Mid-circuit Measurement Error Mitigation
| Path | What it is |
|---|---|
applications/ |
The paper's benchmark circuits: constant-depth GHZ, long-range CNOT, teleportation, qubit reuse |
backends/ |
QPU/simulator backend definitions and calibration data |
compiler/branching/ |
Stochastic branching (§7.3) |
compiler/decoding/ |
Measurement hardening — parity checks, repetition codes (§7.2) |
compiler/removal/ |
Static MCM elimination (§7.1), forked from pcm-ccop-dc-pass |
qubit_state_discrimination/ |
MCMit-CNN / MCMit-T discriminators and the HERQULES/QubiCML/baseline comparisons (§6, §8.3) |
branch_instruction/ |
The FPGA controller for branch_reduce_fproc (§5), forked from QubiC |
lattice-sim/ |
The QEC simulator behind Fig. 3 and Fig. 13 (§3.3, §8.5) |
evaluation/ |
Runnable per-figure evaluation drivers, plus evaluation/motivation/MECH/ for Fig. 2 (§3.2) |
results/ |
Every figure's CSV data, in one place |
plotting/ |
The remaining top-level plotting scripts (Fig. 10, 11, 12) |
MCMit's own code (applications/, backends/, compiler/, evaluation/,
plotting/) needs:
- Qiskit (
qiskit,qiskit-aer,qiskit-ibm-runtime) — circuits, simulation, hardware access - mthree — Qiskit M3 readout calibration, used by stochastic branching and GHZ evaluation
- NumPy, pandas, NetworkX, matplotlib, seaborn — numerics, the DAG compiler pass, plotting
Pinned in requirements.txt: pip install -r requirements.txt.
Each vendored subproject pins its own dependencies separately, since they're
independent tools with their own environments:
lattice-sim/requirements.txt,
qubit_state_discrimination/requirements.txt,
evaluation/motivation/MECH/requirements.txt,
branch_instruction/qubic-software/pyproject.toml.
MCMit builds on several external tools, either vendored with attribution or reproduced as evaluation baselines. Each has its own README with setup and reproduction details.
| Tool | What it is | How MCMit uses it |
|---|---|---|
lattice-sim (lattice-sim/) |
Independent QEC simulator | Produces Fig. 3 and Fig. 13's surface-code logical-error-rate data (§3.3, §8.5); we added the MCM-latency sweep scripts and presets on top. Vendored with full commit history. |
MECH (evaluation/motivation/MECH/) |
DQC chiplet compiler | Produces Fig. 2's sensitivity analysis (§3.2) via its own notebook, unmodified; we added a CSV-export script alongside it. |
HERQULES (qubit_state_discrimination/) |
Matched-filter + FNN qubit-state discriminator | Reproduced as MCMit's primary discriminator baseline throughout §6/§8.3. |
QubiC (branch_instruction/) |
Open-source FPGA-based QPU control framework | Forked and extended with the branch_reduce_fproc constant-latency multi-qubit branch instruction (§5). |
pcm-ccop-dc-pass (compiler/removal/) |
Quantum-constant-propagation MCM-elimination compiler pass | Forked and extended with multi-qubit classical-control-logic simplification (§7.1). |
- lattice-sim: S. Maurya and S. Tannu, "Synchronization for Fault-Tolerant Quantum Computers," ISCA 2025. https://doi.org/10.1145/3695053.3730991 (artifact: https://zenodo.org/records/15092177)
- MECH: H. Zhang, K. Yin, A. Wu, H. Shapourian, A. Shabani, Y. Ding, "MECH: Multi-Entry Communication Highway for Superconducting Quantum Chiplets," ASPLOS 2024. https://doi.org/10.1145/3620665.3640377
- HERQULES: S. Maurya, C. N. Mude, W. D. Oliver, B. Lienhard, S. Tannu, "Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures," ISCA 2023. https://doi.org/10.1145/3579371.3589042
- QubiC: Y. Xu, G. Huang, J. Balewski, R. Naik, A. Morvan, B. Mitchell, K. Nowrouzi, D. I. Santiago, I. Siddiqi, "QubiC: An Open-Source FPGA-Based Control and Measurement System for Superconducting Quantum Information Processors," IEEE Transactions on Quantum Engineering, vol. 2, 2021. Source: https://gitlab.com/LBL-QubiC
- pcm-ccop-dc-pass: https://github.com/i2-tum/pcm-ccop-dc-pass, based on Y. Chen and Y. Stade, "Quantum Constant Propagation," in Static Analysis (SAS 2023), Springer, pp. 164–189, and extended in Y. Chen, I. Fulginiti, C. B. Mendl, "Reducing Mid-Circuit Measurements via Probabilistic Circuits," QCE 2024. https://ieeexplore.ieee.org/abstract/document/10821341, and "Optimization Framework for Reducing Mid-Circuit Measurements and Resets," Computational Science – ICCS 2025 Workshops, Springer. https://link.springer.com/chapter/10.1007/978-3-031-97570-7_13