Standard quantum circuit simulation represents amplitudes as double-precision complex numbers, so quantities such as entanglement entropy are computed only approximately. We revisit two well-known facts about the stabilizer formalism — that entanglement entropy of a stabilizer state is always an integer, and that the distance of a stabilizer code is a combinatorial quantity — and implement both exactly, using binary linear algebra over F2 on the stabilizer tableau, with no floating-point step anywhere in the computation. We verify the entropy formula against Qiskit's floating-point von Neumann entropy on canonical states and on 30 random Clifford circuits (all matches exact), reproduce the area-law entanglement of the 1D cluster state, and verify the code-distance procedure against three canonical quantum error-correcting codes (Steane, the 5-qubit code, and Shor's code), recovering their published distances exactly. As an illustration of the exact toolkit in use, we report an empirical scaling law for the depth at which entanglement saturates in a specific random-circuit model. Finally, since every result above concerns exact mathematics or exact simulation of noiseless Clifford circuits, we complement it with a small, honestly-labelled real-hardware comparison: the GHZ state on 3–7 qubits, run on IBM Quantum superconducting hardware, measured against the exact noiseless prediction our formalism computes, together with a quantitative check against a naive noise model built from the device's own published calibration data — which the data systematically contradicts by 18–21 percentage points, a discrepancy we report along with our best physical explanation for it. A second, independent hardware experiment measures the cluster state's own stabilizer generators directly, and identifies a single miscalibrated physical qubit as correlated with elevated error; a follow-up control experiment, forcing the circuit onto verified low-error qubits instead, confirms the effect is causal but partial (leakage drops by 1.84 percentage points, roughly a third of the excess) rather than the whole explanation, which we report at its true, unexaggerated size. Applying the naive noise model to the control circuit itself reverses its bias (now optimistic rather than pessimistic) by an amount consistent, to order of magnitude, with ordinary decoherence over the circuit's execution time — a physical effect the naive model was never built to capture.
Standard quantum circuit simulation represents amplitudes as double-precision complex numbers, so quantities such as entanglement entropy are computed only approximately. We revisit two well-known facts about the stabilizer formalism — that entanglement entropy of a stabilizer state is always an integer, and that the distance of a stabilizer code is a combinatorial quantity — and implement both exactly, using binary linear algebra over F2 on the stabilizer tableau, with no floating-point step anywhere in the computation. We verify the entropy formula against Qiskit's floating-point von Neumann entropy on canonical states and on 30 random Clifford circuits (all matches exact), reproduce the area-law entanglement of the 1D cluster state, and verify the code-distance procedure against three canonical quantum error-correcting codes (Steane, the 5-qubit code, and Shor's code), recovering their published distances exactly. As an illustration of the exact toolkit in use, we report an empirical scaling law for the depth at which entanglement saturates in a specific random-circuit model. Finally, since every result above concerns exact mathematics or exact simulation of noiseless Clifford circuits, we complement it with a small, honestly-labelled real-hardware comparison: the GHZ state on 3–7 qubits, run on IBM Quantum superconducting hardware, measured against the exact noiseless prediction our formalism computes, together with a quantitative check against a naive noise model built from the device's own published calibration data — which the data systematically contradicts by 18–21 percentage points, a discrepancy we report along with our best physical explanation for it. A second, independent hardware experiment measures the cluster state's own stabilizer generators directly, and identifies a single miscalibrated physical qubit as correlated with elevated error; a follow-up control experiment, forcing the circuit onto verified low-error qubits instead, confirms the effect is causal but partial (leakage drops by 1.84 percentage points, roughly a third of the excess) rather than the whole explanation, which we report at its true, unexaggerated size. Applying the naive noise model to the control circuit itself reverses its bias (now optimistic rather than pessimistic) by an amount consistent, to order of magnitude, with ordinary decoherence over the circuit's execution time — a physical effect the naive model was never built to capture.
