/jcc.70202
• 🌐 Web app: atomgpt.org/quantum
• 📓 Colab notebook: Qiskit-based electronic bandstructure
• 🧰 JARVIS-Tools: github.com/atomgptlab/jarvis
• ⚛️ Qiskit: qiskit.org

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License

Distributed under the terms of the LICENSE included in this repository.