scaluq 0.1.0

Quantum circuit simulator for research

Scaluq

For the Japanese version of this README, see README_ja.md.

Scaluq is a newly redeveloped Python/C++ library based on the quantum circuit simulator Qulacs.
It enables high-speed simulation of large-scale quantum circuits, noisy quantum circuits, and parametric quantum circuits.
This library is released under the MIT License.

Compared to Qulacs, the following improvements have been made:

• Implementation based on Kokkos allows seamless switching between execution environments (CPU/GPU) without requiring code changes.
• Improved execution speed.
• Pointers are hidden from users, making the code simpler and safer to write.
• Integration of nanobind enables more compact and faster Python bindings.
• Provides a faster interface for the case where the same circuit is applied to multiple quantum states.

Build Requirements

• Ninja ≥ 1.10
• GCC ≥ 11 (≥ 13 if not using CUDA)
• CMake ≥ 3.24
• CUDA ≥ 12.6 (only when using CUDA)
• Python ≥ 3.9 (only when using Python)

Note: It may work with lower versions, but this has not been verified.

Runtime Requirements

• CUDA ≥ 12.6 (only when using CUDA)

Note: It may work with lower versions, but this has not been verified.

Build Options

Build options can be specified using environment variables when running script/configure or pip install ..

Variable Name	Default	Description
CMAKE_C_COMPILER	-	See CMake Documentation
CMAKE_CXX_COMPILER	-	See CMake Documentation
CMAKE_BUILD_TYPE	-	See CMake Documentation
CMAKE_INSTALL_PREFIX	-	See CMake Documentation
SCALUQ_USE_OMP	ON	Use OpenMP for parallel computation on CPU
SCALUQ_USE_CUDA	OFF	Enable parallel computation using GPU (CUDA)
SCALUQ_CUDA_ARCH	(auto)	Target Nvidia GPU architecture (see Kokkos CMake Keywords, e.g., SCALUQ_CUDA_ARCH=AMPERE80)
SCALUQ_USE_TEST	ON	Include test/ in build targets. You can build and run tests with ctest --test-dir build/
SCALUQ_USE_EXE	ON	Include exe/ in build targets. You can try running without installing by building with ninja -C build and running build/exe/main
SCALUQ_FLOAT16	OFF	Enable f16 precision
SCALUQ_FLOAT32	ON	Enable f32 precision
SCALUQ_FLOAT64	ON	Enable f64 precision
SCALUQ_BFLOAT16	OFF	Enable bf16 precision

Installing as a C++ Library

To install Scaluq as a static C++ library, run the following commands:

git clone https://github.com/qulacs/scaluq
cd scaluq
script/configure
sudo -E env "PATH=$PATH" ninja -C build install

• Required libraries such as Eigen and Kokkos will be installed together.
• You can install to a location other than /usr/local/ by setting CMAKE_INSTALL_PREFIX. For example, if you want to install locally or avoid conflicts with other Kokkos builds:
  CMAKE_INSTALL_PREFIX=~/.local script/configure; ninja -C build install
• sudo is used to install files to /usr/local/, but to preserve the user environment, we use -E and explicitly pass PATH.
• If you want to build the CUDA-enabled version (when NVIDIA GPU and CUDA are available), set SCALUQ_USE_CUDA=ON. Example:
  SCALUQ_USE_CUDA=ON script/configure; sudo env -E "PATH=$PATH" ninja -C build install

When changing options and rebuilding, make sure to clear the CMake cache by running:

rm build/CMakeCache.txt

An example CMake configuration for a project using the installed Scaluq library is provided in example_project/.

Installing as a Python Library

Scaluq can also be used as a Python library:

pip install scaluq

If you want to use GPU or precisions other than f32 and f64, clone the repository and install with options:

git clone https://github.com/qulacs/scaluq
cd ./scaluq
SCALUQ_USE_CUDA=ON pip install .

