DROPS IN FRONT OF THE DECODER YOU ALREADY RUN

No rip-and-replace. No retraining. No circuit changes.

The adaptor corrects the error model your decoder reads, before it decodes. The optimization and governance tools around it consume signals your stack already produces. All of it works alongside your current SDKs, decoders, and cloud environments.

QUBITBOOST IN A REAL QUANTUM STACK

Integrates around quantum execution without modifying circuits, decoders, or hardware.

User Workloads

QEC•VQE•QAOA•Calibration•Benchmarking

existing workflows

Quantum SDKs & Runtimes

Qiskit•Braket SDK•Cirq•PennyLane•Custom pipelines

unchanged execution

QUBITBOOST

Decoder Adaptor, decoding performance (private pilot)

Corrects the error model your decoder reads, lower logical error rate

DriftRadar, Observability

Observe + select the backend that is stable right now

LiveGate, Early-Flight

Abort runs at round 2 when the signal says they will not recover

SafetyGate, In-Flight

Trust / Defer decoded results

ChemGate, VQE Optimization

VQE cost reduction + safety gating

ShotValidator, QEC Shot Quality

Flag and drop corrupted QEC shots

Decisions are statistical, auditable, and reversible.

RESULT PASSPORT

v1.0

Provenance•Trust Score•Gate Scores•Policy Decision•HMAC Signature

Decoders & Control

• Production decoders

• MWPM, BP+OSD

• Custom decoders

Runs unchanged

We improve the model it reads, never its logic

Quantum Execution Platforms

IBM Fez•IBM Torino•IBM Marrakesh•Rigetti Ankaa-3•IonQ Aria•IonQ Forte•IQM Garnet•IQM Emerald

Observability

• Decisions

• Confidence intervals

• Cost impact

• Backend health

No circuit modification

Decoder-agnostic

Never makes the decoder worse

5 vendors, 8 backends validated

At the core, the adaptor corrects the error model your decoder reads so the decoder you already run reports a lower logical error rate. Around it, the optimization and governance tools consume evidence your stack already produces and intervene only when a confidence or cost threshold is crossed. Nothing changes your circuits or your decoder logic.

HARDWARE VALIDATED

Multiple gates hardware-validated on real quantum processors across vendors. All gates trained on real calibration data. No synthetic benchmarks.

IBM Quantum

Fez, Torino, Marrakesh

Hardware Validated

ChemGate: 44.2% eval reduction on IBM Fez caffeine 16q (n=8, Apr 2026), reaches HF energy. ShotValidator: 216K shots, 74% mean capture across 9 configs (6/9 hit 100%).

120 calibration snapshots

Rigetti

Ankaa-3

Hardware Demo

DriftRadar: single-round stub on Ankaa-3 (n=8K, beta). The earlier 57.3% SafetyGate Ankaa claim was withdrawn after the raw-data audit; multi-round QEC validation pending.

10 calibration snapshots

IonQ

Aria, Forte

Calibration Validated

MS gate, full connectivity. Result Passport signed end-to-end.

14 calibration snapshots

IQM

Garnet, Emerald

Calibration Validated

CZ native gate model. Result Passport signed end-to-end.

2 calibration snapshots

Quantinuum

Enterprise Tier

Trapped-ion, signed Result Passport. EUR 8 per shot, available on Enterprise tier. Used for compliance-grade workloads where shot-cost is dominated by signed-record value.

connected via vendor SDK

146 calibration snapshots across 5 vendors and 8 backends. 6 gates hardware-validated on IBM Heron, plus DriftRadar cross-backend signal. Cross-backend consistency <0.5% AR variance on IBM hardware.

Last refresh May 2026. Rigetti currently a single-round stub for DriftRadar; multi-round QEC validation pending.

SUPPORTED INTEGRATION SURFACES

Cloud & Execution Platforms

QubitBoost integrates with managed quantum platforms through SDK- and API-level hooks.

Validated backends:

•IBM Quantum, Fez, Torino, Marrakesh (hardware validated)
•Rigetti, Ankaa-3 via AWS Braket (hardware validated)
•IonQ, Aria, Forte via AWS Braket (calibration validated)
•IQM, Garnet, Emerald (calibration validated)

Does not require control of scheduling, transpilation, or hardware configuration.

Decoder & Control-Plane Integration

QubitBoost is decoder-agnostic. It consumes evidence already produced during execution.

Evidence sources:

•Execution telemetry
•Decoder diagnostics
•Cost metrics
•Backend health signals

Compatible with production decoders, standard decoders, and custom pipelines.

No decoder logic is modified.

DEPLOYMENT MODES

SDK Mode

Embedded directly into user workflows for evaluation or research use.

Shadow Mode

Decisions are logged but not enforced. Used for validation and calibration.

Enforced Mode

Abort, defer, or certify decisions are applied automatically.

Managed / Enterprise

Deployed as a service with observability, audit logging, and rollout controls.

PYTHON SDK

Run your job through QubitBoost. Install and start in three lines.

# pip install qubitboost-sdk[qiskit]

from qubitboost import QubitBoost

qb = QubitBoost(api_key="...")

result = qb.submit(circuit, backend="ibm_fez", shots=4000).wait()

print(result.status)

print(result.counts)

Backend extras: [ibm], [braket], [qm], [full]

Infrastructure Placement

Depending on requirements, QubitBoost can run:

Inside user applicationsAs a cloud-hosted serviceAs a sidecar alongside execution pipelinesOn-premise within restricted environments

No outbound data sharing is required for decision-making.

DESIGN GUARANTEES

No modification of circuits or algorithms

No interference with scheduling or queueing

No dependency on proprietary hardware features

Explicit fail-closed behavior under uncertainty

QubitBoost integrates around quantum execution, not inside it.

ENTERPRISE INTEGRATION

Full enterprise integration documentation, including decoder adapters, deployment playbooks, governance controls, and audit considerations , is available on request.

REQUEST DOCUMENTATION