qsimkit 0.1.14

Quantum Simulation Toolkit - Error bounds and Trotterization tools

Qsimkit - Quantum Simulation Toolkit

⚠️ Disclaimer: This package is still under active development. APIs may change, and features are being continuously improved. Use in production with caution.

A Python package for quantum simulation with error bounds and Trotterization tools. Built on Qiskit and OpenFermion.

Features

• Hamiltonians: Spin lattice models (nearest-neighbor, power-law, IQP) and fermionic systems
• Trotter Formulas: First, second, and high-order product formulas with JAX acceleration
• Error Bounds: Analytical and numerical bounds for Trotter error analysis
• States: Entangled states (GHZ, W) and random states
• Channels: Quantum noise channels and transformations
• Utilities: Binary search for optimal Trotter steps, plotting tools

Installation

From PyPI

pip install qsimkit

From source (for development)

git clone https://github.com/Jue-Xu/Qsimkit.git
cd Qsimkit
pip install -e .

Requirements

• Python >= 3.10
• Core: numpy, scipy, qiskit, matplotlib
• Optional: jax/jaxlib (GPU acceleration), openfermion (fermionic systems)

Note: The package includes heavy dependencies (qiskit, openfermion). Installation may take a few minutes.

Quick Start

import qsimkit
from qsimkit.spin import Nearest_Neighbour_1d
from qsimkit.trotter import pf
from qsimkit.bounds import tight_bound

# Create a spin Hamiltonian
H = Nearest_Neighbour_1d(n=4, Jx=1.0, Jy=1.0, Jz=1.0, hx=0.5)
h_list = H.ham  # Get Hamiltonian terms

# Compute Trotter approximation
t = 1.0  # Evolution time
r = 100  # Number of Trotter steps
U_approx = pf(h_list, t, r, order=2)

# Estimate error bound
error = tight_bound(h_list, order=2, t=t, r=r, type='spectral')
print(f"Error bound: {error}")

Usage Examples

1. Hamiltonian Models

from qsimkit import spin, fermion

# Nearest-neighbor 1D spin chain
nn_chain = spin.Nearest_Neighbour_1d(
    n=10,           # 10 qubits
    Jx=1.0,         # X coupling
    Jy=1.0,         # Y coupling
    Jz=1.0,         # Z coupling
    hx=0.5,         # X field
    pbc=False       # Open boundary
)

# Get Hamiltonian in different groupings
h_parity = nn_chain.ham_par  # Parity grouping
h_xyz = nn_chain.ham_xyz      # XYZ grouping

# 2D lattice
lattice_2d = spin.Nearest_Neighbour_2d(nx=4, ny=4, Jx=1.0, Jy=1.0, Jz=1.0)

# Cluster Ising model
cluster = spin.Cluster_Ising(n=8, J=1.0, g=0.5)

2. Trotter Methods

from qsimkit.trotter import pf, pf_high

# First-order Trotter
U1 = pf(h_list, t=1.0, r=50, order=1)

# Second-order Trotter (symmetric)
U2 = pf(h_list, t=1.0, r=50, order=2)

# High-order Trotter (4th, 6th, 8th order)
U4 = pf_high(h_list, t=1.0, r=50, order=4)
U6 = pf_high(h_list, t=1.0, r=50, order=6)

# With JAX acceleration (if available)
U2_jax = pf(h_list, t=1.0, r=50, order=2, use_jax=True)

3. Error Bounds

from qsimkit.bounds import (
    tight_bound,
    analytic_bound,
    interference_bound,
    lc_tail_bound
)

# Tight commutator-based bound
error = tight_bound(h_list, order=2, t=1.0, r=100, type='spectral')

# Analytical bound
error_analytic = analytic_bound(H=h_list, k=1, t=1.0, r=100)

# Two-term interference bound (Layden 2022)
if len(h_list) == 2:
    bound, e1, e2, e3 = interference_bound(h_list, t=1.0, r=100)

4. Finding Optimal Trotter Steps

todo

Package Structure

qsimkit/
├── spin.py           # Spin Hamiltonian models
├── fermion.py        # Fermionic Hamiltonians
├── states.py         # Quantum states (GHZ, W, random)
├── trotter.py        # Trotter-Suzuki product formulas
├── bounds.py         # Error bound calculations
├── measure.py        # Operators (commutator, norm, etc.)
├── channel.py        # Quantum channels
├── utils.py          # Utility functions
└── plot_config.py    # Plotting configuration

Documentation

More examples and tutorials: https://jue-xu.github.io/cookbook-quantum-simulation

Citation

todo

Development

Running Tests

pytest tests/

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

Apache License 2.0 - see LICENSE file for details.

Contact

• Author: Jue Xu
• Email: [e-mail]
• GitHub: @Jue-Xu