Qsimulation V2.0: An Optimized Quantum Simulator

Xi Deng, Yuxin Deng

Paul E. Black

Dumitru Novic

We apply the inZOR-ND bio-adaptive genomic discovery engine to quantum error correction (QEC) on real IBM Quantum hardware, without implementing any known QEC code structure. Two versions are presented: v1 (21 Ry parameters, X-only correction with 8 syndromes, 4 test states) and v2 (42 Ry+Rz parameters, Full Pauli correction with 22 syndromes, 6 test states, multi-noise training over 4 CZ error rate levels). Both versions discover hardware-native 7-qubit encoding circuits optimized for the IBM Heron processor's tree topology (ibm_fez physical qubits 3, 2, 4, 16, 1, 5, 23). Across 7 hardware runs on ibm_fez and ibm_marrakesh, inZOR-ND outperforms Steane [[7,1,3]] in all observed hardware runs, with gains from +0.2 to +5.1 percentage points. The key finding is not that inZOR-ND produces a theoretically superior code — in ideal depolarizing noise simulation, Steane's Full Pauli correction remains stronger at all tested noise levels. Rather, these results suggest that genomic search can discover hardware-native QEC encoders whose reduced transpiled cost yields consistent advantage over theoretically stronger but hardware-mismatched codes on NISQ devices. This demonstrates that theoretical optimality is not the same as hardware optimality on current processors. The core hardware advantage: inZOR-ND's native CZ circuits transpile to depth ~53–58 on IBM Heron, while the Steane code (CNOT-based) transpiles to ~115, because CZ is a native IBM Heron gate while CNOT requires decomposition. v2 Rz gates are virtual (zero depth, zero error) on Heron. v1 LUPA precision zoom: simulation fidelity 0.999843 (p_cz=0.002, X-only, 3 rounds × 20 cycles, ~5 min). v2 Qiskit AerSimulator cross-validation: perfect match with NumPy simulator (diff < 1e-16). Key results: - inZOR-ND outperforms Steane in 7/7 observed hardware runs - v1 sim fidelity (LUPA): 0.999843 - v2 sim fidelity (multi-noise avg, Full Pauli): 0.990244 - Best hardware gain vs Steane: +5.1 pp (ibm_marrakesh, noisy regime) - v1 transpiled depth: ~53 | v2: ~58 | Steane: ~115 (IBM Heron) - v1: 21 params (Ry) | v2: 42 params (Ry+Rz) | 12 CZ gates - v2 syndromes: 22 (Full Pauli: I + 7X + 7Y + 7Z) - 8000 shots per circuit, SamplerV2 runtime API

Salvador Elías Venegas-Andraca, José Luis Gómez-Muñoz, Marco O. Lanzagorta +1 more

Akram Abdulsalam Elkaseh, Vallabhapurapu Vijaya Srinivasu, Willem Jakobus Perold +37 more

We would like to make some comments on the flowchart of the Chase-based soft-decision decoding algorithm, i.e., Algorithm 2, in our previous paper [1]. We note that there is a mistake in Fig. 1 in [1]; that is, v1 is initialized to z where z denotes the hard-decision vector of the channel observation r. In our previous simulations, we initially set λ(r,v1) = λ(r,z) = +∞ which guarantees that v2 rather than z is chosen where v2 represents the first decoded codeword. Then v2 will be compared with other valid codewords obtained from different test patterns. Nevertheless, perhaps the first valid codeword v2 and the initial v1 (namely z) satisfy condition (13) in [1] and thus terminate the decoding process. In this case, the codeword v(v = v2) may not be the optimal candidate of the soft-decision decoder since Theorem 1 in [1] is correct if and only if v1 and v2 are both valid codewords and meet condition (13). Fortunately, the performance curves of the (89, 45, 17) QR code presented in [1] were accurate despite the mistake mentioned above. In reality, the true error-rate performance can also be obtained for the (73, 37, 13) and (71, 36, 11) QR codes by utilizing the flowchart of Algorithm 2 in [1]. However, we observed a significant performance gap as illustrated in Fig. 1 when the (23, 12, 7) QR code, i.e., the Golay code, was considered. We guess that such a loss results from the Golay code’s ‘perfect code’ property. It is worth to note that the decoding flowchart in [1] is not optimal from decoding speed point of view since the second valid codeword still needs to be solved even if the first one is the maximum-likelihood (ML) codeword. Actually, it is often the case at high signal-to-noise ratio (SNR). To overcome this, a new flowchart which integrates two different sufficient optimality conditions is developed in the next section.

Krishanu Karun

This repository contains the framework, data, and methodology for TOPAZ v2 (Topology OPtimization Ansatze with Zoning). TOPAZ is a high-performance research framework that introduces Quantum Topology Optimization as a proactive layer for designing noise-resilient quantum circuits. While conventional quantum error mitigation (QEM) operates post-execution on measurements, TOPAZ v2 enables intrinsic resilience by tuning the circuit’s physical parameters—specifically Intensity (ρ) and Timing (τ)—to navigate the parameter manifold toward regions of inherent robustness.