Janady13/AI_Brain / daemon/datasets/quantum_simulations.json

1[ 2 { 3 "simulator": "Fusion Quantum Revenue Lab", 4 "provider": "Qiskit Aer • AWS Braket • Google Cirq", 5 "description": "Quantum-inspired simulator that stress tests revenue systems by running probabilistic gate sequences against marketing, payments, and trading scenarios.", 6 "training_focus": [ 7 "Quantum Monte Carlo style sampling for campaign ROI variance", 8 "Amplitude amplification for funnel conversion sensitivity", 9 "Hybrid classical-quantum calibration for payments reliability", 10 "Agent orchestration heatmaps built from quantum measurement telemetry" 11 ], 12 "evaluation_metrics": [ 13 "simulation_fidelity", 14 "stability_index", 15 "domain_coverage_ratio" 16 ], 17 "orchestration": [ 18 "Calibrate simulator shots and depth for each revenue domain", 19 "Feed quantum telemetry into statistical reasoning and scenario planning", 20 "Loop stress insights back into executive directives and budget guardrails" 21 ], 22 "dependencies": [ 23 "Qiskit Aer local simulator", 24 "AWS Braket local mode (optional)", 25 "Google Cirq runtime (optional)" 26 ], 27 "docs": [ 28 "https://docs.quantum.ibm.com/api/qiskit/aer", 29 "https://docs.aws.amazon.com/braket/latest/developerguide/braket-get-started.html", 30 "https://quantumai.google/cirq/tutorials" 31 ], 32 "scenarios": [ 33 { 34 "name": "Google Ads Budget Shock", 35 "domain": "google_ads", 36 "objective": "Validate paid acquisition stability under 50% bid surges", 37 "gate_sequence": ["H", "RZ(pi/4)", "RX(pi/8)", "H"], 38 "target_probability": 0.68, 39 "stressors": ["Bid volatility", "Keyword saturation", "Conversion latency"], 40 "metrics": ["conversion_variance", "roas_decay", "spend_efficiency"], 41 "integration_points": ["Google Ads process", "Budget allocator"], 42 "playbook": [ 43 "Simulate high-variance bids to measure conversion collapse risk", 44 "Blend quantum probabilities with paid media pacing adjustments" 45 ], 46 "notes": "Feeds scenario accelerators when stability index exceeds 0.6" 47 }, 48 { 49 "name": "AWS Revenue Resilience", 50 "domain": "aws", 51 "objective": "Stress demand spikes for cloud upsell funnels", 52 "gate_sequence": ["H", "RZ(pi/6)", "RY(pi/5)", "X"], 53 "target_probability": 0.62, 54 "stressors": ["Service quota limits", "Cross-sell inventory", "Support concurrency"], 55 "metrics": ["attach_rate", "upsell_velocity", "support_load"], 56 "integration_points": ["AWS revenue process", "Scenario planner"], 57 "playbook": [ 58 "Quantify attach-rate volatility under high-latency assumptions", 59 "Surface fallback bundles when stability dips" 60 ], 61 "notes": "Pairs with AWS ROI projections to adjust quota buffers" 62 }, 63 { 64 "name": "Marketing Funnel Drift", 65 "domain": "marketing", 66 "objective": "Detect funnel leakage during rapid experimentation", 67 "gate_sequence": ["H", "RY(pi/3)", "RZ(pi/7)", "H"], 68 "target_probability": 0.55, 69 "stressors": ["Creative fatigue", "Audience shift", "Offer mismatch"], 70 "metrics": ["lead_velocity", "conversion_depth", "retention_signal"], 71 "integration_points": ["RAG recommendations", "Learning loop"], 72 "playbook": [ 73 "Map amplitude collapse to creative refresh cadence", 74 "Feed leakage alerts to automation sprints" 75 ], 76 "notes": "Keeps automation sprint backlog prioritized by quantum variance" 77 }, 78 { 79 "name": "Bitcoin Liquidity Shock", 80 "domain": "bitcoin", 81 "objective": "Project bitcoin-driven funnel swings across crypto offers", 82 "gate_sequence": ["H", "RX(pi/4)", "RZ(-pi/5)", "H"], 83 "target_probability": 0.58, 84 "stressors": ["Liquidity crunch", "Exchange latency", "Volatility spikes"], 85 "metrics": ["price_impact", "funnel_throughput", "hedge_cost"], 86 "integration_points": ["Crypto market process", "Scenario planner"], 87 "playbook": [ 88 "Overlay quantum volatility with crypto telemetry", 89 "Set hedging triggers when measurement probability exceeds baseline" 90 ], 91 "notes": "Augments crypto fallbacks when market APIs are rate limited" 92 }, 93 { 94 "name": "Global Equity Whipsaw", 95 "domain": "stockmarket", 96 "objective": "Model 