H. Billah and M.M.H. Sarker contributed equally to this work. Interpreting a fixed quantum circuit's output as evidence of a hardware effect requires ruling out two things usually conflated with it: finite-sample fluctuation, and structure already present in the circuit's own noiseless output. Less commonly checked is a third: whether the reference circuit compared against is the one actually executed, once compiler-level qubit routing is accounted for. We execute a fixed 35-qubit, 243-gate diagnostic circuit on IBM Heron-family hardware (three runs, two backends) and show this third check is essential: the routed circuit submitted to hardware has different noiseless predictions than the logical circuit intended, for several qubits. Against the correct, routed-circuit reference, the aggregate deviation is significant in two of three runs, and four registers (0, 9, 14, 27) show a significant output-probability deviation in every run — though we distinguish a high-confidence subset, {0, 9, 27}, with tightly time-matched calibration, from qubit 14, included only on an exploratory basis given its calibration was retrieved roughly two weeks post-execution. Same-backend replicates correlate more strongly by the Pearson measure, though a rank-based (Spearman) test shows this gap does not survive at the level of register ordering, indicating substantial leverage from a small number of registers rather than a broadly robust pattern; these three executions also share a byte-identical payload and are not independent replications in the strictest sense. The mechanism is only partially resolved: independent single-qubit readout error and raw gate count are ruled out as complete explanations for most of these registers, but the simplest version of the leading hypothesis for the single most-affected register does not reproduce the observed cross-backend pattern even with live, backend-specific timing data. A second, structurally different circuit validated only in simulation calibrates the statistical procedure's behavior under one synthetic noise model; it does not itself establish generality to real hardware, and the specific register set found here should not be expected to generalize beyond this study. Note on scope: this is a methodology and case-study contribution centered on executed-circuit references for fixed-circuit diagnostics, not a characterization of IBM Heron hardware in general or a randomized-benchmarking experiment in the formal sense. Supporting data, the routed OpenQASM circuits, raw measurement counts, and analysis code for this study are available on request from the corresponding author. Affiliation: Sonicium Quantum Lab Ltd., Dhaka, Bangladesh.
Quantum error correction (QEC) is a prerequisite for fault-tolerant quantum computation, yet the overhead cost—the number of physical qubits required to encode and protect a single logical qubit—varies substantially across QEC code families. This paper presents a systematic simulation-based comparison of two paradigmatic QEC codes: the three-qubit bit-flip repetition code and the distance-3 surface code. The bit-flip code is simulated by literal Qiskit Aer circuit execution (encoding, injected depolarizing noise, and dynamic classical feed-forward correction). The distance-3 surface code is instead simulated with a custom, vectorized stabilizer-formalism engine, because its 17-qubit noisy circuit would be far more expensive to run shot-by-shot in Aer at the scale needed here. This engine implements the same Aaronson-Gottesman algorithm underlying Aer's stabilizer backend and is cross-validated against genuine Aer circuit execution at a representative operating point; no surface-code result in this paper comes from literal Aer execution. We apply an independent single-qubit depolarizing channel at physical error rates p ∈ [0.001, 0.30] and measure the resulting logical error rate P_L for each code across 200,000 independent trials per data point (95% Wilson confidence intervals reported throughout). We identify the pseudo-threshold p*—the physical error rate at which a code's logical error rate equals the uncorrected physical error rate, and compare simulated crossover points against closed-form analytical predictions. Our results reveal a more nuanced picture than the naive threshold-theorem intuition suggests. The three-qubit bit-flip code cannot correct phase-type errors under any circumstance: under a full quantum-state-protection criterion its logical error rate exceeds the physical error rate for every physically meaningful p, with the two curves crossing only at p ≈ 0.75 deep in the maximally-mixed regime and of no practical relevance. Under the narrower, but operationally standard, "bit-observable" criterion (protection of a definite computational-basis value only), the code is unconditionally beneficial (P_L < p) across the entire tested range, because the 2/3 fraction of depolarizing weight that manifests as a detectable bit-flip is compressed further by majority-vote correction. These two results are not contradictory: they evaluate different protection goals—preservation of a classical bit value versus preservation of an arbitrary quantum state—and every comparison in this paper that mixes the two is labeled accordingly. Under this study's idealized, single-round setup, the distance-3 surface code achieves a simulated finite-size pseudo-threshold of p*_SC ≈ 0.083 (8.3%)—a result specific to noiseless syndrome extraction, a single correction round, and an exact minimum weight decoder, and not a hardware representative or asymptotic threshold. That figure is roughly three orders of magnitude higher than an order-of-magnitude literature extrapolation of ≈ 5.6 × 10⁻⁵ (substituting the asymptotic literature threshold p_th ≈ 0.75% into a power law fit for a different, many-round, measurement noisy regime, not a dedicated prediction for our exact setup); the gap is attributable to precisely those idealizations. The surface code requires 17 physical qubits per logical qubit in our implementation (9 data + 8 ancilla) versus 3 for the bit-flip code, a ≈ 5.7× overhead, quantitatively illustrating the overhead-versus-protection tradeoff fundamental to near-term fault-tolerant architectures. All simulation code is available at https://github.com/shanjeev-b-u/qec-code-comparison-depolarizing-noise
Entanglement and non-stabilizerness are complementary resources underlying the complexity of quantum states, yet extracting either quantity from large quantum circuits generally requires exponentially scaling resources. In diagrammatic approaches to quantum computation, such as the ZX-calculus, both quantities likewise typically require expensive tensor contractions that are not native to graphical rewriting. Here we show that both entanglement and magic can be efficiently estimated directly from ZX-diagrams. Flow enables efficient extraction of a normal form separating a potentially extensively entangled graph-state backbone from non-Clifford Pauli gadgets. This structure yields additive upper and lower bounds on bipartite entanglement entropy, which we further tighten through a preprocessing procedure that removes or merges redundant non-Clifford contributions. The same normal form gives an upper bound on logarithmic stabilizer extent. We benchmark these methods in random unitary and monitored circuits, and a circuit combining Trotterized Hamiltonian evolution with Clifford layers, finding that the resulting bounds remain informative even at large qubit numbers, circuit depths, and internal spider counts. Our results provide scalable probes of entanglement and non-stabilizerness of ZX-diagrams and establish a bridge between ZX-calculus and quantum many-body resource characterization, with applications across quantum computing, quantum information, and condensed-matter physics.
