Version 6.1 develops the Quantum Correlations Stack into a complete multi-resource researcharchitecture.The framework integrates six principal resource families:1. quantum coherence;2. quantum discord;3. quantum steering;4. contextuality;5. entanglement;6. Bell nonlocality.Version 6.1 introduces and preserves a formal separation between:witness ̸= estimator ̸= certification ̸= selection ̸= application utility. (1)Each resource is represented by an estimated state, uncertainty, certification status, imple-mentation cost, latency, robustness, and application utility.A resource-state vector is defined asR = (RC , RD, RS, RV , RE, RB, uC , uD, uS, uV , uE, uB). (2)The selection problem is extended from scalar optimization to constrained multiobjectiveoptimization:R⋆ ∈ arg minR∈HcertF(R; A), (3) where Hcert contains only resources that pass preregistered certification criteria.The Pareto frontier is explicitly retained so that the architecture does not artificiallymanufacture a unique winner from an arbitrary weighting scheme.The framework additionally specifies:• resource certification;• witness-based evidence versus full resource estimation;• uncertainty propagation;• multiplicity control;• classical and quantum reference states;• resource-conversion accounting;• FMEA risk analysis;• Go/No-Go criteria;• configuration locking;• digital audit trails;• economic-cost separation;• independent reproduction;• computational reference implementation.No experimental result is claimed.
Version 6.1 develops the Quantum Correlations Stack into a complete multi-resource researcharchitecture.The framework integrates six principal resource families:1. quantum coherence;2. quantum discord;3. quantum steering;4. contextuality;5. entanglement;6. Bell nonlocality.Version 6.1 introduces and preserves a formal separation between:witness ̸= estimator ̸= certification ̸= selection ̸= application utility. (1)Each resource is represented by an estimated state, uncertainty, certification status, imple-mentation cost, latency, robustness, and application utility.A resource-state vector is defined asR = (RC , RD, RS, RV , RE, RB, uC , uD, uS, uV , uE, uB). (2)The selection problem is extended from scalar optimization to constrained multiobjectiveoptimization:R⋆ ∈ arg minR∈HcertF(R; A), (3) where Hcert contains only resources that pass preregistered certification criteria.The Pareto frontier is explicitly retained so that the architecture does not artificiallymanufacture a unique winner from an arbitrary weighting scheme.The framework additionally specifies:• resource certification;• witness-based evidence versus full resource estimation;• uncertainty propagation;• multiplicity control;• classical and quantum reference states;• resource-conversion accounting;• FMEA risk analysis;• Go/No-Go criteria;• configuration locking;• digital audit trails;• economic-cost separation;• independent reproduction;• computational reference implementation.No experimental result is claimed.
Version 6.1 develops an integrated research framework for a modular particle detector usingmultiple quantum correlation resources.The architecture does not posit one universal quantum resource. Instead, it defines a resourcestackH = {C, D, S, V, E}, (1)together with an adaptive controller operating on measured resource estimates, uncertaintyintervals, detector conditions, and environmental covariates.The V6.1 architecture introduces five major extensions: 1. leakage-resistant experimental data partitioning;2. explicit calibration and domain-shift correction;3. uncertainty-aware adaptive resource selection;4. multi-objective classification and energy inference;5. hierarchical reproducibility and falsification criteria.The detector is represented as a conditional measurement system:p(Y | P, Z, Q, Θ), (2)where P denotes particle state, Z environmental conditions, Q the quantum subsystem, andΘ the locked experimental configuration.The adaptive controller selects a resource or resource subset using a preregistered utilityfunction:r⋆ = arg minr∈HLr. (3)Unlike a simple point-estimate controller, V6.1 explicitly propagates uncertainty and allowsan abstention state when the available evidence does not justify a classification.The primary validation framework evaluates classification, energy reconstruction, backgroundrejection, calibration, calibration transfer, robustness, and independent replication.Version 6.1 additionally integrates an explicit sensor platform layer, an event-conditioningchannel, a multi-resource feature vector, and a readout-and-timing block.No experimental result is claimed.
