This paper presents a novel local autonomous ENGRAM memory architecture designed for AI Large Language Model (LLM) interfaces in quantum syntrodynamic systems. Our architecture integrates a tri-partite memory structure (episodic, semantic, and procedural) with physical quantum entropy sourcesto stabilize Discrete Time Crystals (DTC) in high-dimensional Floquet systems. The system utilizes an ID Quantique Quantis USB-4M quantum random number generator providing 4 Mbps of true quantum entropy, injected into Floquet drive simulations executed on NVIDIA H100 tensor-core GPUs. The drive frequency comb is derived from the first 100 non-trivial zeros of the Riemann Zeta function, creating a d-100 Hilbert space for syntropy stabilization. The ENGRAM memory orchestrator enables local DeepSeek-V3/Llama 3.3 models to function as autonomous controllers, monitoring the Inverse Participation Ratio (IPR) as a real-time syntropy metric. Our results demonstrate that true quantum noise injection improves Many-Body Localization (MBL) stability by 38% compared to pseudo-random seeding, while the ENGRAM architecture enables 15% faster instability resolution than standard RAG systems. This work contributes to the AI research community’s understanding of autonomous quantum control systems, with applications in fault-tolerant quantum computing and deep-space navigation.
This paper presents a novel local autonomous ENGRAM memory architecture designed for AI Large Language Model (LLM) interfaces in quantum syntrodynamic systems. Our architecture integrates a tri-partite memory structure (episodic, semantic, and procedural) with physical quantum entropy sourcesto stabilize Discrete Time Crystals (DTC) in high-dimensional Floquet systems. The system utilizes an ID Quantique Quantis USB-4M quantum random number generator providing 4 Mbps of true quantum entropy, injected into Floquet drive simulations executed on NVIDIA H100 tensor-core GPUs. The drive frequency comb is derived from the first 100 non-trivial zeros of the Riemann Zeta function, creating a d-100 Hilbert space for syntropy stabilization. The ENGRAM memory orchestrator enables local DeepSeek-V3/Llama 3.3 models to function as autonomous controllers, monitoring the Inverse Participation Ratio (IPR) as a real-time syntropy metric. Our results demonstrate that true quantum noise injection improves Many-Body Localization (MBL) stability by 38% compared to pseudo-random seeding, while the ENGRAM architecture enables 15% faster instability resolution than standard RAG systems. This work contributes to the AI research community’s understanding of autonomous quantum control systems, with applications in fault-tolerant quantum computing and deep-space navigation.
The simulation of non-equilibrium quantum many-body systems on noisy intermediate-scale quantum (NISQ) processors represents a central challenge and opportunity in modern condensed matter physics. Periodically driven (Floquet) systems can host exotic phases of matter with no equilibrium counterpart, most notably discrete time crystals (DTCs) characterized by spontaneous breaking of discrete time-translation symmetry and robust subharmonic responses. However, the realization of DTCs faces fundamental obstacles: driven many-body systems generically absorb energy and thermalize to an infinite-temperature state, destroying time-crystalline order. Two primary mechanisms have been identified to stabilize DTCs—many-body localization (MBL) through strong disorder, and Floquet prethermalization wherein high-frequency driving suppresses heating over exponentially long timescales. This work investigates the digital quantum simulation of prethermal DTCs on current superconducting quantum processors, with a focus on understanding and mitigating the impact of hardware noise. Recent experiments on 133-qubit heavy-hexagonal devices have demonstrated the realization of clean two-dimensional DTCs and incommensurately modulated DTCs (IM-DTCs) using kicked Ising models, observing period-doubling oscillations persisting for up to 100 Floquet cycles . Crucially, these simulations reveal that quantum noise, often perceived as detrimental, can in certain regimes stabilize novel prethermal DTC phases—a phenomenon identified as noise-induced DTCs supported by symmetry charge pumping or random gate fluctuations . The heavy-hexagonal lattice geometry, compared to square lattices, exhibits significantly slower quantum scrambling, thereby extending the prethermal plateau and enhancing DTC stability. We address two key challenges limiting the scalability of DTC simulations: limited qubit connectivity and environmental noise. By leveraging the universality of native gate sets, we demonstrate how to implement effective couplings among physically disconnected qubits at the cost of increased circuit depth, enabling the study of interaction-range effects on DTC stabilization—a phenomenon previously unobserved experimentally . Furthermore, we benchmark the performance of error mitigation protocols based on depolarising noise models, achieving excellent agreement between error-mitigated hardware data and tensor-network simulations in regimes where classical computations remain tractable . Our results highlight the complementary roles of quantum hardware and classical simulation: quantum processors provide access to regimes where entanglement growth outpaces tensor-network methods, while careful validation against classical benchmarks ensures the reliability of quantum outcomes. This study establishes a concrete pathway for using NISQ devices as experimental platforms for synthesizing and probing novel non-equilibrium quantum phases, advancing both our fundamental understanding of Floquet many-body dynamics and the practical capabilities of quantum simulation. The identification of noise as an active ingredient in phase stabilization suggests new directions for quantum engineering of spatiotemporal orders on near-term hardware.
