This technical note documents a prototype-level demonstration of the Interplanetary Command Mesh (IPCM) architecture executed on IBM superconducting quantum hardware. The purpose of the work is to show that a structured quantum output can move beyond passive observation and serve as the causal trigger for a real downstream system event. In repeated runs on the ibm_marrakesh backend, the IPCM circuit preserved a constrained dominant support family consistent with the pre-measurement state structure. A dominant measured basis state was selected from that stable support family, decoded into a command token (PING), and used to trigger a live UDP beacon from a sender machine to a separate receiving machine on a local network. The receiving endpoint successfully logged the transmitted payload, including backend, job, dominant state, and support-family data. This result does not claim direct QPU-to-QPU signaling, faster-than-light communication, or a fielded secure communications system. Rather, it establishes a narrower but important prototype primitive: a real hardware quantum output can be decoded into an operational command and used to drive a completed cross-machine signaling event. In that sense, IPCM is demonstrated here not merely as a structured-collapse circuit, but as a working quantum-to-classical command-delivery prototype. The note includes the prototype architecture, execution flow, real hardware job references, sender/receiver console evidence, and repeatability discussion across multiple submissions.
This technical note documents a prototype-level demonstration of the Interplanetary Command Mesh (IPCM) architecture executed on IBM superconducting quantum hardware. The purpose of the work is to show that a structured quantum output can move beyond passive observation and serve as the causal trigger for a real downstream system event. In repeated runs on the ibm_marrakesh backend, the IPCM circuit preserved a constrained dominant support family consistent with the pre-measurement state structure. A dominant measured basis state was selected from that stable support family, decoded into a command token (PING), and used to trigger a live UDP beacon from a sender machine to a separate receiving machine on a local network. The receiving endpoint successfully logged the transmitted payload, including backend, job, dominant state, and support-family data. This result does not claim direct QPU-to-QPU signaling, faster-than-light communication, or a fielded secure communications system. Rather, it establishes a narrower but important prototype primitive: a real hardware quantum output can be decoded into an operational command and used to drive a completed cross-machine signaling event. In that sense, IPCM is demonstrated here not merely as a structured-collapse circuit, but as a working quantum-to-classical command-delivery prototype. The note includes the prototype architecture, execution flow, real hardware job references, sender/receiver console evidence, and repeatability discussion across multiple submissions.
Version update: This record has been updated to remove enabling implementation details pending patent prosecution. Technical implementation is available under NDA. This preprint documents three quantum-native sensing kernels implemented on current IBM superconducting hardware under the Quantum State Command Encoding (QSCE) architecture. Rather than treating a quantum processor as a probabilistic number generator, QSCE treats the device as a command substrate, where quantum state is executable code and collapse is the execution of a control instruction. The work presents three low-qubit, shallow-depth sensing primitives designed to convert small, controlled perturbations into structured, repeatable changes in collapse-band behavior around a deterministic baseline: QMCA — a 1-qubit collapse-response kernel that converts small perturbations into calibrated changes in a non-baseline population while preserving a stable baseline anchor. SQCA — a 1-qubit hybrid-response kernel consistent with superconducting drive behavior, exhibiting a deterministic baseline anchor and a complementary deviation profile under perturbation. EBA — a 2-qubit correlation-based kernel producing a repeatable joint-distribution response signature under perturbation while retaining a deterministic baseline band. All three kernels are validated on real IBM backends (4096 shots per configuration), showing: Deterministic baselines under the null condition, with >99 of counts concentrated in a single “quiet” band, and Monotonic, structured deviations in non-baseline states under small perturbations, consistent with a calibrated response signature. Within QSCE, these sensing kernels are treated as command-native primitives: baseline and deviant collapse bands are assigned explicit operational semantics (“no signal,” “signal present,” “threshold exceeded”), so that collapse functions as a decision event rather than merely a readout step. The note contrasts these primitives with bespoke quantum magnetometers (e.g., NV and vapor-cell platforms) and argues that QSCE kernels trade extreme sensitivity for programmability, integrability, and substrate-level command semantics on today’s NISQ hardware. This preprint also situates the sensing kernels alongside recent QSCE benchmarking on IBM hardware (including RCS-style metrics on 16-qubit circuits), highlighting a unified, control-first approach to quantum sensing and computation. Enabling implementation details are withheld in this public version pending ongoing patent prosecution; technical details are available under NDA.