Quantum metrology exploits physical resources whose parameter sensitivity can exceed that ofconventional measurement strategies under appropriate conditions. However, a resource that isoptimal under one noise, temperature, drift, preparation, or detection regime need not remainoptimal under another.Version 6.1 proposes an adaptive hierarchy metrology architecture in which a predefined set ofprotocol families is available: H = {C, D, S,E}.The central methodological correction of Version 6.1 is that coherence, discord, steering, andentanglement are not themselves identified with quantum Fisher information. Instead, each resourcecorresponds to a physically specified preparation–encoding–noise– measurement protocol. Theresulting parameterized density operator defines the quantum Fisher information (QFI), while theactual measurement defines the classical Fisher information (CFI).The adaptive system therefore estimates, for each candidate protocol,FQr(θ|z), FCr(θ|z), Ir(θ|z) = FCr(θ|z)τr,where r denotes the protocol family, z denotes the measured operating context, and τr is thetotal wall-clock cost per useful trial.The primary optimization target is the information rate, not raw QFI.Selection is uncertainty-aware. Rather than choosing the resource with the largest pointestimate, the system may use lower confidence bounds, switching penalties, feasibility constraints,and hysteresis:rt = arg maxr∈Ft[LCB1−α (Ir|zt) − Pswitch(r, rt−1)] .Version 6.1 additionally introduces an overhead-aware net advantage quantity:Anet =Iadaptmaxr Ifixed,r·11 + ηoverhead,where ηoverhead accounts for characterization, switching, computational, and dead-time coststhat may not be entirely captured by the per-shot cycle-time τr.The test remains locked against target-outcome leakage: the primary adaptive policy may usecalibration and context variables, but it must not use the target measurement outcomes of theheld-out test block to modify the selection rule.The system is evaluated against fixed-resource baselines under equalized wall-clock budgets andheld-out operating conditions. Primary analysis uses paired information-rate comparisons withhierarchical uncertainty estimation.The framework includes preregistered thresholds, falsification criteria, robustness analysis,selection regret, switching stability, shadow-mode validation, missing-data rules, reproducibilityrequirements, audit logs, synthetic reference implementations, and a staged experimental validationplan.
Version 8.1 develops a state–channel–task framework for quantum correlations, with anexplicit pedagogical and diagnostic layer.The framework contains three distinct structures.First, the set-inclusion hierarchy describes exact mathematical relations among classicalcorrelations, separable states, entangled states, steerable states and Bell-nonlocal states:Cclass ⊂ Csep, CBell ⊂ Csteer ⊂ Cent = S \ Csep. (1)Second, operational robustness is defined as a state–channel functional rather than as auniversal ordering of resources. For a resource measure M, noise channel Eθ and initial stateρ0, the survival function isPM(θ) = Pr[M(Eθ(ρ0)) > Mmin] . (2)Third, task utility is defined through an explicitly specified performance functional involvingsensitivity, precision, resource cost, robustness and experimental overhead.Version 8.1 additionally integrates a distance-to-boundary analysis, an explicit correc-tion note on the misstated inclusion Csteer ⊂ Cent, a verification protocol based on the chainSCHSH > 2 ⇒ Rsteer > 0 ⇒ N > 0, and illustrative reference tables with explicit disclaimers.The framework rejects the interpretation that a single universal hierarchy of robustness canbe inferred from set inclusion.No experimental result is claimed.
