Skip to main content

A framework for fermionic quantum simulation based on variational quantum algorithms.

Project description

Carcará logo

License: MIT PyPI

Carcará

Carcará is a framework for fermionic quantum simulation based on variational quantum algorithms, engineered from the ground up for deployment on real quantum hardware.

Overview

Carcará connects theoretical condensed matter physics with NISQ-era quantum hardware. Engineered around variational workflows, the framework streamlines the pipeline from mapping complex fermionic Hamiltonians onto qubit operators to optimizing ansatz states and executing error-mitigated circuits on real quantum backends.

Key Features

  • Fermion-to-Qubit Mapping: Built-in, optimized transformations including Jordan-Wigner, Bravyi-Kitaev, and parity mappings to translate fermionic creation/annihilation operators into Pauli strings.

  • Hardware-Efficient & Physics-Inspired Ansatzes: Ready-to-use ansatz generation, including Unitary Coupled Cluster (UCCSD) and hardware-efficient templates designed to minimize circuit depth and gate errors on real QPUs.

  • Hybrid Variational Solvers: Robust implementation of the Variational Quantum Eigensolver (VQE) and its time-dependent variants, coupled with state-of-the-art classical optimizers (e.g., SPSA, COBYLA, SLSQP).

  • Real Hardware Deployment: Seamless integration with major quantum cloud providers (IBM Quantum Platform) with native support.

  • Advanced Error Mitigation: Built-in noise-resilient pipelines featuring Zero-Noise Extrapolation (ZNE) and symmetry verification.

Installation

From pip

The easiest way to install Carcará is with pip:

pip install carcara

From github

To install Carcará directly from the GitHub repository, run the following commands:

git clone https://github.com/seixas-research/carcara.git
cd carcara
pip install -e .

Getting started

One- and two-body integrals for H2

The carcara.integrals module computes real-space one- and two-body integrals over any localized basis. The example below builds a minimal basis of one hydrogen 1s orbital on each proton and evaluates the core Hamiltonian and the electron-repulsion tensor. The full script lives in examples/H2_integrals.py.

import numpy as np

from carcara.basis import HydrogenicOrbital
from carcara.integrals import Grid, IntegralEngine, Potentials

# Geometry: the user-facing API uses Angstrom for lengths and eV for energies.
# H2 equilibrium bond length ~0.74 A; two protons about the origin.
Z, R = 1.0, 0.74
proton_a = np.array([0.0, 0.0, -R / 2])
proton_b = np.array([0.0, 0.0, +R / 2])

# External electron-nuclear potential V(r) = -sum_A Z / |r - R_A|.
potentials = Potentials([(Z, proton_a), (Z, proton_b)])

grid = Grid(center=[0.0, 0.0, 0.0], box_size=5.0, h=0.10)  # Angstrom
basis = [HydrogenicOrbital(1, 0, 0, Z=Z, center=proton_a),
         HydrogenicOrbital(1, 0, 0, Z=Z, center=proton_b)]

engine = IntegralEngine(basis, grid)

# One-body: kinetic T and nuclear attraction V -> core Hamiltonian (eV).
T, V = engine.one_body(potentials.nuclear_potential)
h_core = T + V

# Two-body electron-repulsion tensor (ab|cd) in chemists' notation (eV).
eri = engine.two_body(method="fft")

print("Core Hamiltonian h = T + V (eV):")
print(h_core.real)
print(f"(00|00) on-site repulsion = {eri[0, 0, 0, 0].real:.3f} eV")

Running it prints the 2 x 2 core Hamiltonian and the on-site repulsion (00|00) ~ 17.0 eV, in agreement with the exact hydrogen 1s value of 5/8 Ha = 17.007 eV.

A heteronuclear molecule: LiH

The same machinery scales to multi-orbital, heteronuclear systems. The example examples/LiH_integrals.py builds a small minimal basis for LiH -- the Li 1s, 2s and 2p_z orbitals plus the H 1s -- using the true nuclear charges (Z_Li = 3, Z_H = 1) in the potential and effective charges from Slater's rules for the hydrogenic basis orbitals via HydrogenicOrbital.from_slater:

labels = ["Li 1s", "Li 2s", "Li 2pz", "H 1s"]
basis = [HydrogenicOrbital.from_slater(1, 0, 0, atomic_number=3, center=li_pos),
         HydrogenicOrbital.from_slater(2, 0, 0, atomic_number=3, center=li_pos),
         HydrogenicOrbital.from_slater(2, 1, 0, atomic_number=3, center=li_pos),
         HydrogenicOrbital.from_slater(1, 0, 0, atomic_number=1, center=h_pos)]

potentials = Potentials([(3.0, li_pos), (1.0, h_pos)])  # true nuclear charges
engine = IntegralEngine(basis, grid)
T, V = engine.one_body(potentials.nuclear_potential)
eri = engine.two_body(method="fft")

This yields the 4 x 4 one-body matrices and the 4 x 4 x 4 x 4 electron-repulsion tensor. The H 1s on-site integral (33|33) ~ 17.0 eV again recovers the exact 5/8 Ha.

License

This is an open source code under MIT License.

Acknowledgements

We thank financial support from INCT Materials Informatics (Grant No. 406447/2022-5).

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

carcara-26.7.8.tar.gz (2.2 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

carcara-26.7.8-py3-none-any.whl (88.4 kB view details)

Uploaded Python 3

File details

Details for the file carcara-26.7.8.tar.gz.

File metadata

  • Download URL: carcara-26.7.8.tar.gz
  • Upload date:
  • Size: 2.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.7

File hashes

Hashes for carcara-26.7.8.tar.gz
Algorithm Hash digest
SHA256 4e6a2b3a562ae055909f1fcca41a5c426a49e85662aedc78faac7a1eb41b4390
MD5 3eb7ee99646329472878ad586e2515d0
BLAKE2b-256 93f53ffe326aa425efd502bc74da50c1a24e56316d08a3e585ff66d804a50e79

See more details on using hashes here.

File details

Details for the file carcara-26.7.8-py3-none-any.whl.

File metadata

  • Download URL: carcara-26.7.8-py3-none-any.whl
  • Upload date:
  • Size: 88.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.7

File hashes

Hashes for carcara-26.7.8-py3-none-any.whl
Algorithm Hash digest
SHA256 8502619da929df4ee94745c4762bfc96f0ac8acc26d12d1cbfd7323fb47311c6
MD5 698a55fbe7469c02b0e95184e62dffbf
BLAKE2b-256 b12073c1765e258e039c4f42488e46670a8f6c8d2aa3c756f36b516f3b5a7d55

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page