Q-Alchemy Python SDK

This is the Python-SDK for using the data cybernetics Q-Alchemy API which helps quantum computing researchers to put classical data into the quantum computer. This is all also called: the loading problem, encoding problem, or quantum state preparation. Some people also call it a form of QRAM, or quantum random-access memory.

Under the hood, Q-Alchemy runs on PineXQ, the hypermedia (Siren) API platform of data cybernetics. You do not need to know anything about it to use this SDK; if you want to work with the API directly, see the PineXQ documentation.

Installation

The SDK is published on PyPI, so you can install it with pip (or poetry, uv, pdm, ...):

pip install q-alchemy-sdk-py
# or
uv add q-alchemy-sdk-py
# or
pdm add q-alchemy-sdk-py

If you want to use the qiskit-integration, please use

pip install q-alchemy-sdk-py[qiskit]

And if you want the PennyLane-integration, please use

pip install q-alchemy-sdk-py[pennylane]

If you would like to run our examples, please use

pip install q-alchemy-sdk-py[examples]

We use uv and have tested this all with Python 3.11 through 3.14. So the way to install it after cloning is simply

uv sync --locked

Again, for qiskit- or PennyLane-integrations, please add the groups

uv sync --locked --extra qiskit --extra pennylane

And for running our examples,

uv sync --locked --extra examples

Usage

There are examples under the /examples folder, but for those that are eager to find out, here it is. First, you will want to get an API key from the Q-Alchemy Portal. You need to sign up for this, sorry, but this is necessary. Once you have the API key (free of charge of course) you can test it!

Direct Example

import numpy as np
import os
from sklearn.datasets import fetch_openml

from q_alchemy.initialize import q_alchemy_as_qasm

mnist = fetch_openml('mnist_784', version=1, parser="auto")

zero: np.ndarray = mnist.data[mnist.target == "0"].iloc[0].to_numpy()
filler = np.empty(2 ** 10 - zero.shape[0])
filler.fill(0)

zero = np.hstack([zero, filler])
zero = zero / np.linalg.norm(zero)

qasm, summary = q_alchemy_as_qasm(zero, max_fidelity_loss=0.2, 
    api_key=os.environ["Q_ALCHEMY_API_KEY"], return_summary=True)
print(summary)

Qiskit Example

import numpy as np
from sklearn.datasets import fetch_openml
import os

from q_alchemy.qiskit_integration import QAlchemyInitialize, OptParams

mnist = fetch_openml('mnist_784', version=1, parser="auto")

zero: np.ndarray = mnist.data[mnist.target == "0"].iloc[0].to_numpy()
filler = np.empty(2 ** 10 - zero.shape[0])
filler.fill(0)

zero = np.hstack([zero, filler])
zero = zero / np.linalg.norm(zero)

instr = QAlchemyInitialize(
    params=zero.tolist(),
    opt_params=OptParams(
        max_fidelity_loss=0.1,
        basis_gates=["id", "rx", "ry", "rz", "cx"],
        api_key=os.environ["Q_ALCHEMY_API_KEY"]
    )
)
instr.definition.draw(fold=-1)

PennyLane Example

import numpy as np
import pennylane as qml
from sklearn.datasets import fetch_openml
import os

from q_alchemy.pennylane_integration import QAlchemyStatePreparation, OptParams

mnist = fetch_openml('mnist_784', version=1, parser="auto")

zero: np.ndarray = mnist.data[mnist.target == "0"].iloc[0].to_numpy()
filler = np.empty(2 ** 10 - zero.shape[0])
filler.fill(0)

zero = np.hstack([zero, filler])
zero = zero / np.linalg.norm(zero)

dev = qml.device('lightning.qubit', wires=10)

@qml.qnode(dev)
def circuit(state=None):
    QAlchemyStatePreparation(
        state,
        wires=range(10),
        opt_params=OptParams(
            max_fidelity_loss=0.1,
            basis_gates=["id", "rx", "ry", "rz", "cx"],
            api_key=os.environ["Q_ALCHEMY_API_KEY"]
        )
    )
    return qml.state()

print(qml.draw(circuit, level="device", max_length=100)(zero.tolist()))

Broadcasting with PennyLane

PennyLane provides native support for broadcasting, which allows quantum nodes to process batches of inputs efficiently. This is particularly useful in machine learning applications where inputs often come in batches. When broadcasting is used in conjunction with Q-Alchemy, each state in the batch is individually prepared using Q-Alchemy's circuit synthesis capabilities.

⚠️ Note: For simulators or backends that support native state initialization using the StatePrep gate—such as default.qubit, and lightning.qubit—the state vector is injected directly without any decomposition into quantum gates. In this case, Q-Alchemy is not used. This behavior is ideal for rapid prototyping and testing. Switching to a hardware backend (or one without native state prep) will automatically invoke Q-Alchemy for state preparation.

