Computational Quantum Chemistry

This journal presents a comprehensive, multidisciplinary investigation into the foundations of resilient computational architectures, spanning eight interconnected volumes. Volume I introduces the Compressive Multi-Projection Homology (CMPH) framework and its resilient evolution R-CMPH for validating high-dimensional AI embeddings through topological stress-testing. Volume II examines quantum hardware architectures, comparing ion trap Raman routing against superconducting IBM Heron processors for qLDPC implementation. Volume III details the 1nm transistor frontier with MoS channels and carbon nanotube gates. Volume IV formalizes the Engagement Lock, an Ising-critical neuromodulatory control manifold for autonomous AI. Volume V establishes the mathematical

This preprint introduces and characterises a collective Fisher-information-inspired magnetisation-feedback term for Kuramoto--XY quantum simulations. The model augments a heterogeneous XY Hamiltonian with a diagonal term proportional to minus lambda times total magnetisation squared, normalised by qubit count, creating explicit magnetisation-sector energy shifts while preserving ideal XY magnetisation conservation. Artefact-generated exact diagonalisation on n = 4, 6, and 8 qubits shows systematic changes in ground energy, spectral width, and spectral gap as the feedback strength varies. The manuscript also reports adjacent-gap statistics, low-energy bipartition entropy trends, and a small n = 4 VQE benchmark comparing a K_ij-informed ansatz against generic TwoLocal and EfficientSU2 baselines. The record archives the source, bibliography, and PDF for a hardware-falsifiable modelling contribution. Claims are limited to the stated exact diagonalisation grid, VQE benchmark configuration, and associated committed artefacts.

This preprint reports a preregistered dynamic-circuit quantum-control experiment comparing monitored feedback against matched open-loop control for a four-qubit Kuramoto--XY synchronisation payload. The experiment uses three system qubits, one monitor qubit, and three dynamic rounds to test feedback directly on IBM gate-model hardware. The primary ibm_kingston binary endpoint is negative: the monitored-feedback arm does not improve the selected target relative to the matched open-loop control. The same S1--S1f stack is then replicated on ibm_fez and ibm_marrakesh, where the primary endpoint is positive but direct-observable extensions show backend-dependent YYI and response structure. The contribution is therefore a hardware-control boundary result: it documents where the monitored-feedback policy fails, where it appears to improve the selected target, and why the result should not be interpreted as a simple backend-independent controller promotion. The record includes the source, bibliography, and rebuilt PDF.

In this paper, a non-repeating quantum algorithm is introduced that depends on detectable Byzantine’s quantum solution that attains Clk synchronization in the arbitrary faultier processes’ presence through utilizing a double quantum system only. Precisely, it has been shown that general relativistic mass–energy equivalences intimate gravitational relation among Clks, however, energy’s quantum mechanical superposition resulted in a non-fixed metric background. Depending only on assumptions that gravitational principles held in such conditions, it has been shown that the Clks essentially gets involve over time dilation effect that ultimately results in a double Clk coherence loss. Therefore, the measured time through a double Clk is not defined precisely. Although, the time’s general relativistic notion is recuperated in the Clks conventional limits.

Hybrid quantum-classical machine learning on noisy intermediate-scale quantum (NISQ) devices suers from three coupled overheads: hardware miscalibration, classical syndrome-decoding latency, and shot-noise on gradient estimators. We present QCOP (Quantum Calibration and Observable Processing), a unified closed-loop control layer integrating a Spectral Calibration Engine, a Hierarchical Syndrome Decoder (HSD), and an Observable Compression Layer within a single GPU-accelerated pipeline. Three theoretical bounds characterize the joint eject on logical error rate, convergence rate, and observable variance. On device-calibrated noise-model simulators (n = 20 seeds), QCOP achieves up to 2× wall-clock training speedup and ≈40% VQE energy error reduction relative to the unaugmented baseline; all simulation should be treated as promising but unconfirmed on physical hardware. A hardware pilot on ibm_nairobi at surface code distance d = 5 (2000 syndrome volumes, 8192 shots each) yields a 1.6× latency reduction and 1.9× logical error rate reduction for the HSD over MWPM directionally consistent with simulation but quantitatively smaller, indicating noise sources beyond the model. End-to-end hardware validation of all three modules remains future work. Code and model weights: github.com/hekimoghlu/QCOP.