Magic, or nonstabilizerness, is the resource that lifts Clifford circuits to universal quantum computation and has become a standard diagnostic of many-body states. For a state shared between two parties, however, a basic question has remained open: how much of the magic resides in the correlations between the parties rather than in their local bases? Isolating this nonlocal magic requires minimizing over all local bases, an optimization that has so far resisted exact solution. Here we solve it for the stabilizer fidelity: the nonlocal magic of every pure multiqubit state is the distance of its entanglement spectrum from the closest spectrum of Bell pairs. The same quantity governs an apparently unrelated task: a family of states universally embezzles entanglement under local operations and classical communication if and only if its nonlocal magic diverges. The deciding property is not the amount of entanglement but the way the entanglement spectrum spreads its weight across factor-of-two windows of rank, so that critical chains and random-singlet states, with identical logarithmic entanglement scaling, carry unbounded and vanishing nonlocal magic, respectively. Nonlocal magic thereby becomes an operationally meaningful property of quantum correlations, directly accessible to tensor-network simulations and, through entanglement spectroscopy, to experiments.
We study mathematical, physical and computational aspects of the stabilizer formalism arising in quantum information and quantum computation. The measurement process of Pauli observables with its algorithm is given. It is shown that to detect genuine entanglement we need a full set of stabilizer generators and the stabilizer witness is coarser than the GHZ (Greenberger–Horne–Zeilinger) witness. We discuss stabilizer codes and construct a stabilizer code from a given linear code. We also discuss quantum error correction, error recovery criteria and syndrome extraction. The symplectic structure of the stabilizer formalism is established and it is shown that any stabilizer code is unitarily equivalent to a trivial code. The structure of graph codes as stabilizer codes is identified by obtaining the respective stabilizer generators. The distance of embeddable stabilizer codes in lattices is obtained. We discuss the Knill–Gottesman theorem, tableau representation and frame representation. The runtime of simulating stabilizer gates is obtained by applying stabilizer matrices. Furthermore, an algorithm for updating global phases is given. Resolution of quantum channels into stabilizer channels is shown. We discuss capacity achieving codes to obtain the capacity of the quantum erasure channel. Finally, we discuss the shadow tomography problem and an algorithm for constructing classical shadow is given.
The preparation of high-fidelity logical magic states is a crucial subroutine for universal fault-tolerant quantum computation (FTQC). Predicting the performance of FTQC and developing improved protocols rely on numerical methods to classically simulate logical magic state preparation in the presence of noise. Clifford logic on Pauli-stabilizer codes with circuit-level Pauli errors can be efficiently simulated using Pauli-stabilizer formalism, but the non-Clifford operations required to prepare logical magic states render generic simulation inefficient. We introduce Clifford-stabilizer simulation, an exact and efficient algorithm based on updating a Clifford-stabilizer group to simulate noisy preparation protocols for a broad class of logical magic states used to implement non-Clifford gates in the third level of the Clifford hierarchy under circuit-level Pauli errors. Clifford-stabilizer simulation applies to a range of operations that commonly appear in preparation protocols for such logical magic states, including Pauli-stabilizer measurements, logical Clifford measurements, and transversal non-Clifford gates. Our algorithm for Clifford-stabilizer simulation maps a non-Clifford circuit with sampled circuit-level Pauli errors to a Clifford circuit that exactly reproduces its measurement outcome distribution, achieving time and space complexities polynomial in relevant protocol parameters. We perform exact simulation of magic state cultivation up to fault distance 7 by Clifford-stabilizer simulation. Our method provides a route to perform exact benchmarking of large-scale logical magic state preparation protocols required for useful FTQC.