Moonlab Python Bindings

Python interface for the Moonlab Quantum Simulator

Fast, feature-complete quantum computing in Python with PyTorch integration.

Quick Start

from moonlab import QuantumState

# Create Bell state (maximal entanglement)
state = QuantumState(2)
state.h(0).cnot(0, 1)

# Measure probabilities
probs = state.probabilities()
print(probs)  # [0.5, 0.0, 0.0, 0.5] - |00⟩ and |11⟩

Installation

Prerequisites

The published wheel is self-contained: the Python build pins QSIM_ENABLE_OPENMP=OFF (see bindings/python/pyproject.toml), so pip install moonlab needs no libomp install.

libomp is only relevant if you build libquantumsim yourself with OpenMP turned on (the top-level CMake default), e.g. for a non-Python build or local development against a custom CMake configuration:

# macOS with Apple Silicon
brew install libomp

# Linux
sudo apt-get install libomp-dev

Build & Install

# 1. Build C library
cd /path/to/moonlab
make

# 2. Install Python package
cd bindings/python
pip install -e .

# 3. Test installation
python test_moonlab.py

Features

Core Quantum Operations

• 32-qubit simulation (4.3 billion states)
• Complete universal gate set (H, X, Y, Z, CNOT, Toffoli, rotations)
• Bell inequality violation on explicit Bell states (CHSH ~ 2.87 measured at 10k samples on |Phi+>, vs the Tsirelson bound 2.828). The CHSH test now correctly honours whatever state you pass in -- the previous release silently overwrote the input with |Phi+>, making every CHSH result read 2.828 by fiat. Separable inputs now give CHSH ~ 0 as physics requires.
• SIMD-dispatched C core (AVX-512 / AVX2 / NEON / SVE) with an optional Metal GPU backend on Apple Silicon; see the reproducible-benchmark harness for host-specific numbers rather than a single multiplier

Quantum Algorithms

• VQE - Variational Quantum Eigensolver for molecular simulation, with native reverse-mode autograd (adjoint-method gradient) for the hardware-efficient ansatz in noise-free simulation
• QAOA - Quantum optimization (MaxCut, Ising models)
• Grover - Quantum search algorithm
• Bell Tests - CHSH, Mermin (3-qubit GHZ), and Mermin-Klyshko N-qubit nonlocality inequalities

Native Autograd (moonlab.diff)

Reverse-mode gradients for parameterised circuits, without a PyTorch dependency:

from moonlab import QuantumState
from moonlab.diff import DiffCircuit, PauliTerm, OBS_Z, OBS_X

circ = DiffCircuit(num_qubits=2).ry(0, 0.3).ry(1, -0.4).cnot(0, 1)
H = [PauliTerm(1.0, [0], [OBS_Z]),
     PauliTerm(0.5, [0, 1], [OBS_Z, OBS_Z])]

state = QuantumState(2)
circ.forward(state)
cost = DiffCircuit.expect_pauli_sum(state, H)
grad = circ.backward_pauli_sum(state, H)   # ndarray, shape (n_params,)

Supported gates: RX / RY / RZ / H / X / Y / Z / CNOT / CZ / CRX / CRY / CRZ.

Post-Quantum Cryptography (moonlab.crypto)

FIPS 202 SHA-3 / SHAKE and FIPS 203 ML-KEM (512 / 768 / 1024), with health-tested, Bell-gated, SHAKE256-conditioned RNG convenience wrappers:

from moonlab.crypto import sha3, mlkem

digest = sha3.sha3_256(b"quantum randomness")        # 32 bytes
stream = sha3.shake256(b"seed", outlen=1024)          # XOF

# Alice keygens with Moonlab's conditioned hybrid RNG
ek, dk = mlkem.keygen768_qrng()                       # 1184-byte pk, 2400-byte sk
# Bob encapsulates a shared secret
ct, K_bob = mlkem.encaps768_qrng(ek)                  # 1088-byte ciphertext
# Alice decapsulates
K_alice = mlkem.decaps768(ct, dk)
assert K_alice == K_bob                               # same 32-byte shared secret

All NIST SHA-3 / SHAKE known-answer vectors pass; ML-KEM is validated against the pq-crystals reference via AES-256-CTR_DRBG-derived NIST count=0 seed (see docs/security/pqc.md for the full threat model).

