quantinuum / sg-grand-challenge
Publicquantinuum / sg-grand-challenge
Public| Framework / Target | H2-1E | H2-2E | H2-Emulator | H2-1SC | H2-2SC |
|---|---|---|---|---|---|
| Guppy (hugr-qir) | ✅ | ✅ | ✅ | ✅ | |
| Pytket | ✅ | ✅ | ✅ | ✅ | ✅ |
| Qiskit (qsharp) | ✅ | ✅ | ✅ | ✅ |
H2-Emulator: A Nexus-tier emulation resource with state-vector and stabilizer simulation support. An average error model across all H2 hardware instances is used to model noise mechanisms. This is costed in seconds.H2-1E: A hardware-tier emulator instance with state-vector and stabilizer simulation support. The noise model and physical properties of the emulator corresponds to H2-1 hardware. This is costed in hardware quantum credits (HQCs).H2-2E: A hardware-tier emulator instance with state-vector and stabilizer simulation support. The noise model and physical properties of the emulator corresponds to H2-2 hardware. This is costed in hardware quantum credits (HQCs).H2-1SC, H2-2SC: A debug tool, also known as a syntax checker, to verify user programs and to estimate the job cost of running a program on hardware or hardware-tier emulators.guppy_example.py — a GHZ state written in Guppy, compiled to HUGR and
lowered to QIR before submission.pytket_example.py — a Bell state built with pytket and rebased into the
H2 gateset before submission.utils.py: cost estimation, program summary
tiles, circuit rendering and a counts histogram.aqora.QPU is framework-agnostic. run() accepts pytket Circuits, qiskit
QuantumCircuits, @guppy-decorated functions, hugr Packages, raw HUGR or
QIR bytes, and QASM source — it reads the formats your platform advertises and
encodes into the best match, so switching framework does not change the
submission code in the examples:
qpu = QPU(platform='nexus:Selene')
job = qpu.run(my_guppy_program, shots=1000)
job.counts(timeout=600)
from aqora import QPUJob
QPUJob.from_id("<job id>").counts()