Getting started

quantlop is a high-performance quantum simulator for evolving systems whose Hamiltonians can be expressed as sparse sums of Pauli words. It applies those operators directly to the state vector and evaluates the matrix-exponential action with either an adaptively scaled, truncated Taylor series or a Lanczos-Krylov subspace projection, avoiding the memory cost of constructing a dense Hamiltonian.

This page walks you through installing quantlop, defining a qubit Hamiltonian in the Pauli basis, preparing an initial state, and running your first matrix-free simulation.

Installation

Create or activate a Python virtual environment, then install the latest release from PyPI:

pip install quantlop

Working on quantlop itself?

Clone the repository and install with its development and documentation tools:

pip install -e .[dev,docs]

Quick example

Here is a simple code example using quantlop native data structures:

import numpy as np
import quantlop as ql

num_qubits = 3

pwords = [
    ql.PauliWord(coeff=0.5, string="ZZI"),
    ql.PauliWord(coeff=0.2, string="YIX"),
]
ham = ql.Hamiltonian(pwords=pwords)

psi = np.zeros(2**num_qubits, dtype=complex)
psi[0] = 1.0

evolved_psi = ql.evolve_higham(ham, psi)
# or
evolved_psi = ql.evolve_krylov(ham, psi)

Both algorithms select their Taylor truncation or Krylov dimension automatically. The default relative tolerance is 1e-9. Smaller values generally improve accuracy at the cost of more computation. The tolerance guides the internal approximation rather than measuring the final error directly.

The interface allows to import Hamiltonians from other quantum computing frameworks using from_pennylane() and from_qiskit().

By default, evolution is serial. To enable multi-threading, pass a non-zero positive integer as num_threads to request that many OpenMP threads. Passing "auto" selects the thread count reported by the operating system.

evolved_psi = ql.evolve_higham(ham, psi, num_threads=4)