Readout and calibration optimization for superconducting quantum processors.

We optimize useful measurement information per unit wall-clock time — not only single-shot fidelity. Drop-in software for the qubit measurement stack you already operate.

For superconducting-qubit teams Targets calibration, certification, and mid-circuit measurement

01 · What it does

Calibration and readout, optimized for wall-clock throughput.

Three modules, one objective:
minimize time-to-confident-result on a running quantum processor.

Module · 01

Throughput-driven calibration

Treats readout as an information channel. Selects integration windows that minimize total certification wall-clock time, accounting for hardware overhead — not just single-shot fidelity.

Module · 02

Adaptive readout truncation

Sequential decision logic terminates measurement once confidence is reached. Targets installed-base control stacks where measurement duration sets the cycle time of the processor.

Module · 03

Information-extraction diagnostic

Quantifies how closely your measurement chain approaches the hypothesis-testing limit. Separates detection-inefficiency and decoherence contributions on a per-system basis.

Module · 04

Drop-in, no hardware changes

Deployed as a calibration or firmware update on existing RFSoC-class control systems. No new resonators, no new amplifiers, no requalification.

02 · Technical demo

Reproduce the result on your own parameters.

Open-source notebooks.
Bring your own χ, κ, T₁, and hardware overhead.

readout-throughput-demo

A reproducible notebook that takes your measurement-system parameters and returns the integration window that minimizes wall-clock certification time. Compares against the conventional fidelity-optimal operating point on the same hardware.

View on GitHub →

Language · Python 3.11+ Runtime · ~30 s on laptop Inputs · χ, κ, T₁, η, n̄, τ_overhead Output · τ_rate, expected speedup

03 · Representative simulated result

The fidelity-optimal point is not the throughput-optimal point.

Parameters typical of contemporary transmon platforms.
Simulation; on-hardware validation in progress.

Figure 01 · Throughput vs. fidelity optima simulated · representative parameters

placeholder · replace with throughput_summary.png

τ_fid — fidelity-optimal ~0.78 µs

τ_rate — throughput-optimal ~1.22 µs

wall-clock speedup ~9–11%

Fig. 01. At the conventional fidelity-optimal integration time, the measurement chain is single-shot-best but throughput-suboptimal. The wall-clock objective — accounting for per-shot hardware overhead and information rate — places the optimum at a noticeably longer integration window. Representative simulation under transmon parameters; hardware validation in progress.

04 · Contact

Operating a superconducting QPU? Let’s talk.

Pilots, design partnerships,
and technical conversations.

Direct contact@readoutiq.com

Send your hardware parameters and we will return a representative speedup estimate within a few days.