H2 Resource Estimation¶

This tutorial estimates EFTQC hardware resources for H2 with q2m3.core.resource_estimation. It compares vacuum and MM-embedded Hamiltonians.

Run The Script¶

uv run python examples/resources/h2_resource_estimation.py

Minimal API Pattern¶

import numpy as np

from q2m3.core import compare_vacuum_solvated, estimate_resources

h2_symbols = ["H", "H"]
h2_coords = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 0.74]])

vacuum = estimate_resources(symbols=h2_symbols, coords=h2_coords, basis="sto-3g")
print(vacuum.logical_qubits)
print(vacuum.toffoli_gates)

comparison = compare_vacuum_solvated(
    symbols=h2_symbols,
    coords=h2_coords,
    mm_charges=np.array([-0.834, 0.417, 0.417, -0.834, 0.417, 0.417]),
    mm_coords=np.array(
        [
            [3.0, 0.0, 0.0],
            [3.5, 0.8, 0.0],
            [3.5, -0.8, 0.0],
            [-3.0, 0.0, 0.0],
            [-3.5, 0.8, 0.0],
            [-3.5, -0.8, 0.0],
        ]
    ),
)
print(comparison.delta_lambda_percent)

Current H2 Reference Values¶

The H2/STO-3G example reports these values for its default cost model:

Metric	H2/STO-3G reference
Logical qubits	`115`
Toffoli gates	`1,224,608`
Target error	Chemical-accuracy scale by default

MM embedding is expected to change the resource estimate only slightly. Point charges mainly change one-electron terms. For this small H2 example, two-electron integrals dominate the resource estimate.

Interpretation Boundaries¶

Resource estimates describe an EFTQC algorithmic cost model. They do not predict local Catalyst compile memory, host RAM usage, or wall-clock runtime for PennyLane simulation.