Quantum Computation¶

Run quantum chemistry simulations on Wannier tight-binding Hamiltonians from JARVIS-DFT. Two tabs: (1) VQE — variational quantum eigensolver at a single k-point using Qiskit statevector simulator with EfficientSU2 (circuit6) ansatz, returning ground-state energy, Pauli decomposition, and circuit diagram; (2) Bandstructure — full VQD bandstructure across the Brillouin zone via JARVIS get_bandstruct, comparing quantum (VQD) vs classical (NumPy) eigenvalues. Supports both electron and phonon Hamiltonians.

Open App

Overview¶

The Quantum Computation app bridges Wannier tight-binding Hamiltonians with quantum computing. It loads the WTBH from JARVIS-DFT, builds H(k) at the requested k-point, decomposes it into a sum of Pauli operators, then runs Qiskit VQE (or VQD for bandstructure) on a statevector simulator. The EfficientSU2 ansatz (QuantumCircuitLibrary.circuit6) parameterizes the trial wavefunction. Results are compared against classical NumPy diagonalization.

Data Source

JARVIS-WTB — Wannier tight-binding Hamiltonians via jarvis.db.figshare.get_wann_electron / get_wann_phonon. Qiskit — VQE/VQD via jarvis.io.qiskit.inputs.HermitianSolver (requires qiskit==0.41.1, qiskit-aer).

Available Materials¶

JID	Formula	Type	Qubits	Description
JVASP-816	Al	electron	3	FCC Aluminum
JVASP-1002	Si	electron	3	Diamond Silicon
JVASP-1174	GaAs	electron	4	Zinc blende GaAs
JVASP-35680	PbS	electron	4	Hexagonal PbS
JVASP-39	Cu	electron	3	FCC Copper
JVASP-816	Al	phonon	2	Al phonon
JVASP-1002	Si	phonon	3	Si phonon

Endpoints¶

`GET /quantum/materials` — List available materials¶

curl "https://atomgpt.org/quantum/materials" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "accept: application/json"

Returns {"materials": [{"jid": "JVASP-816", "formula": "Al", "type": "electron", "qubits": 3, "desc": "FCC Aluminum"}, ...]}.

`POST /quantum/vqe` — VQE at a single k-point¶

Run Qiskit VQE on the Wannier Hamiltonian H(k) at a specific k-point. Returns VQE ground-state energy, classical eigenvalues, Pauli decomposition, and circuit diagram.

curl -X POST "https://atomgpt.org/quantum/vqe" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "jid": "JVASP-816",
    "kpoint": [0.5, 0.0, 0.5],
    "ham_type": "electron",
    "reps": 2,
    "backend": "statevector_simulator"
  }'

Phonon example:

curl -X POST "https://atomgpt.org/quantum/vqe" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "jid": "JVASP-1002",
    "kpoint": [0.0, 0.0, 0.0],
    "ham_type": "phonon",
    "reps": 2
  }'

Field	Type	Default	Description
`jid`	string	`"JVASP-816"`	JARVIS material ID
`kpoint`	list[float]	`[0.5, 0.0, 0.5]`	k-point in fractional coordinates [kx, ky, kz]
`ham_type`	string	`"electron"`	`"electron"` or `"phonon"`
`reps`	int	2	EfficientSU2 circuit repetitions (1–5)
`backend`	string	`"statevector_simulator"`	Qiskit Aer backend

Response:

Field	Description
`vqe_ground_state`	VQE ground-state energy relative to E_F (eV)
`classical_ground_state`	NumPy ground-state energy (eV)
`error_eV`	Absolute error between VQE and classical
`classical_eigenvalues`	All eigenvalues from NumPy (eV, relative to E_F)
`n_qubits`	Number of qubits = log₂(matrix_size)
`matrix_size`	Hamiltonian dimension
`n_pauli_terms`	Number of Pauli operators in decomposition
`n_circuit_params`	Number of variational parameters
`circuit_depth`	Quantum circuit depth
`circuit_diagram`	ASCII text diagram of the EfficientSU2 circuit
`pauli_decomposition`	Top Pauli terms with coefficients

`POST /quantum/bandstructure` — Full VQD bandstructure¶

Compute band structure across the Brillouin zone using JARVIS get_bandstruct. Runs VQD at each k-point along the high-symmetry path and compares with classical diagonalization.