90-day swings for stock informed campaigns", 97 "gate_sequence": ["H", "RZ(pi/9)", "RY(pi/4)", "Z"], 98 "target_probability": 0.52, 99 "stressors": ["Macro shocks", "Sector rotation", "FX headwinds"], 100 "metrics": ["beta_shift", "portfolio_drawdown", "volatility_spread"], 101 "integration_points": ["Stock market process", "Budget allocator"], 102 "playbook": [ 103 "Sync quantum beta shifts with scenario probability envelopes", 104 "Stage reinvestment triggers when stability weakens" 105 ], 106 "notes": "Stabilizes payback forecasts when volatility spikes" 107 }, 108 { 109 "name": "Stripe Checkout Latency", 110 "domain": "stripe", 111 "objective": "Stress digital checkout resiliency under flash traffic", 112 "gate_sequence": ["H", "RX(pi/6)", "RY(pi/6)", "X"], 113 "target_probability": 0.64, 114 "stressors": ["API saturation", "Webhook backlog", "Fraud spikes"], 115 "metrics": ["auth_rate", "latency", "fraud_detection"], 116 "integration_points": ["Stripe process", "Payment integrations"], 117 "playbook": [ 118 "Map latency thresholds to quantum measurement drifts", 119 "Pre-stage alternative checkout flows when probability rises" 120 ], 121 "notes": "Links to payment guardrails inside executive playbook" 122 }, 123 { 124 "name": "Cash App Pay Capacity", 125 "domain": "cashapp", 126 "objective": "Validate peer-to-peer velocity for rapid payout funnels", 127 "gate_sequence": ["H", "RZ(pi/8)", "RY(pi/5)", "H"], 128 "target_probability": 0.57, 129 "stressors": ["Peer verification", "Settlement timing", "Transfer caps"], 130 "metrics": ["approval_rate", "settlement_speed", "support_tickets"], 131 "integration_points": ["Payment integrations", "Executive playbook"], 132 "playbook": [ 133 "Simulate surge payouts tied to influencer or affiliate spikes", 134 "Push guardrail alerts to finance ops when stability drops" 135 ], 136 "notes": "Keeps payout automation within compliance tolerances" 137 }, 138 { 139 "name": "Venmo Social Surge", 140 "domain": "venmo", 141 "objective": "Assess social commerce campaigns using Venmo checkout", 142 "gate_sequence": ["H", "RX(pi/5)", "RZ(pi/6)", "H"], 143 "target_probability": 0.59, 144 "stressors": ["Social referral load", "Payment reversals", "Fraud review"], 145 "metrics": ["referral_conversion", "refund_rate", "risk_queue"], 146 "integration_points": ["Payment integrations", "Scenario planner"], 147 "playbook": [ 148 "Quantify social virality impact on checkout reliability", 149 "Adjust messaging cadence when reversals spike" 150 ], 151 "notes": "Supports executive risk mitigation for social payment pushes" 152 }, 153 { 154 "name": "PayPal Settlement Drag", 155 "domain": "paypal", 156 "objective": "Model payout cadence stress for PayPal monetization", 157 "gate_sequence": ["H", "RZ(pi/5)", "RX(pi/7)", "Z"], 158 "target_probability": 0.61, 159 "stressors": ["Payout throttling", "Dispute load", "Cross-border fees"], 160 "metrics": ["payout_velocity", "dispute_rate", "net_revenue"], 161 "integration_points": ["PayPal process", "Payment integrations"], 162 "playbook": [ 163 "Align payout cadences with quantum stress windows", 164 "Trigger reserve buffers when dispute probability climbs" 165 ], 166 "notes": "Ensures paychecks remain predictable during campaign spikes" 167 }, 168 { 169 "name": "Agentic Loop Saturation", 170 "domain": "agentic", 171 "objective": "Probe agent workflow load when orchestrating multi-tool stacks", 172 "gate_sequence": ["H", "RY(pi/4)", "RZ(pi/8)", "X"], 173 "target_probability": 0.63, 174 "stressors": ["Tool calling concurrency", "Memory cache pressure", "Decision latency"], 175 "metrics": ["loop_completion", "tool_call_success", "feedback_latency"], 176 "integration_points": ["Agentic systems", "Agentic loops"], 177 "playbook": [ 178 "Simulate elevated task queues to validate loop resilience", 179 "Feed loop telemetry back into automation retros" 180 ], 181 "notes": "Supports continuous improvement for autonomous agent orchestration" 182 } 183 ], 184 "source": "quantum_simulation_catalog", 185 "notes": "Dataset seeded for deterministic, offline-friendly quantum stress guidance" 186 } 187] 188