Alkyl acrylates are widely used as primary binders in coating formulations for the automobile industry [1À5]. The basic nature of acrylic resins and the processing plants producing the resins have changed considerably over the past decades as a result of environmental limits on the resins' allowable volatile organic contents (VOCs) [6À8]. High temperature ( . 100 C) polymerization, which allows for the production of high-solids lowÀmolecular weight resins, has mostly replaced conventional (low-temperature) polymerization, which produces low-solids high molecularÀweight resins . It has been reported [1,2,11À15] that at higher temperatures, secondary reactions, such as spontaneous initiation, backbiting, β-scission, and chain transfer to monomer and polymer, should be accounted for in describing polymerization of alkyl acrylates. The production of alkyl acrylate polymers with polydispersity indices of 1.5À2.2 [14,16] at high temperatures indicated that various types of chain transfer reactions occur at high rates at the high temperatures. Recent studies using quantum chemical calculations and matrixassisted laser desorption ionization (MALDI) showed that monomer self-initiation is a likely mechanism of initiation in the spontaneous thermal polymerization of alkyl acrylates. Monoradicals (a monomeric monoradical (MMR), M 1 • , and a dimeric monoradical, M 2 • ) generated by the self-initiation mechanism are shown in Fig. .1. A better understanding of the mechanisms of chain transfer reactions is important for developing more efficient high-temperature polymerization processes. Previous studies of thermally self-initiated polymerization of methyl acrylate (MA), ethyl acrylate (EA), and nbutyl acrylate (nBA) using electrospray ionizationÀFourier transform mass spectrometry (ESI-FTMS) and MALDI [16] showed abundant polymer chains with end groups formed by chain transfer reactions. Nuclear magnetic resonance (NMR) analysis of these polymers indicated the possible presence of end groups from chain transfer to monomer 135

The Noisy Intermediate-Scale Quantum (NISQ) era is characterized by high error rates that fundamentally
limit the utility of quantum algorithms. Existing error mitigation techniques—Zero-Noise Extrapolation
(ZNE), Probabilistic Error Cancellation (PEC), and Clifford Data Regression (CDR)—achieve varying degrees of
success but lack a unified signal-processing perspective. Motivated by the recently established isomorphism
between Kraus operator representations and discrete impulse decompositions in digital signal processing
(DSP), we present a novel quantum error mitigation framework that treats noisy quantum channels as linear
time-invariant systems amenable to deconvolution. We adapt classical Wiener filtering theory to the quantum
domain via the Liouville superoperator representation, formulating error mitigation as a linear minimum
mean-square error (LMMSE) estimation problem in the space of density operators. Experimental validation
across five representative NISQ scenarios demonstrates that the Quantum Wiener Filter (QWF) significantly
outperforms ZNE, PEC, and CDR in four of five test cases, achieving 66–93% reduction in observable error for
GHZ state preparation under amplitude damping, VQE energy estimation under thermal relaxation, random
Clifford circuits under depolarizing noise, and QAOA optimization under crosstalk. The method exhibits
statistically significant superiority (Wilcoxon signed-rank test, p < 10⁻⁴) across 30 independent trials per
scenario. The fifth scenario—an adversarial state-dependent noise model—validates the Markovian
assumption underlying the deconvolution framework by demonstrating controlled failure when channel
linearity is violated. Our results establish that the DSP-QM isomorphism enables practical error mitigation
with superior fidelity recovery (mean fidelity 0.96–0.99 vs. 0.78–0.98 for baselines) at modest computational
overhead (1.2× shot budget). This work bridges quantum information theory and classical signal processing,
opening a new avenue for NISQ algorithm optimization.