This record contains the Madmartigan 96-Active-Qubit Structured-Output Benchmark package developed under the Quantum State Command Encoding (QSCE) research program. The benchmark documents the progression from an original 16-qubit Madmartigan structured-output circuit into a controlled 96-active-qubit, six-tile NISQ benchmark executed on IBM superconducting quantum hardware. The objective was not merely to increase qubit count, but to test whether a designed quantum circuit could preserve a reference-specific structured-output band across multiple physical tile regions under real hardware constraints. The final benchmark package includes execution on IBM Marrakesh using 96 active qubits, 156 measured qubits, six simultaneous 16-qubit tiles, 4096 shots per run, no quantum error correction, and no post-selection. The primary GLOBALPACK T6 rank-2 layout is accompanied by calibration-aware 64-active-qubit replication results, multi-seed robustness testing, an 831-depth 96-active-qubit stress layout, same-layout generic RCS controls, phase-scrambled architecture-adjacent controls, and partial-entanglement ablation analysis. The central finding is that the Madmartigan circuit preserved repeatable, reference-specific structured-output behavior across tiled hardware execution, while same-layout generic RCS controls and phase-scrambled controls failed to reproduce the Madmartigan reference band. The package includes statistical raw-versus-control separation, physical tile-map visualizations, and reproducibility artifacts to support independent technical review. Included materials may include some or all of the following: benchmark report, QASM3 circuit exports, QPY circuit artifacts, raw hardware counts, metadata, analysis CSVs, physical tile maps, scanner outputs, statistical comparison files, figures, and supporting run scripts. These artifacts are provided to support auditability, reproducibility review, and continued evaluation of QSCE/Madmartigan structured-output preservation as a potential near-term NISQ utility pathway. This work is positioned as a structured-output preservation benchmark rather than a universal quantum advantage claim. Its relevance lies in testing whether quantum hardware output can be engineered into stable, classifiable, reference-specific signal bands that may support future quantum-to-classical handoff, signaling, authentication, command validation, and cyber-hardening primitives. Reproducibility package available upon request at frank@quantummidiposse.com
Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.
This paper tests a specific, falsifiable hypothesis: under standard quantum mechanics, measurement order on sender qubits cannot produce statistically distinguishable distributions on receiver qubit measurements. Any decoder operating solely on receiver statistics should achieve only chance accuracy (50%). To test this, we designed and executed experiments on real quantum hardware (IQM Garnet and Rigetti Ankaa-3 via AWS Braket). Using 8-qubit W+GHZ entangled structures, we encoded binary information by varying collapse order on sender qubits and decoded by measuring receiver qubit statistics. Results contradict the null hypothesis: 100% classification accuracy (10/10 bits correctly decoded) 23σ statistical significance (χ² = 548.45, p = 2.74×10⁻¹²¹) Cross-platform replication on Rigetti Ankaa-3 (5σ) Control experiments without entanglement: 50% accuracy (random chance) Witness qubits rule out electromagnetic crosstalk The protocol parameters (topology, collapse-order mapping, decision threshold) were pre-specified in July 2025—six months before hardware validation—eliminating post-hoc tuning concerns. Supplementary materials S1 and S2 document this timeline with SHA-256 hashes. These results demonstrate that collapse-order information can be encoded and decoded within entangled quantum systems, implementing a semantic communication protocol that requires entanglement and cannot be explained by standard quantum mechanics combined with calibrated device noise. Archive includes: complete manuscript, Jupyter notebook with experiment code, raw JSON results from AWS Braket tasks, prior protocol documentation (S1), and hardware-calibrated simulation suite (S2).