Version 6.1 develops an integral framework for an analog quantum simulator of spin modelsbased on programmable superconducting-qubit arrays.The principal objective is not merely to construct a quantum device, but to establish atraceable mappingcontrol parameters −→ Hreal −→ quantum evolution −→ observables −→ classical reference.The framework supports transverse-field Ising, XY, XXZ, Heisenberg, long-range, anddisordered spin Hamiltonians.Version 6.1 introduces an explicit Hamiltonian- identification layer between hardwarecalibration and scientific validation.The realized Hamiltonian is decomposed into target terms, static calibration errors, dynamiccontrol errors, crosstalk, leakage, decoherence, and residual unwanted interactions.Validation is organized hierarchically:1. hardware calibration;2. Hamiltonian identification;3. state-preparation validation;4. readout calibration;5. static observable validation;6. dynamical observable validation;7. finite-size benchmarking;8. statistical equivalence testing;9. cross-device reproducibility;10. independent analytical reproduction. The statistical framework replaces the inadequate criterion of “statistical significance ofagreement” with an equivalence framework based on preregistered tolerances.The primary hypothesis is therefore formulated using two one-sided tests (TOST) or anequivalent confidence interval criterion:|∆O| < ϵO.Version 6.1 additionally specifies:• a supplementary Bayesian posterior probability of equivalence as a robustness check;• an explicit distinction between engineering no-go and physical falsification.The framework also specifies negative controls, positive controls, blind analysis, lockedanalysis code, exclusion rules, uncertainty propagation, data provenance, and reproducibilityrequirements.No experimental result or quantum advantage is claimed.
Version 6.1 presents an integral framework for an enhanced GRAPE algorithm for quantumoptimal control.The architecture combines:1. finite-dimensional Hamiltonian control;2. exact or controlled numerical propagation;3. analytic GRAPE gradients using exact Fréchet derivatives;4. gradient verification by independent numerical differentiation;5. robustness to parameter uncertainty;6. robustness to stochastic and quasistatic noise;7. amplitude and slew-rate constraints;8. spectral regularization;9. train/validation/test separation at the level of individual uncertainty realizations;10. multi-start optimization;11. computational-budget-matched comparison; 12. ablation analysis;13. distribution-shift stress testing;14. preregistered statistical analysis;15. independent reproducibility.The central optimization problem is written as a constrained robust optimization problem:maxϵJ(ϵ) (1)subject to amplitude, slew-rate, bandwidth, and numerical constraints.The robust objective is constructed from an in-sample expectation of gate or state-transferfidelity together with explicit regularization terms.A separate held-out distribution is reserved for validation and a locked test distribution isreserved for final evaluation.The framework distinguishes:training performance ̸= validation performance ̸= test performance. (2)The exact propagator derivative is expressed using the Fréchet derivative of the matrixexponential:∂Un∂ϵk,n= D expAn[︃−iℏHk∆t]︃, (3)whereAn = −iℏHn∆t. (4)This formulation avoids the mathematically invalid simplification ∂eA/∂x = eA ∂A/∂x,which only holds under additional commutation conditions.Version 6.1 additionally introduces formal computational-budget specification, ablationexperiments, paired statistical comparisons, bootstrap confidence intervals, multiple-comparisoncontrol, computational profiling, and independent reproduction.No experimental advantage is claimed.
Quantum artificial-life (QAL) claims combine two demanding vocabularies. Artificial-life labels such as inheritance, mutation, interaction, turnover, and selection require population records and lineage controls. Quantum-resource labels such as coherence, entanglement, Bell nonlocality, and channel resources require resource diagnostics and resource-destroying controls. QALBench makes these claims independently auditable. It is a tiered benchmark suite and reference implementation that separates artificial-life adequacy, quantum-resource evidence, and computational nonclassicality. The reference results are deliberately diagnostic. Under matched seeds, basis-selection demographic, phenotype, and lineage summaries are identical for the quantum, dephased, and classical controls, excluding resource diagnostics. A software positive-control assay inserts a stored negativity-derived birth-event score into the reproduction rule and shows that resource-coupled reproduction is removed by dephasing or Markov controls. A no-inheritance resource control remains resource-positive but transmission-negative, establishing that resource-coupled reproduction and inherited resource-mediated artificial life are distinct claims. QALBench also supplies finite-shot certification and simulator-challenge submission workflows for external evidence. Its included reference packages exercise those workflows without asserting quantum advantage, loophole-free hardware certification, open-ended evolution, or persistent population-level entanglement.