Python Documentation

A simple documentation page is available with function descriptions and type information for the Python library version:
https://scaluq.readthedocs.io/en/latest/index.html

Sample Code (C++)

#include <iostream>
#include <cstdint>

#include <scaluq/circuit/circuit.hpp>
#include <scaluq/gate/gate_factory.hpp>
#include <scaluq/operator/operator.hpp>
#include <scaluq/state/state_vector.hpp>

int main() {
    scaluq::initialize();  // must be called before using any scaluq methods
    {
        constexpr Precision Prec = scaluq::Precision::F64;
        constexpr ExecutionSpace Space = scaluq::ExecutionSpace::Default;
        const std::uint64_t n_qubits = 3;
        scaluq::StateVector<Prec, Default> state = scaluq::StateVector<Prec, Default>::Haar_random_state(n_qubits, 0);
        std::cout << state << std::endl;

        scaluq::Circuit<Prec, Default> circuit(n_qubits);
        circuit.add_gate(scaluq::gate::X<Prec, Default>(0));
        circuit.add_gate(scaluq::gate::CNot<Prec, Default>(0, 1));
        circuit.add_gate(scaluq::gate::Y<Prec, Default>(1));
        circuit.add_gate(scaluq::gate::RX<Prec, Default>(1, std::numbers::pi / 2));
        circuit.update_quantum_state(state);

        scaluq::Operator<Prec, Default> observable(n_qubits);
        observable.add_random_operator(1, 0);
        auto value = observable.get_expectation_value(state);
        std::cout << value << std::endl;
    }
    scaluq::finalize();  // must be called last
}

By including scaluq/all.hpp, you can omit template arguments using SCALUQ_OMIT_TEMPLATE.

#include <iostream>
#include <cstdint>

#include <scaluq/all.hpp>

namespace my_scaluq {
    SCALUQ_OMIT_TEMPLATE(scaluq::Precision::F64, scaluq::ExecutionSpace::Default)
}

using namespace my_scaluq;

int main() {
    scaluq::initialize();  // must be called before using any scaluq methods
    {
        const std::uint64_t n_qubits = 3;
        StateVector state = StateVector::Haar_random_state(n_qubits, 0);
        std::cout << state << std::endl;

        Circuit circuit(n_qubits);
        circuit.add_gate(gate::X(0));
        circuit.add_gate(gate::CNot(0, 1));
        circuit.add_gate(gate::Y(1));
        circuit.add_gate(gate::RX(1, std::numbers::pi / 2));
        circuit.update_quantum_state(state);

        Operator observable(n_qubits);
        observable.add_random_operator(1, 0);
        auto value = observable.get_expectation_value(state);
        std::cout << value << std::endl;
    }
    scaluq::finalize();  // must be called last
}

Sample Code (Python)

from scaluq.default.f64 import *
import math

n_qubits = 3
state = StateVector.Haar_random_state(n_qubits, 0)

circuit = Circuit(n_qubits)
circuit.add_gate(gate.X(0))
circuit.add_gate(gate.CNot(0, 1))
circuit.add_gate(gate.Y(1))
circuit.add_gate(gate.RX(1, math.pi / 2))
circuit.update_quantum_state(state)

observable = Operator(n_qubits)
observable.add_random_operator(1, 0)
value = observable.get_expectation_value(state)
print(value)

Specifying Precision and Execution Space

Scaluq supports multiple floating-point precisions: f16, f32, f64, and bf16.
By default, only f32 and f64 are enabled.
While f64 is generally recommended, lower precisions like f32 can be up to 2–4x faster in applications such as quantum machine learning that do not require high precision.

Precision	C++ Template Argument	Python Submodule	Description
f16	Precision::F16	f16	IEEE754 binary16
f32	Precision::F32	f32	IEEE754 binary32
f64	Precision::F64	f64	IEEE754 binary64
bf16	Precision::BF16	bf16	bfloat16

Execution spaces determine whether computation is performed on CPU or GPU:

Execution Space	C++ Template Argument	Python Submodule	Description
default	ExecutionSpace::Default	default	GPU if CUDA is enabled, otherwise CPU
host	ExecutionSpace::Host	host	Always CPU

Note: You can only perform operations between objects with the same precision and execution space. For example, a gate created for 32-bit precision cannot be used with a 64-bit StateVector, even if both are CPU-based.

In C++, classes like StateVector, Circuit, Gate, and Operator accept Precision and ExecutionSpace as template arguments.

In Python, you import from submodules like scaluq.default.f32 or scaluq.host.f64 based on your desired configuration.

You can dynamically select the submodule using importlib:

import importlib

prec = 'f64'
space = 'default'
scaluq_sub = importlib.import_module(f'scaluq.{space}.{prec}')
StateVector = scaluq_sub.StateVector
gate = scaluq_sub.gate

state = StateVector(3)
x = gate.X(0)
x.update_quantum_state(state)
print(state)