zilver 0.3.2

Open quantum simulation network for Apple Silicon

Zilver

Open quantum simulation network for Apple Silicon.

Zilver turns your Apple Silicon Mac into a quantum simulation node.

Built on MLX — Apple's machine learning framework for macOS. Requires an Apple Silicon Mac (M1 or later) running macOS. MLX uses Apple's Metal GPU and unified memory architecture; it does not run on Linux, Windows, or Intel Macs.

Statevector, density matrix, and tensor network backends.

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Run a node

Got an Apple Silicon Mac? You can contribute compute to the network.

pip install "zilver[network]"
zilver-node start --registry https://registry.siriusquantum.com

See NODES.md for full requirements and how to apply for operator approval.

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Submit jobs

Researchers and AI labs can submit quantum simulation jobs to the network via API. No infrastructure required — bring your circuit, we handle the compute.

To request API access, open an issue at github.com/Sirius-Quantum/zilver with your use case and institution. Once approved you will receive a client API key and the registry endpoint.

from zilver.client import NetworkCoordinator
from zilver.node import SimJob

coord = NetworkCoordinator(
    "https://registry.siriusquantum.com",
    client_api_key="your-api-key",
)

job = SimJob(
    circuit_ops=[{"type": "ry", "qubits": [0], "param_idx": 0}],
    n_qubits=4, n_params=1, params=[1.57], backend="sv",
)
result = coord.submit(job)
print(result.expectation)
print(result.verify(job))   # True

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Local simulation

Use Zilver as a standalone quantum simulator on any Apple Silicon Mac — no network or account required.

pip install zilver

MLX is installed automatically as a dependency. No separate download needed.

from zilver.circuit import hardware_efficient
from zilver.landscape import LossLandscape

circuit = hardware_efficient(n_qubits=6, depth=3)
result = LossLandscape(circuit, sweep_params=(0, 1), resolution=20).compute()

print(f"Plateau coverage:   {result.plateau_coverage():.1%}")
print(f"Trainability score: {result.trainability_score():.3f}")
print(f"Wall time:          {result.wall_time_seconds:.3f}s")

Parameter-shift gradients, fully batched:

from zilver.circuit import hardware_efficient
from zilver.gradients import param_shift_gradient
import mlx.core as mx

circuit = hardware_efficient(n_qubits=4, depth=2)
f = circuit.compile(observable="sum_z")
params = mx.zeros([circuit.n_params])
grads = param_shift_gradient(f, params)

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License

Apache 2.0. See LICENSE.

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Manifesto

Read the Sirius Quantum Manifesto