Frequently Asked Questions

Q-Alchemy is a product of data cybernetics. It connects classical data loading, circuit compression, and result extraction: prepare a quantum state from a classical description, reduce circuit costs for the intended inputs, and recover useful information from measurements. The Feasibility Suite brings these tools together to assess a complete workload.

• Explore the workflow

Create an account in the Q-Alchemy Portal, then create an API key under Settings → API Keys. Install the q-alchemy-sdk-py package and set the Q_ALCHEMY_API_KEY environment variable. The SDK guide includes examples for preparing a state, fetching a circuit, and checking the result.

• Getting started guide
• Python SDK examples

Yes. The Python SDK includes Qiskit and PennyLane integrations and can return a preparation circuit directly as OpenQASM. OpenQASM 2 is the default; OpenQASM 3 output is available as an experimental option. Choose the integration that fits your workflow and compile the resulting circuit for your target device.

• SDK integrations

Provide a normalized vector of real or complex amplitudes describing the target quantum state. The SDK accepts lists, NumPy arrays, SciPy sparse arrays, and Qiskit Statevector inputs through the relevant entry points. For n qubits, the vector describes 2ⁿ basis states; sparse input represents only occupied entries. The SDK examples show how to pad and normalize classical data.

• Input arguments and options
• Worked examples

The free service includes state preparation up to 12 qubits and simulator access with 4 MB RAM. For commercial use, choose platform pay-as-you-go access for your own workloads or OEM licensing for integration into your products. OEM access covers state preparation, readout, circuit compression, the simulator, and the Feasibility Suite.

• Compare access options
• Discuss business access

Yes. Platform-based pay-as-you-go access is available for non-OEM use, so your team can use Q-Alchemy for its own workloads. IBM Quantum access is also available with BYOL (bring your own licence). Contact us to discuss platform access, your IBM connection, and usage pricing. OEM licensing is for integrating the suite into products or services you provide to your own customers.

• Pricing and access
• Discuss platform access

Q-Tucker uses correlations in the input to group qubits, then decomposes the state into a residual core and local factors. Repeating this step transfers structure into circuit layers. When the core is close enough to the all-zero state, its preparation can be omitted and the extracted factors are assembled in reverse order. The 2026 paper explains the method and illustrates the accuracy–depth tradeoff.

• Visual Algorithm overview
• Read the Q-Tucker paper

Use max_fidelity_loss to set the allowed state-preparation approximation. Allowing some loss can reduce circuit cost, but the benefit depends on your input and method. This parameter is an algorithmic target, not a prediction of hardware fidelity. Compare the returned loss estimate and circuit metrics, then check the observable, energy, or reconstruction accuracy your application actually needs.

• Accuracy and verification
• Configure fidelity loss

AUTO compares iterative and hierarchical Tucker candidates that fit its resource limits. It uses reported fidelity-loss estimates and circuit costs to select a candidate. If none meets the requested loss budget, it returns a best-effort result with the smallest reported loss. Inspect the selection report and validate the circuit when possible. To use iterative-only controls such as factor size or iteration limits, select the iterative method explicitly.

• Automatic selection explained
• Method-specific options

The achievable reduction depends on structure, the allowed approximation, and the computation. Sparse amplitudes and low tensor rank can help, but they are different properties and neither guarantees a cheap full workflow. A dense n-qubit input still has 2ⁿ amplitudes. Assess preparation cost, circuit execution, and extraction together rather than inferring feasibility from qubit count alone.

• Practical limits of the method
• Dense-statevector memory baseline

State preparation starts with a classical description of the state you want and produces a circuit to prepare it. Circuit compression starts with an existing computation and seeks a cheaper circuit implementation while preserving its behavior for the intended inputs. In both cases, assess the resulting circuit cost and validate the relevant output quality.

• Loading, compression, and extraction

The public release is scheduled for November 2026. The Feasibility Suite is available now for businesses, and customers can request early access ahead of the public release. Contact us with your use case to arrange access.

• Request early access or business access
• Explore the Feasibility Suite

It assesses whether a workload can execute within the available classical and quantum resources, and separately checks whether its results meet your quality targets. It brings together state preparation, circuit compression, simulation, execution, and extraction evidence. The report identifies resource constraints and distinguishes hardware results, simulation results, and estimates.

• What an assessment covers

Bring the state you want to prepare, the computation you want to run, and the measurements or outputs you need. Specify the classical resource budget, any quantum resources available to you, and a quality target such as energy accuracy, observable error, or reconstruction fidelity. If you are still defining these, describe your use case when requesting access so we can help scope the evaluation.

• Discuss your workload

The default workflow assesses classical execution first. If the workload fits the classical resource budget, it selects that route and reports quality separately. Missing an accuracy target alone does not trigger a quantum job. When classical execution is resource-infeasible, the suite can assess and use a configured physical quantum device. An explicit comparison mode can collect evidence from both paths, and an offline mode supports assessments without quantum execution.

• Execution paths and comparison mode

A feasibility result says whether the assessed computation can run within the specified resources. It does not by itself show that a quantum approach is faster, cheaper, or more accurate than a suitable classical alternative. Result quality is a separate finding, and any advantage claim needs a fair comparison for the same task, including preparation, execution, and extraction costs.

• Understand resource and quality findings

The Q-Alchemy Console is the browser interface we are developing for the Feasibility Suite. It brings workload inputs, jobs, execution history, and reports together, with a plain-language verdict and the supporting metrics. Contact us about the current business and early-access workflow; the Console is still in development.

• Console and suite overview

Yes, when classical resources permit. The SDK includes a sparse statevector simulator for the prepare → simulate → verify workflow. Compare its result with your target state or relevant observables. Sparse simulation can save memory while few amplitudes are populated, but intermediate states can become dense. Limiting retained amplitudes makes the simulation approximate, so account for that when interpreting the comparison.

• Sparse simulator and verification examples

Hardware returns measurement data rather than a complete amplitude vector. Q-Alchemy uses selected measurements to estimate observables and, when configured, fit a compact state model. That reconstruction depends on the measurements, model assumptions, and noise; it should be checked against independent measurements or an appropriate reference. A simulated statevector and a model reconstructed from hardware measurements are different kinds of evidence.

• Preparation, measurement, and reconstruction

Yes. Our quantum chemistry campaign connected molecular-state preparation, IBM hardware measurements, and compact state reconstruction across twelve molecular systems. The solution page reports the individual results and limitations, including where reconstruction quality fell short. This hardware campaign is separate from the numerical state-preparation study in the Q-Tucker paper.

• Hardware results and limitations
• An introduction to the chemistry workflow

Start with the documentation for setup, SDK examples, algorithm details, and configuration options. For a workload evaluation, larger state-preparation jobs, or Feasibility Suite access, contact the data cybernetics team with your intended application, resource constraints, and success criteria.

• Browse the documentation
• Contact the team