The QV-k performance classes can be estimated from qubit count, connectivity, and error rates, with a fault-tolerant model predicting a crossover near a few thousand physical qubits.
An entanglement-based volumetric benchmark for near-term quantum hardware
1 Pith paper cite this work, alongside 2 external citations. Polarity classification is still indexing.
abstract
We introduce a volumetric benchmark for near-term quantum platforms based on the generation and verification of genuine entanglement across n-qubits using graph states and direct stabilizer measurements. Our benchmark evaluates the robustness of multipartite and bipartite n-qubit entanglement with respect to many sources of hardware noise: qubit decoherence, CNOT and swap gate noise, and readout error. We demonstrate our benchmark on multiple superconducting qubit platforms available from IBM (ibmq_belem, ibmq_toronto, ibmq_guadalupe and ibmq_jakarta). Subsets of $n<10$ qubits are used for graph state preparation and stabilizer measurement. Evaluation of genuine and biseparable entanglement witnesses we report observations of $5$ qubit genuine entanglement, but robust multipartite entanglement is difficult to generate for $n>4$ qubits and identify two-qubit gate noise as strongly correlated with the quality of genuine multipartite entanglement.
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Estimating the Power of a Quantum Computer
The QV-k performance classes can be estimated from qubit count, connectivity, and error rates, with a fault-tolerant model predicting a crossover near a few thousand physical qubits.