Robust characterization of average gate set noise: Filtered randomized benchmarking and cross-talk tomography

Beschreibung

As quantum processors scale, robust and scalable tools to characterize gate-level noise become indispensable.
Randomized benchmarking (RB) has emerged as a SPAM-robust, bottom-up approach: it yields a single, meaningful number — the average gate fidelity — to characterize a gate set.
At the same time, RB can in principle be extended into a full tomographic framework (RB tomography), but practical implementations have long been hindered by the need for sequence inversion, explicit gate interleaving, and an infeasible amount of shot.
In this talk, I present two applications that explore both ends of this spectrum. First, I show how filtered randomized benchmarking (filtered RB) — a variant of RB where the sequence inversion is performed in classical post-processing — can be adapted to benchmark analog quantum simulators with bosonic and fermionic dynamics. This yields robust fidelity benchmarks for systems governed by particle-number-conserving dynamics.
Second, I demonstrate the practical application of RB tomography on digital platforms: using only one random sequence experiment, we reconstruct marginals of the average noise channel.
This allows us to learn classically correlated quantum noise, including cross-talk, from experimental data.
These protocols are lightweight, scalable, and compatible with current hardware.
I will present both numerical simulations and experimental results, including what may be the first direct observation of average gate-set noise channels reconstructed from real superconducting quantum device data.

Zeitraum	23 Sep. 2025
Ereignistitel	Assessing Performance of Quantum Computers (APQC) 2025
Veranstaltungstyp	Konferenz
Ort	Estes Park (Colorado), USA/Vereinigte Staaten, ColoradoAuf Karte anzeigen
Bekanntheitsgrad	International