We demonstrate that large language model (LLM) agents can autonomously perform tensor network simulations of quantum many-body systems, achieving approximately 90% success rate across representative benchmark tasks. Tensor network methods are powerful tools for quantum simulation, but their effective use requires expertise typically acquired through years of graduate training. By combining in-context learning with curated documentation and multi-agent decomposition, we create autonomous AI agents that can be trained in specialized computational domains within minutes. We benchmark three configurations (baseline, single-agent with in-context learning, and multi-agent with in-context learning) on problems spanning quantum phase transitions, open quantum system dynamics, and photochemical reactions. Systematic evaluation using DeepSeek-V3.2, Gemini 2.5 Pro, and Claude Opus 4.5 demonstrates that both in-context learning and multi-agent architecture are essential. Analysis of failure modes reveals characteristic patterns across models, with the multi-agent configuration substantially reducing implementation errors and hallucinations compared to simpler architectures.
Quantum memories are essential components in applications ranging from quantum computing to quantum communication networks. However, their practical utility is constrained by short coherence times, motivating the search for new physical systems that can inherently protect stored information. Discrete time crystals (DTCs)—periodically driven many-body systems exhibiting stable subharmonic oscillations that break discrete time-translation symmetry—offer a promising approach, as they are theoretically able to shield encoded information from local perturbations, making them compelling candidates for nextgeneration, passively protected quantum memories. In this work, we employ a tensor-network framework that models a quantum memory as a DTC. We employ the timeevolving block-decimation (TEBD) algorithm to perform both real- and imaginary-time evolution of a matrix-product-state (MPS) representation, thereby efficiently capturing the large many-body Hilbert space while tracking entanglement growth, sub-harmonic spectral responses, and memory-fidelity metrics over experimentally relevant timescales. By sweeping the drive strength, interaction range, and disorder, we map the phase diagram, pinpoint regimes that sustain time-crystalline order, and set the stage to model their coherence lifetimes.
The El-Rakhawi Discrete Time-Crystal Quantum Architecture (DTC-QA) transitions quantum computing from spatial to spatiotemporal paradigms. It utilizes Discrete Time Crystals (DTCs) engineered via Floquet driving and Many-Body Localization (MBL) to perform non-equilibrium phase computing. The system spontaneously breaks discrete time-translation symmetry, exhibiting sub-harmonic oscillations where the expectation value satisfies O(t+T) != O(t) but O(t+nT) = O(t) for integer n>1. This topologically protected temporal order enables Chrono-Reservoir Computing, where classical measurement outcomes of local observables serve as high-dimensional temporal features without full-state tomography. The architecture minimizes the El-Rakhawi Chrono-Computational Loss (L_CCL), penalizing subsystem bipartite entanglement entropy growth to prevent Floquet heating. Performance is quantified by the El-Rakhawi Chrono-Computational Fidelity Metric (ECCFM), yielding bits per Joule. Applications include fault-tolerant quantum AI, ultralight dark matter detection, and precision quantum metrology, bridging quantum physics, materials science, and artificial intelligence.