Broadcasting Example

import numpy as np
import pennylane as qml
import os
import torch

from q_alchemy.pennylane_integration import AmplitudeEmbedding, OptParams
from sklearn.datasets import make_moons

# Sample data
X, _ = make_moons(n_samples=5, noise=0.1)
X = X / np.linalg.norm(X, axis=1, keepdims=True)  # Normalize each row for amplitude embedding

# Create PennyLane device
dev = qml.device("qiskit.aer", wires=1)

@qml.qnode(dev, interface="torch")
def circuit(x):
    AmplitudeEmbedding(
        x,
        wires=[0],
        opt_params=OptParams(
            max_fidelity_loss=0.0,
            api_key=os.environ["Q_ALCHEMY_API_KEY"]
        )
    )
    return qml.expval(qml.PauliZ(0))

# Run the circuit on a batch of inputs
X_tensor = torch.tensor(X, dtype=torch.float64)
print(qml.draw(circuit, level="device", max_length=100)(X_tensor))

This example demonstrates how batched data can be processed using broadcasting with AmplitudeEmbedding, and how Q-Alchemy is triggered on simulators like qiskit.aer. When moving to real hardware or gate-based backends that lack StatePrep gate, Q-Alchemy will transparently handle the state preparation.

Advanced options

Every entry point (q_alchemy_as_qasm, QAlchemyInitialize, QAlchemyStatePreparation, AmplitudeEmbedding) takes its settings as an OptParams object, or as a plain dict with the same keys. q_alchemy_as_qasm also accepts them as keyword arguments. The state itself can be a list, a numpy array, a scipy sparse array or, for Qiskit, a Statevector.

The fields you are most likely to touch:

InitializationMethods lives in q_alchemy.initialize:

• AUTO (default) runs several Tucker candidates (iterative and hierarchical, as resources allow) at your fidelity budget and keeps the cheapest realized circuit. Trivial inputs such as single-qubit and single-basis states take an exact fast path.
• ITERATIVE_TUCKER and HIERARCHICAL_TUCKER pin one Tucker variant.
• SWAP_PIVOT is suited to very sparse states.
• BAA_LOW_RANK uses the BAA low-rank initializer; it is limited to 12 qubits.

Method-specific options (extra_kwargs)

extra_kwargs is passed through to the chosen method. Pass a dict, and the SDK serializes it for you:

from q_alchemy.initialize import OptParams, InitializationMethods

opt_params = OptParams(
    max_fidelity_loss=0.05,
    initialization_method=InitializationMethods.ITERATIVE_TUCKER,
    extra_kwargs={"max_iterations": 10, "factors_size": 4},
)

⚠️ Options are validated per method. A key that the chosen method does not accept fails the job with Could not build the initialization circuit: ... instead of being silently ignored. In particular, the iterative-Tucker keys below are not valid under AUTO. Pin the method to use them.

Two interactions worth knowing:

• Set the fidelity with max_fidelity_loss on OptParams, not in extra_kwargs. Every method accepts max_fidelity_loss in extra_kwargs too, but if you put it there it silently overrides the top-level value.
• Under AUTO, basis_gates on OptParams only affects the final transpilation. Candidates are compared on u/cx cost unless you also pass extra_kwargs={"basis_gates": [...]}.

Verifying preparation circuits with the sparse simulator

Q-Alchemy also hosts a sparse state-vector simulator so you can verify that a preparation circuit really produces your target state. The typical loop is prepare → simulate → verify:

from qiskit import QuantumCircuit
from qiskit.quantum_info import Statevector, state_fidelity
from q_alchemy import q_alchemy_as_qasm, SparseSimulator

qasm = q_alchemy_as_qasm(target, max_fidelity_loss=0.0)   # prepare
prep = QuantumCircuit.from_qasm_str(qasm)

sim = SparseSimulator()                                    # verify
sv = sim.sparse_statevector(prep)
print("fidelity:", state_fidelity(Statevector(sv.to_dense()), Statevector(target)))

SparseSimulator also exposes .counts(...) and .tomography(...), and it auto-selects your resource tier from your plan (Standard/Medium for everyone, XLarge for enterprise).

⚠️ The free plan is strongly limited — state preparation is capped (currently ~12 qubits, batches up to 100) and runs on the Medium simulator tier. Enterprise plans get larger circuits and the XLarge tier. See the limits table in the guide.

