Multi-level benchmarking methodologies

Efficiency-oriented methodologies

• Metric Noise Resource (MNR) (2022) [1]

In a nutshell, the MNR methodology aims to analyse and optimize the power consumption of quantum computers at different levels of the quantum stack. Since high-fidelity quantum operations demand a greater power consumption, identifying optimal operating points—where power usage is minimized and success probability is maximized—is highly desirable. The MNR approach provides valuable insight into these optimal sweet spots, enabling more efficient and reliable quantum computing.

Volumetric methodologies

• Volumetric Benchmark (VB) (20219) [2]

In a nutshell, the volumetric benchmark methodology analyses the running success of a set of quantum circuits considering their width (number of qubits) and depth (number of gates). It outlines regions (a combination of width and depth values) where the quantum computer can successfully run the circuit, called capability regions. These regions can then be used to predict the computer’s performance running other tasks.

References

1. [1]M. Fellous-Asiani, J. H. Chai, Y. Thonnart, H. K. Ng, R. S. Whitney, and A. Auffèves, “Optimizing Resource Efficiencies for Scalable Full-Stack Quantum Computers,” PRX Quantum, vol. 4, no. 4, Oct. 2023, doi: 10.1103/prxquantum.4.040319. [Online]. Available at: http://dx.doi.org/10.1103/PRXQuantum.4.040319
2. [2]R. Blume-Kohout and K. C. Young, “A volumetric framework for quantum computer benchmarks,” Quantum, vol. 4, p. 362, Nov. 2020, doi: 10.22331/q-2020-11-15-362. [Online]. Available at: http://dx.doi.org/10.22331/q-2020-11-15-362