Quantum Machine Learning

• Feature Maps: Angle, Amplitude, IQP encoding
• Quantum Kernels: Exponential feature spaces
• QSVM: Quantum Support Vector Machine
• Quantum PCA: Principal component analysis
• PyTorch Integration: QuantumLayer with autograd

Examples

Basic Quantum Circuit

from moonlab import QuantumState, Gates

# Create 3-qubit GHZ state
state = QuantumState(3)
Gates.H(state, 0)
Gates.CNOT(state, 0, 1)
Gates.CNOT(state, 1, 2)

# Get state vector
sv = state.get_statevector()
print(f"|GHZ⟩ = {sv}")

Quantum Machine Learning

from moonlab.ml import QSVM, IQPEncoding
import numpy as np

# Prepare data
X_train = np.random.randn(50, 4)
y_train = np.random.choice([-1, 1], 50)

# Train Quantum SVM
qsvm = QSVM(num_qubits=4, feature_map='iqp')
qsvm.fit(X_train, y_train)

# Predict
y_pred = qsvm.predict(X_test)
accuracy = qsvm.score(X_test, y_test)
print(f"Accuracy: {accuracy:.1%}")

PyTorch Integration

import torch
import torch.nn as nn
from moonlab.torch_layer import QuantumLayer

# Build hybrid quantum-classical network
model = nn.Sequential(
    nn.Linear(28*28, 16),
    nn.Tanh(),
    QuantumLayer(num_qubits=16, depth=3),
    nn.Linear(16, 10)
)

# Train with standard PyTorch
optimizer = torch.optim.Adam(model.parameters())
for epoch in range(10):
    outputs = model(train_data)
    loss = criterion(outputs, labels)
    loss.backward()  # Quantum gradients via parameter shift!
    optimizer.step()

Quantum PCA

from moonlab.ml import QuantumPCA

# Dimensionality reduction with quantum advantage
qpca = QuantumPCA(num_components=2, num_qubits=3)
qpca.fit(X_highdim)
X_reduced = qpca.transform(X_highdim)

print(f"Explained variance: {qpca.explained_variance_}")

Advanced Usage

Custom Feature Maps

from moonlab.ml import QuantumFeatureMap
from moonlab import QuantumState

class CustomEncoding(QuantumFeatureMap):
    def encode(self, x, state):
        state.reset()
        for i, val in enumerate(x):
            state.ry(i, val)
            state.rz(i, val**2)
        # Add entanglement
        for i in range(state.num_qubits - 1):
            state.cnot(i, i+1)

Variational Quantum Circuits

from moonlab.ml import VariationalCircuit
from moonlab import QuantumState

circuit = VariationalCircuit(num_qubits=8, num_layers=4)
state = QuantumState(8)
circuit(state)  # Apply parameterized circuit

Quantum Kernels

from moonlab.ml import QuantumKernel, IQPEncoding

# Create quantum kernel
encoder = IQPEncoding(num_qubits=4, num_layers=2)
kernel = QuantumKernel(encoder)

# Compute kernel matrix
K = kernel.compute_matrix(X_train)

# Use in any kernel method (SVM, Ridge, etc.)
from sklearn.svm import SVC
svm = SVC(kernel='precomputed')
svm.fit(K, y_train)

Applications

Drug Discovery (VQE)

from moonlab.algorithms import VQE

# Simulate H₂ molecule
vqe = VQE(num_qubits=4, num_layers=3)
result = vqe.solve_h2(bond_distance=0.74)
print(f"Ground state energy: {result['energy']:.6f} Ha")
print(f"Converged: {result['converged']}")

Graph Optimization (QAOA)

from moonlab.algorithms import QAOA

# Solve MaxCut problem on a 5-vertex graph
qaoa = QAOA(num_qubits=5, num_layers=3)
result = qaoa.solve_maxcut(
    edges=[(0,1), (1,2), (2,3), (3,4), (4,0), (0,2)]
)
print(f"Best cut: {bin(result['best_bitstring'])}")
print(f"Cut value: {result['best_cost']}")

Few-Shot Learning

from moonlab.torch_layer import QuantumClassifier

# Quantum classifier for small datasets
model = QuantumClassifier(
    num_features=16,
    num_qubits=8,
    num_classes=5,
    depth=3
)

# Train on small dataset (quantum advantage!)
train_with_few_samples(model, X_train_small, y_train_small)

Performance

Operation	Speed	Notes
20-qubit circuit	<1ms	SIMD + parallel optimized
VQE H₂ molecule	2-5s	Chemical accuracy
QAOA 10-vertex MaxCut	10-30s	Near-optimal solutions
Quantum kernel (n=100)	5-15s	Exponential feature space

vs Other Frameworks

Framework	Speed (rel.)	Features	Apple Silicon
Moonlab	1.0× (fastest)	Complete	✅ Optimized
Qiskit	10-50× slower	Excellent	⚠️ Not optimized
Cirq	15-40× slower	Good	⚠️ Not optimized

Testing

# Run test suite
python test_moonlab.py

# Tests include:
# - Core quantum operations
# - Quantum ML algorithms
# - PyTorch integration
# - End-to-end workflows

API Reference

moonlab.core

• QuantumState(num_qubits) - Quantum state vector

  • Methods: h(), x(), y(), z(), cnot(), rx(), ry(), rz()
  • Properties: probabilities(), get_statevector()

