A compiler pass over a quantum error correction circuit must preserve what the circuit computes. The artefacts a practitioner reaches for when reasoning about such a circuit, detector samples and the detector error model, are defined relative to the circuit's own noiseless execution. This self-reference makes them exactly blind, rather than merely insensitive, in the regime where a pass has to be checked: before noise is added, which is also where the checking must happen if noise channels are not to survive the gates they describe. Across 335 mutants that provably change what a repetition-code memory circuit computes, detector sampling and detector error model comparison detect none. On a rotated surface code memory experiment, deleting every single-qubit gate leaves the detector error model byte-identical, 29 lines and 24 detectors either way, while 21 of 33 measurement outcomes change by up to 0.50. Two negative controls, each equivalent on every one of 200 trials with no oracle raising a false positive, establish that the measurement is not an artefact of the mutation set. The paper gives the measurement-record oracle that closes the gap and is exact for noiseless stabiliser circuits, and reports two negative results: one compiler test suite whose oracle detected every fault that could be constructed against it, and one transformation library that came back clean. All code and data are public.
A compiler pass over a quantum error correction circuit must preserve what the circuit computes. The artefacts a practitioner reaches for when reasoning about such a circuit, detector samples and the detector error model, are defined relative to the circuit's own noiseless execution. This self-reference makes them exactly blind, rather than merely insensitive, in the regime where a pass has to be checked: before noise is added, which is also where the checking must happen if noise channels are not to survive the gates they describe. Across 335 mutants that provably change what a repetition-code memory circuit computes, detector sampling and detector error model comparison detect none. On a rotated surface code memory experiment, deleting every single-qubit gate leaves the detector error model byte-identical, 29 lines and 24 detectors either way, while 21 of 33 measurement outcomes change by up to 0.50. Two negative controls, each equivalent on every one of 200 trials with no oracle raising a false positive, establish that the measurement is not an artefact of the mutation set. The paper gives the measurement-record oracle that closes the gap and is exact for noiseless stabiliser circuits, and reports two negative results: one compiler test suite whose oracle detected every fault that could be constructed against it, and one transformation library that came back clean. All code and data are public.
Quantum compilers face the test oracle problem, judged 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. This empirical software engineering study of quantum transpiler correctness uses repository mining to 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 fault-manifestation taxonomy. Nineteen of 68 fixes (28%, 95% Wilson CI 19-40%) repair faults invisible to this equivalence screen, even one augmented with compilation-validity, circuit-quality, and performance checks, and an extended 104-fix corpus over a wider window holds at the same rate with a tighter interval (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 (7/21, 33%), with Cirq smaller but consistent. We detected no systematic differences on five inexpensive PR-level characteristics (19 vs 49, underpowered alone; the extended 29-vs-75 corpus tightens every interval toward zero). This class dominates the invisible set, concentrating at representation-boundary crossings. We release the corpus, codebook, and coding artifacts for quantum software testing research. Here we only measure it.
Quantum error correction codes are essential for fault-tolerant quantum computing. They are a method of detecting and correcting errors in a quantum computer by encoding logical qubits across multiple physical qubits. Several prominent error correction codes have been developed and larger and more complex codes are being developed continuously. However, evaluating the fault-tolerance of these larger codes is becoming exponentially more complicated. While conventional methods of proving and evaluating the fault-tolerance of these circuits have worked thus far, they are soon to be obsolete. The aim of this thesis is to develop a framework that automatically evaluates the fault-tolerance of a quantum error correction circuit across different hardware architectures. The framework works as follows: it takes a noiseless quantum error correction code; injects hardware-realistic noise into it; and evaluates the fault-tolerance of the resulting noisy circuit by estimating its logical error rate through Monte Carlo sampling. For the injection of hardware-realistic noise, this thesis considers noise stemming from five different quantum computing hardware architectures: superconducting qubits, neutral atoms, trapped ions, photonic qubits, and spin qubits. The noise model was constructed by extracting single-qubit gate, two-qubit gate and measurement error fidelities from current experimental literature. The noise model and circuit generation is implemented in Python, circuit representation and noise injection is executed in Stim and decoding and estimating the logical error rate is achieved through PyMatching. The framework is evaluated on the repetition code, the surface code, and the color code; the logical error rate is estimated across all five hardware architectures under the noise model; and the results are discussed in the context of hardware compatibility for the given codes.
