QuantumKit
Hybrid quantum-classical ML toolkit — QNNs, QSVMs, and physics-informed neural networks through a Python SDK that runs on real quantum hardware and on simulators.
What it is
QuantumKit is a Quantum ML product from Binary AI Labs. Hybrid quantum-classical ML toolkit — QNNs, QSVMs, and physics-informed neural networks through a Python SDK that runs on real quantum hardware and on simulators.
Specs
- Category
- Quantum ML
- Version
- Current release — versioned per engagement
- Deployment
- Managed cloud, customer VPC, or on-premise
- Integrations
- REST and streaming APIs; OpenTelemetry for traces
- Stack
- PennyLane · Qiskit · JAX · DeepXDE
- Pricing
- Per engagement — contact hello@binarylabz.com for a quote
- Support
- Named engineer, business-hours SLA; 24/7 by arrangement
- Compliance
- SOC 2 controls inherited from the host environment; data residency configurable
- QNN and QSVM primitives
- PINN solver framework
- IBM Quantum and IonQ backends
- Hybrid training loops across quantum and classical steps
QuantumKit vs. building it yourself
Two to four engineer-quarters to reach parity, and the ongoing maintenance is the larger cost.
Questions
Does QuantumKit run on-prem?
Managed cloud, customer VPC, or on-premise. Where the deployment target constrains the architecture, that is settled during the discovery sprint rather than after.
How is QuantumKit priced?
Per engagement rather than per seat, scoped from a discovery sprint. There is no public rate card because the variance between deployments is too wide for a headline number to be useful. Contact hello@binarylabz.com for a quote.
Should we build this ourselves instead?
Two to four engineer-quarters to reach parity, and the ongoing maintenance is the larger cost.