• Gates - Static gate interface

  • Gates.H(state, qubit), Gates.CNOT(state, c, t), etc.

moonlab.ml

• AngleEncoding - Simple rotation-based encoding
• AmplitudeEncoding - Exponential data compression
• IQPEncoding - Quantum kernel feature map
• QuantumKernel - Kernel computation K(x,x') = |⟨φ(x)|φ(x')⟩|²
• QSVM - Quantum Support Vector Machine
• QuantumPCA - Quantum Principal Component Analysis

moonlab.torch_layer

• QuantumLayer - Parameterized quantum circuit as nn.Module
• QuantumClassifier - Complete quantum classifier
• HybridQNN - Hybrid quantum-classical network
• VariationalCircuit - General variational ansatz

moonlab.algorithms

• VQE - Variational Quantum Eigensolver
• QAOA - Quantum Approximate Optimization
• Grover - Quantum search
• BellTest - CHSH inequality verification

Contributing

See CONTRIBUTING.md for development guidelines.

License

MIT License - See LICENSE file.

Links

• Documentation: https://github.com/tsotchke/moonlab
• GitHub: https://github.com/tsotchke/moonlab
• Issues: https://github.com/tsotchke/moonlab/issues

Citation

If you use Moonlab in research, please cite:

@software{moonlab2026,
  title={Moonlab: High-Performance Quantum Computing for Apple Silicon},
  author={Tsotchke},
  year={2026},
  url={https://github.com/tsotchke/moonlab}
}

Support

• Issues: https://github.com/tsotchke/moonlab/issues
• Email: support@tsotchke.ai

References

This library implements algorithms from the following foundational works:

Quantum Computing:

• Nielsen, M. A. & Chuang, I. L. (2010). Quantum Computation and Quantum Information. Cambridge University Press.

Variational Algorithms:

• Peruzzo, A. et al. (2014). "A variational eigenvalue solver on a photonic quantum processor." Nat. Commun. 5, 4213.
• Farhi, E., Goldstone, J., & Gutmann, S. (2014). "A quantum approximate optimization algorithm." arXiv:1411.4028.

Quantum Machine Learning:

• Schuld, M. & Petruccione, F. (2021). Machine Learning with Quantum Computers. Springer.
• Benedetti, M. et al. (2019). "Parameterized quantum circuits as machine learning models." Quantum Sci. Technol. 4, 043001.

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Historical: what shipped in v0.3.0

This package is currently at v1.2.0 (stable ABI 0.6.0); see CHANGELOG.md at the repo root and docs/PARITY_MATRIX.md for the full v0.4-v1.1 history, including the v1.1 GPU (CUDA) state API, control-plane job scheduling, and QRNG status surface added since the notes below. The v0.3 highlights are kept here because the module-level docs they reference (docs/reference/qgt-api.md, docs/reference/mpdo-api.md) are unchanged since:

Quantum geometric tensor and topology (moonlab.topology):

• chern_qwz_proj(m, N), chern_qwz_parallel_transport(m, N) — gauge-invariant projector-trace and parallel-transport-gauge Chern integrators on the Qi-Wu-Zhang model.
• kane_mele_z2(t, lambda_so, lambda_r, lambda_v, N) — 4-band Z_2 invariant via Fukui-Hatsugai (2007).
• bhz_z2(A, B, M, N) — HgTe quantum-well topological insulator (Bernevig-Hughes-Zhang 2006).
• kitaev_chain_z2(t, mu, delta) — 1D BdG Z_2 from Pfaffian-sign product at the time-reversal-invariant momenta (Kitaev 2001).
• hofstadter_chern(p, q, n_occupied, t, N) — magnetic-Bloch sub-band Chern numbers (Hofstadter 1976).

Matrix-product density operator noise simulator (moonlab.mpdo.Mpdo):

• Polynomial-cost noisy-circuit simulation per Verstraete, Garcia- Ripoll, and Cirac (Phys. Rev. Lett. 93, 207204, 2004).
• Six named single-qubit Kraus channels (depolarising, amplitude damping, phase damping, bit / phase / bit-phase flip).
• User-supplied Kraus operators via NumPy complex arrays.
• Pauli expectation values (string or integer Pauli code).

Other v0.3 additions:

• moonlab.var_d_run, moonlab.var_d_run_v2 — CA-MPS variational-D with convergence_eps.
• All v0.2 noise channels and Bell-variants harness remain available.

MOONLAB_LIB_PATH and MOONLAB_LIB_DIR environment variables now override the dylib search path (parity with the Rust binding's MOONLAB_LIB_DIR). See docs/reference/qgt-api.md and docs/reference/mpdo-api.md for the corresponding C ABI contracts, and docs/tutorials/{topological_band_structure,mpdo_noise}.md for worked examples.

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Current release: v1.2.0 (ABI 0.6.0)