curl -X POST "https://atomgpt.org/quantum/bandstructure" \
  -H "Authorization: Bearer sk-XYZ" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "jid": "JVASP-816",
    "ham_type": "electron",
    "line_density": 3
  }'

Field	Type	Default	Description
`jid`	string	`"JVASP-816"`	JARVIS material ID
`ham_type`	string	`"electron"`	`"electron"` or `"phonon"`
`line_density`	int	1	K-point density along path (higher = more k-points, slower)

Response:

Field	Description
`eigvals_classical`	2D array (nkpts × nbands) from NumPy
`eigvals_vqd`	2D array (nkpts × nbands) from Qiskit VQD
`n_kpoints`	Number of k-points along path
`n_bands`	Number of bands
`labels`	High-symmetry point labels with k-point indices
`mae_eV`	Mean absolute error between VQD and classical (eV)
`formula`	Chemical formula

Python Examples¶

VQE at single k-pointBandstructurePlot VQD vs classical

import requests

response = requests.post(
    "https://atomgpt.org/quantum/vqe",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "accept": "application/json",
        "Content-Type": "application/json",
    },
    json={
        "jid": "JVASP-816",
        "kpoint": [0.5, 0.0, 0.5],
        "ham_type": "electron",
        "reps": 2,
    },
)
data = response.json()
print(f"Material: {data['jid']} ({data['ham_type']})")
print(f"Qubits: {data['n_qubits']}, Pauli terms: {data['n_pauli_terms']}")
print(f"VQE ground state:      {data['vqe_ground_state']:.6f} eV")
print(f"Classical ground state: {data['classical_ground_state']:.6f} eV")
print(f"Error: {data['error_eV']:.6f} eV")
print(f"\nAll eigenvalues:")
for i, e in enumerate(data["classical_eigenvalues"]):
    print(f"  Band {i+1}: {e:.4f} eV")

import requests

response = requests.post(
    "https://atomgpt.org/quantum/bandstructure",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "accept": "application/json",
        "Content-Type": "application/json",
    },
    json={
        "jid": "JVASP-816",
        "ham_type": "electron",
        "line_density": 3,
    },
)
data = response.json()
print(f"{data['formula']} — {data['n_kpoints']} k-points, {data['n_bands']} bands")
print(f"MAE (VQD vs classical): {data['mae_eV']:.6f} eV")
print(f"Labels: {[(l['label'], l['index']) for l in data['labels']]}")

import requests
import matplotlib.pyplot as plt
import numpy as np

response = requests.post(
    "https://atomgpt.org/quantum/bandstructure",
    headers={
        "Authorization": "Bearer sk-XYZ",
        "Content-Type": "application/json",
    },
    json={"jid": "JVASP-816", "ham_type": "electron", "line_density": 3},
)
data = response.json()

eigvals_np = np.array(data["eigvals_classical"])
eigvals_vqd = np.array(data["eigvals_vqd"])
nk = data["n_kpoints"]

fig, ax = plt.subplots(figsize=(8, 5))
for b in range(eigvals_np.shape[1]):
    ax.plot(range(nk), eigvals_np[:, b], "b-", lw=1.5,
            label="Classical" if b == 0 else "")
    ax.plot(range(nk), eigvals_vqd[:, b], "ro", ms=3, alpha=0.7,
            label="VQD" if b == 0 else "")

for lbl in data["labels"]:
    ax.axvline(lbl["index"], color="gray", ls="--", lw=0.5)
    ax.text(lbl["index"], ax.get_ylim()[0], lbl["label"],
            ha="center", va="top", fontsize=9)

ax.set_ylabel("Energy (eV)")
ax.set_title(f"{data['formula']} — MAE: {data['mae_eV']:.4f} eV")
ax.legend()
plt.tight_layout()
plt.savefig("quantum_bands.png", dpi=150)

AGAPI Agent¶

from agapi.agents import AGAPIAgent
import os

agent = AGAPIAgent(api_key=os.environ.get("AGAPI_KEY"))

# VQE
response = agent.query_sync("Run VQE for Al at the X point using quantum computation")
print(response)

# Bandstructure
response = agent.query_sync("Compute quantum bandstructure for Si using VQD")
print(response)

References¶

K. Choudhary, J. Phys.: Condens. Matter 33, 385501 (2021) DOI
BenchQC (2025) DOI
K. Choudhary, Comp. Mat. Sci. 259, 114063 (2025) DOI
atomgptlab/jarvis