Papers for

quantum hardware integrators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Quantum compilers hide nearly a third of circuit layout bugs

What Output-Equivalence Oracles Miss: An Empirical Study of Equivalence-Invisible Bug Fixes in Quantum Transpilers (Qiskit, tket, Cirq)

Abstract: Quantum compilers are judged correct by an output-equivalence oracle: the compiled circuit must compute the same unitary as the original, modulo global phase and a qubit-layout permutation. This oracle, by construction, checks only that semantic map, not the circuit's own layout, permutation, or phase records: a defect there, or in a fixed-seed run's determinism, can pass unseen though the record is public. We measure how often this happens in real merged compiler fixes: a systematically identified corpus of Qiskit transpiler bug-fixes, classified by an independently dual-coded, source-validated manifestation taxonomy. Nineteen of 68 fixes (28%, 95% Wilson CI 19-40%) repair faults that this equivalence screen does not catch, even one augmented with compilation-validity, circuit-quality, and performance checks. A secondary wider-window robustness sample produced a nearly identical fraction (29/104, 27.9%, CI 20-37%). A conservative floor remains even restricted to the one unconditionally equivalence-invisible channel (a corrupted layout or permutation record): 10 of 68 fixes (15%, CI 8-25%) beneath the 28% headline. The gap is not Qiskit-specific: it replicates in tket (7 of 21, 33%), with Cirq smaller but consistent. We detected no systematic differences on five inexpensive PR-level characteristics (19 vs 49, underpowered to rule out a modest true difference). This class dominates the invisible set, concentrating at representation-boundary crossings. We release the corpus, codebook, and coding artifacts. Here we only measure it.

Sat 12 SeptSoftware Engineering
The gist
Quantum compilers translate quantum programs into hardware instructions and are usually checked for correctness by comparing their outputs. The authors found that many bug fixes in popular quantum compilers fix problems that these standard checks miss, especially issues related to how circuits are mapped to qubits or recorded. This means current testing methods can overlook certain subtle bugs. They confirmed this problem across different quantum compiler tools and shared their data for further study.
Open → 2609.13839v1

Capability gated checks reveal limits of quantum decoder correctness

Capability-Gated Conformance Testing of Quantum Error-Correction Decoder Libraries

Abstract: A quantum error correction decoder is a library other people's results depend on, judged in one dominant way. Sample errors, decode, and count wrong logical observables. We ask what else can be checked there. Our conformance contract needs no oracle. One check asks that a returned correction explain the syndrome in the caller's index space. The other hands a decoder one instance under two presentations differing only in bookkeeping, where two feasible corrections of different weight prove the heavier is not minimum-weight. Verdicts are gated on what each library declares, so a firing contradicts a published guarantee. Nine configurations from five public libraries give three results. Documentation answers 4 of 54 capability questions. Bounded-distance correctness, the property callers most depend on, has a direct declaration yield of 0.0%, though its hypotheses hold in 62.1% of cases. Presentation sensitivity is real but shallow. One solver moved to a 26% heavier correction under a different numbering, which reaches the logical class at most once in twenty thousand shots. Established evaluation misses corruptions that preserve logical parity, while one summation over the caller's weights catches every one we injected. All 639 certificates ship as bundles a standalone verifier re-derives from first principles.

Mon 7 SeptHardware Architecture
The gist
Quantum computers use special codes to fix errors, and programs called decoders work out how to fix these errors. This paper looks beyond usual tests to see if these decoders really do what they claim without needing a perfect answer to check against. The authors find that many decoders don’t clearly state how well they guarantee correctness, and sometimes change their fixes when given the same problem in a slightly different way. They provide tools to verify decoders more thoroughly and independently.
Open → 2609.07035v1