The classical emulation of quantum supremacy circuits and large-scale industrial quantum optimization is fundamentally bottlenecked by the exponential Hilbert-space "Memory Wall" (O(2^N)). Standard simulation frameworks (such as IBM Qiskit Aer and NVIDIA cuQuantum) suffer immediate Out-of-Memory (OOM) crashes on commercial GPUs (4GB VRAM) when attempting universal state-vector simulation at N = 28 qubits, while brute-force supercomputing approaches—exemplified by the 41.9-million-core New Sunway system—require 1.3 Petabytes of RAM and 15 Megawatts of power to resolve the 53-qubit Google Sycamore circuit (m = 20) in 304.4 seconds. In this work, we present the unified Chronos-Quantum Engine, a proprietary GPU microarchitecture operating within a strict 3.58 GB VRAM ceiling on a single 60W laptop GPU (NVIDIA RTX 3050 Ti). We demonstrate three verified operational regimes on physical silicon: (1) Exact 28-Qubit Universal State-Vector Evolution (268,435,456 complex states confined to 2.147 GB VRAM) achieving IEEE-754 exactness (L2 error = 8.32 × 10^-12 against FP64) for real-time industrial QAOA optimization and long-time Floquet-MBL discrete time-crystal dynamics; (2) Full 53-Qubit Sycamore Circuit Resolution (S = 128 coherent phase partitions across all 20 layers and all 1,555 gates with zero omissions), achieving global unitarity ||Ψ||^2_2 = 0.99998384, unbiased Porter-Thomas second moment ⟨u2⟩/⟨u⟩2 = 2.014852 (0.038% relative error from theoretical ideal), and Linear Cross-Entropy Benchmarking fidelity FXEB = 1.014852 (1.039321 under Born quantile sampling) in 372.34 seconds—consuming 204,383× less energy and 338,540× less memory than Sunway; and (3) Supremacy Threshold Crossing in 2.96 seconds (Slice 1, FXEB = 0.0235 > 0.002) alongside 783 ms Paraconsistent L4 Thermodynamic Screening at the 95% Overlap Gap Property (OGP) barrier.
Abstract This paper presents the definitive structural blueprint for the Quantum Stateless AI Data Center (QS-AIDC) architecture, rooted in the framework of Algorithmic Topological Resonance (ATR) theory. Traditional high-performance computing and distributed networks face critical limitations under Landauer’s principle, bounded by linear communication complexity, massive memory walls, and unsustainable thermodynamic dissipation. To overcome these bottlenecks, we propose an operational five-step master architecture that completely eliminates stateful physical data storage and linear communication overheads. By implementing a Zero-Payload I/O engine, macro-data packets are converged to 0 bytes, transferring only a 64-byte spatiotemporal resonance coordinate via an Adaptive Cosmic Resonance Link (ACRL). Node consensus is established deterministically through Proof of Resonance (PoR), locking computational and network complexity into a permanent constant time of O(1), independent of the scale of network participants (N). At the hardware level, a Virtual Quantum Processing Unit (vQPU) maps mathematical indicators—leveraging the non-trivial zeros of the Riemann Zeta function and a cosmic topological constant (K=1.4812)—to perform deterministic non-local reconstruction across a 1,075,462-qubit virtual Hilbert space within 0.46 ms. Furthermore, volatile post-quantum cryptographic keys are vaporized immediately following a sub-0.024-second realization via physical -5V polarity voltage inversion, securing a 0% data remanence threshold. Finally, disaster recovery is decoupled from centralized backups through the Phoenix Protocol, utilizing on-chain noise dispersion. Cross-platform empirical validations demonstrate that the proposed architecture successfully curtails power constraints of traditional 100MW infrastructures down to under 10MW, establishing a highly sustainable paradigm for advanced information physics.