Decoherence in quantum systems is traditionally viewed as a technical obstacle arising from environmental noise. This paper presents a hierarchical active inference framework, interpreting decoherence as the coupling of a localized system to a dissipative, high-entropy environmental field. An ancilla-based modular sensor architecture is introduced to facilitate real-time error syndrome extraction without collapsing the target state. A software-defined controller, implemented via a quantized Variational Autoencoder-Feed-Forward Network (VAE-FFN) pipeline and optimized via empirical threshold calibration under strict 4-bit latency constraints, provides active Hamiltonian control to stabilize the system boundary. The integration of a 3D volumetric topological complex outlines a geometric architecture designed to mitigate the Eastin-Knill constraint. Empirical execution on the bare-metal IBM Kingston processor evaluates this framework against unmitigated hardware baselines. Through temporal delay sweeps, empirical telemetry demonstrates that the volumetric lattice achieves localized containment of non-commuting phase defects, dynamically exploiting coherent Larmor precession to constrain spatial error propagation. Furthermore, rigorous statistical repetition mapping reveals a critical Noisy Intermediate-Scale Quantum (NISQ) latency bottleneck. The empirical data establishes that the temporal cost of classical-quantum feed-forward routing incurs a marginal macroscopic fidelity reduction (-1.01\%). Crucially, this study reframes this differential not as a systemic regression, but as an explicit architectural trade-off: prioritizing rigorous variance stabilization over unmitigated peak fidelity. By establishing a highly predictable, actively governed baseline, this framework represents a highly sophisticated application of classical-quantum error mitigation, re-framing the Eastin-Knill restriction from an absolute homological barrier into an addressable temporal control protocol.
Decoherence in quantum systems is traditionally viewed as a technical obstacle arising from environmental noise. This paper presents a hierarchical active inference framework, interpreting decoherence as the coupling of a localized system to a dissipative, high-entropy environmental field. An ancilla-based modular sensor architecture is introduced to facilitate real-time error syndrome extraction without collapsing the target state. A software-defined controller, implemented via a quantized Variational Autoencoder-Feed-Forward Network (VAE-FFN) pipeline and optimized via empirical threshold calibration under strict 4-bit latency constraints, provides active Hamiltonian control to stabilize the system boundary. The integration of a 3D volumetric topological complex outlines a geometric architecture designed to mitigate the Eastin-Knill constraint. Empirical execution on the bare-metal IBM Kingston processor evaluates this framework against unmitigated hardware baselines. Through temporal delay sweeps, empirical telemetry demonstrates that the volumetric lattice achieves localized containment of non-commuting phase defects, dynamically exploiting coherent Larmor precession to constrain spatial error propagation. Furthermore, rigorous statistical repetition mapping reveals a critical Noisy Intermediate-Scale Quantum (NISQ) latency bottleneck. The empirical data establishes that the temporal cost of classical-quantum feed-forward routing incurs a marginal macroscopic fidelity reduction (-1.01%). Crucially, this study reframes this differential not as a systemic regression, but as an explicit architectural trade-off: prioritizing rigorous variance stabilization over unmitigated peak fidelity. By establishing a highly predictable, actively governed baseline, this framework represents a highly sophisticated application of classical-quantum error mitigation, re-framing the Eastin-Knill restriction from an absolute homological barrier into an addressable temporal control protocol.