Test oracles for quantum transpilers typically judge correctness by comparing compiled output against a reference: a statevector, a sampled distribution, or a unitary compared modulo global phase. A companion empirical study measures how often that choice fails. Roughly 28% of merged Qiskit transpiler bug-fixes (95% Wilson CI 19-40%) repair a fault that corrupts layout metadata, global phase, or run-to-run reproducibility while output stays correct: invisible to a black-box output-equivalence oracle by construction. This paper closes that gap with a fault-class-matched, layout-aware, width-tiered oracle family: a layout/permutation contract checker and a contract-level metamorphic relation (MR-1) for the metadata channel, a global-phase tracker for the phase channel, and a determinism runner for reproducibility. Verified from source on nine real, merged Qiskit transpiler regressions (three per channel), the output-equivalence oracle is blind throughout and the matched mechanism fires on every case. A 675-configuration sweep of the contract/metadata invariant finds no false positive. Synthetic mutant families confirm reliability at scale: 1.00 sensitivity and specificity across 36 mutants apiece for the contract/metadata and global-phase channels, and 1.00 sensitivity (95% CI 0.44-1.00) for reproducibility on the three circuits where the mutation is constructible. The contract checker costs two to six orders of magnitude less than a plain output check, the global-phase tracker is comparably cheap within its exact tier, and only the metamorphic relation carries a bounded cost. Ported natively to pytket/tket, the global-phase mechanism transfers cleanly, an identical 1.00/1.00 result with phases recovered to double-precision accuracy, evidence against a Qiskit-specific artifact.
Mutation testing assesses test suites by introducing program changes and checking whether tests detect them. In quantum programs, a surviving mutant may leave program behavior unchanged, produce a change hidden by the chosen input or measurement, or remain undetected because statistical evidence is insufficient. We develop a circuit-aware framework to distinguish these causes and guide execution planning and test changes. The framework covers ideal, noiseless programs and mutations that replace one gate or subcircuit. The method traces how a mutation can affect measured outputs and combines established mathematical checks with statistical estimates. For supported cases, we establish conditions under which a test cannot reveal a mutation and exact formulas for its effect when the required state information is available. Under stated assumptions, we bound the mutation score while retaining surviving and unresolved mutants, and identify a restricted circuit family whose mutation effects admit efficient classical classification. The deliverables include a decision procedure, mathematical guarantees, worked examples of test changes and their implementation requirements, and a small-circuit reference implementation for differential expectation tests. These results help testers decide what to execute, what to change, and what remains unknown. The evidence is limited to proofs and executable examples under the ideal model.
The classical simulation of universal quantum circuits is crucial both fundamentally and practically for quantum computation. We propose SyQMA, a simulator with several convenient features, particularly suited for quantum error correction (QEC). SyQMA simulates universal quantum circuits with incoherent Pauli noise and computes exact expectation values and measurement probabilities as symbolic functions of circuit parameters: rotation angles, measurement outcomes, and noise rates. This simulator can sample measurement outcomes, enabling the simulation of dynamic quantum programs where circuit composition depends on prior measurement outputs. For QEC, it performs circuit-level maximum-likelihood decoding, provides exact symbolic expressions for logical error rates, and verifies the fault distance of fault-tolerant (FT) stabiliser and magic state preparation protocols. These features are enabled by an intuitive extension of stabiliser simulators, where each non-Clifford Pauli rotation and incoherent Pauli channel is compactly represented via auxiliary qubits and a modified trace. Representing the state requires only polynomial memory and time, while computing expectation values and measurement probabilities takes exponential time in the number of non-Clifford rotations and deterministic measurements, but only polynomial memory. The FT preparation of stabiliser and magic states, including the first stage of magic state cultivation, is analysed without approximations. We also exactly convert the disjoint error probabilities of a general multi-qubit Pauli channel to independent ones, a key step for creating and sampling from detector error models. The code is publicly available and open-source.
Quantum software relies on compilers, intermediate representations, and execution frameworks that transform programs before execution. Faults in these components can change the behavior of otherwise correct quantum programs. Yet quantum testing techniques are often evaluated using mutation-generated faults and curated bug benchmarks. We investigate how well the faults represented by these artifacts reflect those encountered in modern quantum software. We audit four quantum mutation-testing tools and three curated bug benchmarks, and collect 410 verified bugs from ten quantum software repositories over two years. We classify faults from all three sources into a common taxonomy of 56 bug families and compare their representation across mutation operators, curated benchmarks, and recent real-world bugs. The mutation tools support 20 bug families, and curated benchmarks add 15 more. Yet only 56 of the 410 real-world bugs (13.7%) belong to families represented by either source; the remaining 354 (86.3%) belong to families absent from both. The largest unrepresented families concern compiler transformations (66 bugs), type handling (53), and input validation (47). Unlike local gate or statement changes, these faults often depend on intermediate representations, compiler pipelines, input types, or dependency versions. Existing mutation operators primarily modify circuits and program constructs, whereas many real-world faults arise in the compilers, representations, and framework interfaces that process them. Addressing this gap requires looking beyond circuit-level faults toward testing semantic preservation across compiler transformations, correctness at representation boundaries, and interactions between software components. Our study provides empirical evidence to guide fault models and benchmarks that capture these mechanisms and preserve the contexts needed to reproduce them.