Quantum Cloud Platforms

qalgora-Q targets a single kernel abstraction across simulators and the major quantum clouds. Reaching a physical QPU requires hardware-specific compilation plus a vendor adaptation plugin; once that is in place you set a target, supply credentials, and submit. Native direct submission to physical QPUs is not built in.

Targets across simulators, clouds & vendor SDKs

qalgora-Q plans to describe simulator, cloud-platform, and vendor-SDK workflows through one unified target abstraction. For compatible gate-model circuits an adapter can help export, transpile, and submit; non-gate-model tasks such as neutral-atom analog or photonic GBS need their own dedicated APIs.

import qalgora

# bell is a predefined example kernel (see Building Quantum Kernels)
# qalgora.set_target("ibm", machine="ibm_torino")   # planned adapter workflow — needs Qiskit Runtime credentials
# qalgora.set_target("google", machine="willow")       # Google Quantum AI (research)
# qalgora.set_target("braket", machine="ionq/Forte-1")  # Amazon Braket
# qalgora.set_target("azure", machine="quantinuum.h2")  # Azure Quantum

counts = qalgora.sample(bell, shots_count=1000)

Platform comparison

Access tiers, device availability and any pricing shift over time — confirm against each vendor's console / official pages. qalgora-Q's pages document adapter or interop workflows, not real-time device status.

Native gates & connectivity

qalgora-Q transpiles every kernel into the chosen device's basis and connectivity, so you write the algorithm once — see Transpilation.

Get hands-on

Step-by-step walkthroughs for running on each platform — credentials, submit, retrieve: