An exact fp64 state vector
Your browser holds every amplitude of the circuit (up to 16 here), applies each gate as a matrix, and samples measurements with a seeded pseudo-random generator.
QAISS is building a neutral quantum-AI coordination layer. Follow the public roadmap and verify every claim.
Quantum Lab · live
A real state-vector simulation running on your own device — no server, no account, no wallet. Every run produces a Workload Passport you can download and reproduce bit-for-bit.
Pick a gate, then click a cell. Two-qubit gates: click the control, then the target in the same step. Click a placed gate to remove it.
| Outcome | Shots | Sampled | Exact |
|---|
A short arrow is not an error: when qubits are entangled, the information lives in their correlations, not in any single qubit.
Gate error is on: the histogram is noisy, but the Bloch spheres and phases show the ideal (noise-free) state.
| # | Workload | Shots | Seed | Noise | TVD vs exact | Output hash |
|---|---|---|---|---|---|---|
| Runs appear here. | ||||||
Kept only in this browser’s storage (up to 20). Nothing is uploaded; clearing site data removes them.
Hashes are SHA-256 of the canonical circuit and of the output counts. Same circuit + seed + shots + noise ⇒ same output hash, on any device.
Bit order differs between ecosystems: QAISS writes qubit 0 first in a count key; pyqpanda3 and OpenQASM tools usually write it last.
| Target | Kind | Result check | Status |
|---|---|---|---|
| This browser | CPU simulation | Bit-for-bit | Ran above |
QAISS connector · state vectorpython -m qaiss_cuquantum run passport.json --backend reference | CPU simulation | Bit-for-bit | Parity verified |
QAISS connector · tensor network (NumPy)python -m qaiss_cuquantum run passport.json --backend tn-reference | CPU simulation | Bit-for-bit | Parity verified |
Origin Quantum pyqpanda3 · CPUQVMpython -m qaiss_cuquantum run passport.json --backend qpanda3 | CPU simulation | Bit-for-bit | Parity verified |
NVIDIA cuStateVecpython -m qaiss_cuquantum run passport.json --backend gpu | GPU simulation | Bit-for-bit | Untested on GPU |
NVIDIA cuTensorNetpython -m qaiss_cuquantum run passport.json --backend gpu-tn | GPU simulation | Bit-for-bit | Untested on GPU |
Origin Quantum Cloud · simulatorpython -m qaiss_cuquantum run passport.json --backend origin-cloud --origin-backend full_amplitude | Cloud simulation | Statistical | API key · untested |
Origin Quantum Cloud · QPUpython -m qaiss_cuquantum run passport.json --backend origin-cloud --origin-backend <QPU name> | Quantum hardware | Statistical | API key + access · untested |
Download the passport above, then run a line from the connector folder (setup). Bit-for-bit results paste straight into the verifier below. Cloud and QPU results are sampled by Origin's service, so they can only be compared statistically. Origin cloud access needs your own API key in QPANDA_QCLOUD_API_KEY; QAISS holds no key and runs nothing on Origin's hardware.
Paste a Lab passport (or open a .json file). It is re-run from scratch on your device and every hash is re-computed. Nothing is uploaded.
Your browser holds every amplitude of the circuit (up to 16 here), applies each gate as a matrix, and samples measurements with a seeded pseudo-random generator.
This is a classical simulation. It demonstrates workflows and reproducibility — not quantum speed. Small circuits like these are easy for any laptop.
The manifest records everything needed to reproduce the run. This is the core idea QAISS wants to carry to every backend.
The readout-error slider flips each measured bit independently with the chosen probability — a deliberately simple noise model, recorded in the passport. Real devices have richer noise.