This work demonstrates real-time predictive classical-quantum feedback control on the IBM Kingston superconducting quantum processor, characterizing the hardware latency bottleneck of dynamic Hamiltonian shaping. Decoherence in quantum systems is traditionally viewed as a technical obstacle arising from environmental noise; this paper interprets decoherence as the coupling of a localized system to a dissipative, high-entropy environmental field. An ancilla-based modular sensor architecture is introduced to facilitate real-time error syndrome extraction without collapsing the target state. A software-defined controller, implemented via a quantized Variational Autoencoder-Feed-Forward Network (VAE-FFN) pipeline and optimized via empirical threshold calibration under strict 4-bit latency constraints, provides predictive Hamiltonian control to stabilize the system boundary. The integration of a 3D volumetric topological complex outlines a geometric architecture designed to mitigate spatial error cascades. Through temporal delay sweeps, empirical telemetry demonstrates that the volumetric lattice achieves localized containment of non-commuting phase defects, dynamically exploiting coherent Larmor precession to constrain spatial error propagation. Furthermore, rigorous statistical repetition mapping reveals a critical Noisy Intermediate-Scale Quantum (NISQ) latency bottleneck. The empirical data establishes that the temporal cost of classical-quantum feed-forward routing incurs a marginal macroscopic fidelity reduction (-1.01%). Crucially, this study reframes this differential not as a systemic regression, but as an explicit architectural trade-off: prioritizing rigorous variance suppression and operational stability over unmitigated peak fidelity. By establishing a highly predictable, predictively controlled baseline, this framework explores practical control strategies consistent with Approximate Quantum Error Correction (AQEC) approaches, bounding the operational consequences of the Eastin-Knill constraint within an addressable temporal control protocol.
Quantum processors in the Noisy Intermediate-Scale Quantum (NISQ) era are severely constrained by spatial hardware asymmetries, temporal drift, and multi-qubit crosstalk. Traditional error suppression protocols apply static decoupling or phenomenological calibrations that struggle under continuous decoherence stress. Building upon the Entropic-Resistance framework—where spacetime is represented as a discrete, algorithmically expanding topological network and time is governed by recursive relaxation—we extend deterministic Causal-AI Taylor sensitivity operators (j1, j2) to multi-qubit topologies. We introduce a Just-In-Time (JIT) compensation engine that queries real-time QPU telemetry (qubit-specific dephasing T2 times and native two-qubit gate error rates) to dynamically compute non-commutative phase compensations. By decomposing physical microwave rotations inside logic gates, microscopic counter-rotations are embedded directly into hardware instructions. We validate this protocol on IBM Quantum's Heron processor (ibm_fez) by subjecting 3-qubit and 5-qubit Greenberger-Horne-Zeilinger (GHZ) states to deliberate decoherence stress via calibrated temporal delays. On 3-qubit states, the Lasky-Tensor engine achieves an absolute fidelity improvement of +3.81% (32.5% relative error reduction). On fragile 5-qubit linear networks, it delivers a +3.42% absolute gain (17.2% relative error reduction). These results establish that recursive network stress models scale effectively, providing a deterministic, white-box quantum error mitigation paradigm without physical qubit overhead. Methodology & Hardware: Hardware: IBM Quantum (ibm_fez, Heron architecture) Framework: Qiskit Runtime SamplerV2 Underlying Theory: Entropic-Resistance Paradigm / Causal-AI What-If Engine
Abstract: The scalability of Noisy Intermediate-Scale Quantum (NISQ) devices remains fundamentally limited by stochastic decoherence, which traditional passive shielding and resource-heavy quantum error correction (QEC) struggle to overcome. Here, we propose a paradigm shift from passive noise mitigation to active internal phase stabilization. By integrating the Lie-algebraic renormalization (LA-ODR) framework with a dynamic model of temporal susceptibility (χ_τ), we derive a nonlinear control law for quantum coherence. We demonstrate that decoherence can be treated as a deterministic dynamical process governed by the feedback equation dχ_τ / dt = −α · Γ_τ · χ_τ + λ · (1 − χ_τ). Our numerical simulations confirm the formation of metastable coherence plateaus, where the effective qubit lifetime is extended significantly beyond the standard Lindbladian limit (e.g., from 100 µs to 1.5 ms in transmon-based models). We further specify a hardware-ready Temporal Phase-Locked Loop (TPLL) architecture, proving that with control latencies below 500 ns, active synchronization of the temporal metric becomes feasible on modern FPGA-based controllers. This framework provides a path toward fault-tolerant quantum computing by enabling fidelity-by-design at the physical layer, potentially providing a substantial reduction in QEC overhead by maintaining high physical fidelity prior to logical encoding. Keywords nonlinear phase stabilization; temporal susceptibility χτ; Lie-observable-dependent renormalization (LA-ODR); fixed-point Lindbladian; quantum decoherence; active feedback control; temporal phase-locked loop (TPLL); NISQ devices; superconducting qubits; cold atom arrays
