{"paper":{"title":"MCMit: Mid-Circuit Measurement Error Mitigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"MCMit reduces mid-circuit measurement errors in quantum circuits by combining faster classical feedback hardware with improved qubit discriminators.","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Aleksandra \\'Swierkowska, Benjamin Lienhard, Emmanouil Giortamis, Felix Gust, Innocenzo Fulginiti, Martin Schulz, Pramod Bhatotia, Sandra Stankovic, Xiaorang Guo, Yanbin Chen","submitted_at":"2026-04-28T17:00:53Z","abstract_excerpt":"Distributed Quantum Computing (DQC) and Quantum Error Correction (QEC) rely on dynamic circuits that include Mid-Circuit Measurements (MCMs) and classical feedback. These operations present a major bottleneck: MCMs suffer from high error rates that lead to real-time branching errors, while MCM and classical feedback latencies amplify decoherence errors. Current hardware controllers, qubit-state discriminators, and software error mitigation techniques fail to address these challenges holistically.\n  We propose MCMit, a hardware-software co-design to mitigate branching and latency-induced errors"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our CNN discriminator achieves 37-73% higher accuracy for short measurement durations than the baselines, leading to up to 80% lower logical error rates in QEC. Our branch instruction reduces feedback latency by up to 70%, improving circuit depths by up to 7× over Qubic.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the experimentally extracted QPU readout traces used for evaluation faithfully represent the noise and timing behavior of the target hardware under real-time operation, and that the reported gains generalize beyond the specific Qubic setup and tested circuits.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"MCMit reduces mid-circuit measurement errors in quantum circuits by combining faster classical feedback hardware with improved qubit discriminators.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3f609e907def7897d77c82b40a50a8b68473bffea0dce58eac670acd30ec5a0b"},"source":{"id":"2604.25863","kind":"arxiv","version":2},"verdict":{"id":"9f052699-b398-4f44-8081-a253fe710e06","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-07T16:30:04.639793Z","strongest_claim":"Our CNN discriminator achieves 37-73% higher accuracy for short measurement durations than the baselines, leading to up to 80% lower logical error rates in QEC. Our branch instruction reduces feedback latency by up to 70%, improving circuit depths by up to 7× over Qubic.","one_line_summary":"MCMit mitigates mid-circuit measurement errors via a new multi-control branch instruction, CNN and transformer discriminators, and software techniques, reporting up to 70% latency reduction and 80% lower logical error rates in QEC.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the experimentally extracted QPU readout traces used for evaluation faithfully represent the noise and timing behavior of the target hardware under real-time operation, and that the reported gains generalize beyond the specific Qubic setup and tested circuits.","pith_extraction_headline":"MCMit reduces mid-circuit measurement errors in quantum circuits by combining faster classical feedback hardware with improved qubit discriminators."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.25863/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T03:39:28.335734Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:42:51.402416Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"2803207203ddf57fccf54480e428c712de8db8449e03170d3f0004980b2f766f"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}