This paper presents the first experimental validation of Causal-AI topological control on real superconducting quantum hardware. Addressing the fundamental bottleneck of ambient $1/f$ flicker noise and dephasing in quantum processors, we operationalize the Entropic-Resistance framework—modeling spacetime as a discrete network and time as a recursive relaxation process—to optimize transmon qubit performance. By extracting deterministic Taylor sensitivity operators ($j_1, j_2$) from real-time calibration data of IBM Quantum’s live ibm_fez processor, we compute and execute an analytically adjusted "Lasky-pulse" amplitude. Comparative hardware execution against standard factory baselines (validated across live QPU job execution logs: da294biein7c73bea68g and da29qpkdedkc73erutlg) demonstrates that topological network optimization successfully bridges discrete recursive spacetime theory with operational quantum control without requiring hardware modifications.
This paper presents the first experimental validation of Causal-AI topological control on real superconducting quantum hardware. Addressing the fundamental bottleneck of ambient 1/f flicker noise and dephasing in quantum processors, we operationalize the Entropic-Resistance framework—modeling spacetime as a discrete network and time as a recursive relaxation process—to optimize transmon qubit performance. By extracting deterministic Taylor sensitivity operators (j1,j2) from real-time calibration data of IBM Quantum’s live ibm_fez processor, we compute and execute an analytically adjusted "Lasky-pulse" amplitude. Comparative hardware execution against standard factory baselines (validated across live QPU job execution logs: da294biein7c73bea68g and da29qpkdedkc73erutlg) demonstrates that topological network optimization successfully bridges discrete recursive spacetime theory with operational quantum control without requiring hardware modifications.
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
This paper establishes a rigorous theoretical bridge between the Entropic-Resistance paradigm and recent experimental visualizations of quantum vacuum fluctuations (Zhang et al., 2026). Traditional physics often treats analog laboratory models as mere mathematical simulations; however, under this framework, spacetime itself is defined as a discrete, algorithmically expanding topological network. Consequently, a Bose-Einstein Condensate (BEC) is treated as a functioning, discrete physical network (G=(V,E)) in its own right. We demonstrate that the fluctuations observed in a two-dimensional BEC analog model are not chaotic thermal noise, but macroscopic constraint reactions of this discrete network. By mapping the experimental parameters directly to a recursive topological graph, we show that radio-frequency state coupling corresponds to a localized relaxation contrast (Δλ). The resulting density oscillations can be deterministically analyzed using Causal-AI Taylor sensitivity operators (j1,j2,j3). Finally, the paper proposes the emergent Q-hierarchy temporal drift as a definitive, falsifiable laboratory test. Because spatial transport and localized relaxation do not commute on the discrete grid ([L,PB]=0), the normalized sensitivity ratios exhibit a systematic temporal drift. Identifying this specific drift in the raw telemetry of BEC vacuum experiments would provide definitive "white-box" proof that quantum vacuum dynamics are governed by discrete, recursive topological geometry.
Quantum information processing holds the promise of a paradigm shift in computational power for specific problem classes. While contemporary quantum processors operate in the Noisy Intermediate-Scale Quantum (NISQ) regime and remain highly susceptible to errors, ongoing experimental efforts on these near-term devices are crucial for advancing the underlying technology. Among the various hardware implementations, superconducting quantum circuits stand out as one of the most robust architectures for scalable quantum technologies. Nevertheless, the primary bottleneck in these systems is the fragility of quantum information: interactions with the environment inevitably perturb the quantum states, a phenomenon known as decoherence. This rapid loss of stored information imposes a fundamental limit on the time available to reliably execute computations on these devices. Within this context, this thesis focuses on the comprehensive experimental characterization of a planar chip featuring isolated, fixed-frequency superconducting transmon qubits. The measurements were conducted in a cryogenic environment, relying on a dilution refrigerator to maintain the device at millikelvin temperatures. To probe the quantum system under these conditions, custom measurement scripts were developed to operate state-of-the-art microwave control electronics (Keysight Quantum Control System), establishing full control over pulse generation, signal acquisition, and digital demodulation. The experimental workflow systematically mapped the physical properties of the device through a progressive series of characterization steps. Initial spectroscopic techniques probed the coupled resonator-qubit system, identifying its fundamental transition frequencies and relevant energy scales. Building upon these frequency-domain measurements, coherent control was achieved by calibrating resonant microwave pulses to drive single-qubit rotations, enabling the reliable preparation of the qubit in excited or coherent superposition states. Finally, specific time-domain protocols were implemented to evaluate the operational limits of the device. By executing standard pulse sequences, the characteristic decoherence metrics of the system, namely the longitudinal energy relaxation time (T1) and the inhomogeneous dephasing time (T2*), were successfully extracted, providing a quantitative assessment of the qubit's overall performance and establishing a baseline for future implementations of more advanced quantum control protocols.
Scaling superconducting quantum processors is fundamentally limited by environmentally induced decoherence arising from nanoscale fabrication defects. Conventional heuristic microwave control protocols cannot dynamically adapt to device-specific material variations, while purely data-driven deep learning approaches often violate physical constraints. Here we introduce a compact quantum physics-informed neural network (QPINN) that learns device-aware microwave control pulses for superconducting qubits. Trained on empirical defect characteristics extracted from 162 InAs nanosheet Josephson junction measurements, the model embeds the Lindblad master equation directly into its loss function to enforce physical consistency. Open-system simulations demonstrate a 42% reduction in leakage to non-computational states. Hardware experiments executed on the 156-qubit IBM Quantum processor show an increase in Ramsey dephasing time from 83.3 ± 7.8 μs to 113.0 ± 1.1 μs while maintaining gate fidelity exceeding 99.99%.
Fault-tolerant quantum computing requires quantum gates that combine short duration, high fidelity, and robustness to noise. As quantum processors scale, robustness becomes increasingly important because exhaustive characterization and frequent recalibration of individual gates become impractical. We present a comprehensive optimal-control framework for controlled-Z (CZ) gates between directly coupled, frequency-tunable transmons that systematically accounts for intrinsic Hamiltonian errors and realistic decoherence mechanisms, including energy relaxation and dephasing. Using smooth, single-shot flux pulses, we obtain 12-ns CZ gates---close to the theoretical speed limit of 11.6 ns---with intrinsic errors suppressed to the 2×10−5 level. We construct dephasing noise models, calibrated against published experimental measurements, that capture fast dephasing from room-temperature control electronics and slow dephasing from 1/f flux noise. Using these models, we identify dephasing sweet spots that suppress sensitivity to dephasing noise. We further exploit transmon asymmetry as a design parameter to access distinct noise-resilient operating regimes. These results establish an experimentally applicable framework for balancing gate speed, fidelity, and noise robustness while reducing characterization and recalibration overhead.
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.