📖 Full guide: docs/initialize-and-verify.md · 🧪 Runnable notebook: examples/simulator_vs_initializer.ipynb

Using the simulator as a Qiskit backend

The hosted simulator is also exposed through Qiskit's standard BackendV2 interface — the same way IBM backends are used — so it drops straight into the Qiskit ecosystem (transpiler, Sampler, etc.):

from qiskit import QuantumCircuit, transpile
from q_alchemy import QAlchemyBackend

backend = QAlchemyBackend()                         # reads Q_ALCHEMY_API_KEY from env

qc = QuantumCircuit(2, 2)
qc.h(0); qc.cx(0, 1); qc.measure([0, 1], [0, 1])

job = backend.run(transpile(qc, backend), shots=4096)
print(job.result().get_counts())                    # {'00': ~2048, '11': ~2048}

backend.run(...) accepts Aer-style options (shots, seed_simulator, save_sparse_statevector, save_statevector, sparse_index_format, ...), and there's an IBM-style QAlchemyProvider().get_backend() for discovery. The resource tier (Medium vs the enterprise XLarge) is selected automatically from your plan — see the guide.

save_statevector vs save_sparse_statevector

Both ask for the state the circuit prepares, and both are served by the same remote call — the difference is what you get back, and how big it is.

save_statevector is the option you already know from Aer. It hands you the familiar dense 2**n vector, so Statevector, state_fidelity and friends all work unchanged:

qc = QuantumCircuit(2)
qc.h(0); qc.cx(0, 1)                                # no measurement: this is a state export

result = backend.run(qc, save_statevector=True).result()
print(result.data(0)["statevector"])
# [0.70710678+0.j 0.        +0.j 0.        +0.j 0.70710678+0.j]   -> length 2**n

save_sparse_statevector is Q-Alchemy's own, and it is the one that scales. It returns only the amplitudes the circuit actually populates, so nothing of size 2**n is ever built:

result = backend.run(qc, save_sparse_statevector=True).result()
print(result.data(0)["sparse_statevector"])
# {'format': 'sparse_statevector_v1', 'num_qubits': 2, 'nnz': 2,
#  'index_format': 'hex', 'index_convention': 'little_endian',
#  'indices': ['0x0', '0x3'],
#  'amplitudes': [[0.7071067811865476, 0.0], [0.7071067811865476, 0.0]]}

Note the amplitudes are [real, imag] pairs: this entry is the simulator's own JSON payload, passed through untouched. If you would rather have parsed complex values — plus to_coo(), to_arrow() and amplitudes_dict() — go through the client instead of the backend, which returns a typed SparseStatevectorResult:

from q_alchemy import SparseSimulator

sv = SparseSimulator().sparse_statevector(qc)
print(sv.amplitudes)          # [(0.7071067811865476+0j), (0.7071067811865476+0j)]
print(sv.amplitudes_dict())   # {'0x0': (0.707...+0j), '0x3': (0.707...+0j)}

indices and amplitudes line up element by element, and nnz is how many were stored — two here, not four. That gap is the whole point, and it widens fast: a 40-qubit state-preparation circuit populating a thousand basis states returns a thousand amplitudes, while the dense form would need 2**40 complex numbers, or roughly 17 TB. A dense export is impossible in that regime; a sparse one is a few hundred kilobytes. Standard Qiskit backends offer no equivalent.

That only helps if the state is sparse, and nothing forces it to be. By default the simulator keeps every amplitude above 1e-10, so the result is exact, but a circuit that populates most of its basis states needs as much memory as the dense form and can exhaust the simulator. Pass max_nnz=N (to backend.run or SparseSimulator.sparse_statevector) to cap it: after every gate only the N largest amplitudes are kept and renormalised. The result is then approximate, and nothing in it marks that it was truncated.

Ask for both together and you pay for one simulation:

result = backend.run(qc, save_statevector=True, save_sparse_statevector=True).result()
dense = result.data(0)["statevector"]               # 2**n numpy array
sparse = result.data(0)["sparse_statevector"]       # nnz entries

Use the dense form for small circuits you want to compare against Qiskit directly; use the sparse form whenever 2**n would not fit — which is exactly the regime this simulator exists for. Reach for sparse_index_format (hex/bitstring) and sparse_index_convention (little_endian/big_endian) to control how the indices are written, and see the guide for feeding a sparse result straight back into the loader via to_coo().

From PennyLane

Because it's a standard Qiskit backend, you can use it as a PennyLane device via the PennyLane–Qiskit plugin (whose original version was written by this SDK's author, Carsten Blank):

import pennylane as qml
from q_alchemy import QAlchemyBackend

dev = qml.device("qiskit.remote", wires=2, backend=QAlchemyBackend(), shots=4096)

@qml.qnode(dev)
def circuit():
    qml.Hadamard(0)
    qml.CNOT([0, 1])
    return qml.counts()

Contributions

We welcome contributions - simply fork the repository of this plugin, and then make a pull request containing your contribution. All contributers to this plugin will be listed as authors on the releases.

We also encourage bug reports, suggestions for new features and enhancements!

Authors

Carsten Blank

License

The q-alchemy-sdk-py is free and open source, released under the Apache License, Version 2.0.