Design of a Fault‑Tolerant‑Metric‑Aware, Reversible n‑bit Quantum Arithmetic Logic Unit using IBM Qiskit

This paper presents a reversible, n‑bit Quantum Arithmetic Logic Unit (QALU) implemented in Qiskit that supports a classical ALU‑like instruction set: ADD, SUB, CMP, AND/OR/XOR and their negations, unary NOT, shifts and rotates, and operand passthrough. The QALU outputs a result register and status flags N, Z, C, V (negative, zero, carry/no‑borrow, signed overflow) and emits explicit comparison outputs EQ, LT _u , LT _s . The design is modular and operation‑selectable at compile time, enabling clean verification and resource accounting. To evaluate fault‑tolerant efficiency, the circuits are decomposed into a Clifford + T basis and measure T‑count alongside CX‑count and depth. Exhaustive verification on Aer (matrix‑product‑state simulation) confirms correctness for all input pairs for n = 4 across 16 operations. Hardware experiments on IBM Quantum (limited to n = 2) demonstrate end‑to‑end execution with dynamical decoupling and gate twirling, and readout mitigation via mthree. An analytical scaling discussion grounded in ripple‑carry adder theory and known Toffoli/T‑gate constructions is further provided. These results show that a practical, verifiable QALU with flags and compare can be implemented within Qiskit while exposing meaningful fault‑tolerant metrics and hardware‑realistic performance characterization. Key findings from the provided implementation artifacts: (1) Aer exhaustive verification (n = 4): the QALU was exhaustively tested over all 2 ²ⁿ =256 input pairs for each of 16 operations (4096 total tests), with 0 mismatches against a classical reference model. (2) Resource metrics (n = 4): after transpilation to a Clifford + T‑style basis {cx, h, s, sdg, x, z, t, tdg} at optimization level 3, the highest‑cost operations (ADD, SUB, CMP) report T‑count = 86, CX‑count ≈ 102–106, and depth ≈ 138 on a 24‑qubit circuit instance (including flags and MCX ancillae). (3) IBM hardware test (n = 2): on an automatically selected IBM backend (ibm_torino) using dynamical decoupling and gate twirling plus mthree readout mitigation, an exhaustive run over all 256 circuits yielded raw correctness 91.41% and mitigated correctness 91.02% (slightly worse post‑mitigation, consistent with practical tradeoffs where mitigation noise/calibration drift can dominate at small sizes).