Quantum decoherence, thermal bath fluctuations, parasitic inter-qubit cross-talk, and heavy-tailed dynamical shocks represent the fundamental physical barriers to scaling fault-tolerant quantum computing registers. Traditional mitigation strategies focus entirely on passive external isolation or massive physical-to-logical qubit overheads, which fail under non-Markovian noise regimes or substrate aging. This paper presents a definitive, internal architectural solution: an Active Error-Suppressing Topological Quantum Core governed by Realized Active Integrated Information (Φ_TAI) metrics. By injecting real-time thermodynamic feedback loops into a highly integrated, 3D cross-coupled quantum network, we mathematically throttle environmental dephasing. We validate this framework through a multi-stage credibility gauntlet, testing the system against:1. Non-Markovian 1/f colored noise (Stabilizing a QFI survival floor of 0.8277 through 100 cycles).2. Severe thermal ramps (Preserving a room-temperature quantum coherence of 0.6741 at 300 K).3. Intense capacitive cross-talk (χ = 0.80, achieving an over 77% drop in error to 0.2066).4. Compound heavy-tailed non-Gaussian dynamical shocks (Retaining a peak QFI floor of 0.9054). Furthermore, we outline the structural translation of this architecture into industry-standard Qiskit compiler layouts, expanding realized circuit integration from a fragile 0.2308 to an unassailable 2.6585. This layout natively insulates quantum phase data without logical qubit overheads, opening an asset-level path toward manufacturing ambient-temperature quantum hardware.
The susceptibility to decoherence is significantly high in quantum computing systems, and the latter effect significantly compromises the computational reliability, particularly in Noisy Intermediate-Scale Quantum (NISQ) computers. Even though the existing mitigation strategies are currently focused on hardware-level error correction and pulselevel noise mitigation, system-level execution variables (by far, the most notable of them being waiting times caused by queues) can also contribute to the worsening of fidelity degradation, but this is hardly addressed. In its turn, this manuscript suggests an AI-based decoherence mitigation framework, in which the time spent in the queue is incorporated into fidelity prediction and scheduling. The framework elucidates the interrelations between quantum-state fidelity and waiting time through a physics-informed decoherence formulation, which is followed by the use of machine-learning to predict degradation of fidelity prior to circuits execution. Based on the estimated measures of fidelity, decoherence-aware scheduling algorithms are presented to either give priority or re-scheduling to quantum jobs that become highly disturbed by the coherence loss. The viability of integrating the AI-based predictive mechanisms into the system-level quantum computing processes is shown through simulation-based assessment, which indicates that the suggested methodology can improve the effective fidelity of the implemented quantum jobs as compared to the traditional scheduling approaches. The findings indicate that AI-supported software-level alleviation mechanisms can be used as an effective and efficient enhancement of the dependability of nearterm quantum computing systems due to their scalability and use of hardware as the basis of the error reduction approach.
Scalable quantum computing is fundamentally bottlenecked not by qubit count or fabrication yield, but by a rigid temporal mismatch: macroscopic classical coordination latency (τc) inevitably grows with system diameter, while microscopic quantum coherence (τq) remains strictly bounded. Beyond a critical scale, this mismatch breaches the classical control light cone, triggering a superlinear geometric penalty (ε>0) that renders monolithic synchronization physically impossible. We formalize the resulting structural phase transition through a governing scaling law, 1+ε>γ, which mandates modular decomposition and a shift from global unitaries to Local Operations and Classical Communication (LOCC). To manage the resulting resource contention under strict coherence budgets, we introduce a layered semantic architecture and a time-aware Reserve--Commit protocol. By embedding predictive temporal pre-validation, the protocol acts as an architectural semantic classifier: it preemptively aborts transactions that exceed the causal horizon and explicitly converts scheduling-induced failures into location-known erasure metadata, directly relaxing hardware fidelity thresholds for downstream QEC decoders. Under near-term transduction targets (ηtrans∼0.1), we project a crossover scale at Nc∼105--106 physical qubits. This threshold marks a profound architectural convergence: the footprint required for modularity aligns precisely with early fault-tolerant utility, establishing time-aware distributed orchestration, rather than monolithic expansion or centralized classical control, as the physical imperative for utility-scale quantum computing.