Connecting AI-generated quantum programs to executable experiments requires more than syntactically valid code: the workflow must also control execution authority and preserve the basis for interpreting its results. This paper develops a conceptual architecture organized around three responsibilities: AI-assisted circuit proposal, deterministic classical validation, and quantum measurement execution. A quantum-job contract binds circuit artifacts, measurement settings, resource allocations, backend information, and provenance to explicit admission conditions. The design separates permission to run an experiment from evidence that its output answers the intended scientific question. An analytical single-qubit variational example demonstrates this distinction through exact energy calculations, finite-shot variance, and budget-constrained measurement allocation. Responsibility allocation, runtime interception, and staged transformation provide the architectural principles, while uncertainty reporting and reference calculations supply the scientific checks. A prospective evaluation protocol covers accuracy, policy violations, reproducibility, end-to-end cost, and researcher effort. The framework is presented as a design proposal supported by mathematical illustration, not as a tested implementation; its operational benefits and applicability to larger quantum workloads remain to be established.
Quantum Computing is advancing from isolated prototypes toward heterogeneous, cloud-accessed quantum processors that are noisy, capacity-limited, and operationally diverse. In this landscape, the prevalent device-centric execution model (i.e., compiling a circuit for a single backend and running all shots monolithically) constrains scale, reliability, and cost-effectiveness. This thesis argues for and instantiates a system-centric paradigm of distributed execution, which treats execution as an engineered process that orchestrates spatial and temporal distribution across multiple devices and time scales, guided by explicit Quality-of-Service (QoS) objectives. Spatial distribution aggregates multiple, heterogeneous Quantum Processing Units and supporting classical services into a shared resource pool, placing circuits, subcircuits, and workloads where their algorithmic needs best align with hardware characteristics and availability. Temporal distribution adapts execution over time, modulating sampling depth, pacing, and scheduling in response to drift, queue dynamics, and evidence of statistical convergence. Together, these dimensions are governed by policy-driven orchestrations that reconciles fidelity, latency, and cost, and that elevates user intent above device idiosyncrasies. This thesis delivers five main contributions: (1) a shot-wise execution methodology that distributes measurement shots across heterogeneous QPUs under policy control, reducing output variance and matching or surpassing single-device averages; (2) Quantum Broker, a QoS-aware orchestration framework with the Quantum QoS Specification Language (QQSpec) that consistently finds feasible, high-quality allocations and outperforms single-backend and uniform-shot baselines under competing fidelity/latency/cost goals; (3) Cut&Shoot, an integrated circuit-cutting + shot-wise pipeline that executes large circuits across multiple devices with significantly lower error than monolithic, cutting-only, or shot-wise-only baselines, at negligible time overhead relative to standard cutting; (4) a hybrid orchestration architecture, QCRAFT Scheduler–Shotwise, which combines high-level circuit scheduling with shot-wise distribution to achieve up to 95% cost reduction and 92% fewer job submissions while improving robustness to hardware variability; and (5) IncrementalExecution, an adaptive, evidence-seeking shot manager that approximates optimal allocations with minimal overhead and high fidelity across unseen circuits and backends. Evaluations on simulated and real cloud backends demonstrate practicality across heterogeneous providers. Situated within Quantum Software Engineering, this thesis promotes abstractions and runtime mechanisms that make distribution a first-class program property rather than a backend option. The resulting perspective embraces hardware diversity as an optimization opportunity, improves portability across providers, and lays a path from today’s Noisy Intermediate-Scale Quantum (NISQ) platforms to future networked and fault-tolerant regimes. The overarching vision is that, by engineering execution as a principled, evidence-seeking process, heterogeneous quantum resources become the substrate for scalable, reliable, and efficient Quantum Computing.