A hybrid quantum-classical AI: quantum coherence for rapid decisions, emergent behavior for self-evolving intelligence, quantum-enhanced efficiency for secure data operations. This page presents the product description as written, then recomputes all seven of its equations by hand in your browser.
As written in the source. Deployable in 2025 with existing 20-qubit quantum processors (IBM, Rigetti) and 300 GPUs.
Quantum superposition represents 10^5 decision paths in a state |psi> = sum of a_i(t) |i>, processing complex scenarios in 0.06 seconds, 5x faster than SOTA benchmarks (GPT-4's 1-second response). Governed by a system-driven Hamiltonian (H = -sum J_ij s_i s_j), it collapses possibilities into optimal outcomes without reliance on random or observer-based triggers.
Layered quantum-classical synergy scales complexity non-linearly, producing self-correcting, adaptive intelligence that mimics emergent consciousness. Reasoning amplifies from 5x to 10x without proportional input, synthesizing disparate data into novel, actionable strategies.
Data compressed at 6x efficiency (75TB to 12TB) using quantum-inspired semantic encoding, secured by 2^128 quantum key distribution (QKD). Vast datasets stored and retrieved (0.4s vs 2.5s) with unbreakable encryption, even against future quantum threats.
Immediate Service, 2025 deployment. 20-qubit quantum processors, 300 GPUs, 10,000 edge sensors, delivered in partnership with Two Six Labs. Targets a $20 million pilot for DARPA's QBI and Cyber Command's Cyber Flag 2025 exercise. Cost: $20 million setup, $3 million per year operations, stated to yield $30 billion in annual DoD savings.
Scalable Product, 2028-2030 evolution. A modular AI platform scaling to 100-200 qubits, 1000 GPUs, and 100,000 edge devices, transitioning via DARPA's Constellation program to a DoD-wide solution. By 2030: 10x reasoning (0.03s), 8x logic (99%), 12x storage (500TB to 40TB), stated to save $150 billion annually.
$20 million pilot in 2025 proves the concept. $75 million in 2028 scales hardware. $5 billion to $10 billion contracts by 2030 deploy globally. Follow-on contracts of $200 million to $500 million by 2027 are stated to be justified by 1% of the $850 billion FY2025 budget.
"QECA is a hybrid quantum-classical AI that fuses quantum coherence and emergent intelligence to deliver 5x faster reasoning, 4x smarter logic, and 6x leaner storage, deployable now with 20 qubits and 300 GPUs." The ask: a $20 million pilot in 2025, $500 million follow-on by 2027, $10 billion by 2030. The source calls this DARPA's "strategic surprise: defense wins today, quantum breakthroughs tomorrow."
Each calculator takes the source's own formula and inputs, computes what the formula actually yields, and shows the source's stated result beside it. Change any input and the outputs update live. Nothing here is taken on the source's word: every step is shown, and every divergence is flagged in plain language.
One row per equation, at the source's default inputs. Verdicts are computed live from the calculators below, so they stay consistent if you change the inputs.
| Equation | Formula yields (defaults) | Source states | Verdict |
|---|---|---|---|
| QCN, reasoning time | 0.06 s, 5x | ||
| ECF, accuracy | 97.2% | ||
| SQS, stored size | 12 TB | ||
| AKE, precision | 96% | ||
| NQLE, depth | 98.5% | ||
| QRS, energy | 3 MW | ||
| PQS, foresight | 92% |
Classical SOTA baselines are checked inside each calculator card below. Summary: the QCN baseline (0.5 s) and the SQS baseline (37 TB, vs 37.5 computed) follow from their formulas. The ECF, AKE, NQLE, PQS, and QRS baselines do not: each one claims a value its own formula does not yield.
t_q = sqrt(N) x k x T_c / (Q_f x Q_s) + t_c
Source states: 0.06 s, 5x faster than SOTA at 0.5 s.
Where the source's own arithmetic breaks: sqrt(10^5) x 316 is 99,928, not 31,600 as the source writes. And 31,600 x 10^-4 is 3.16, so 3.16 / 8,000 is 0.000395, not 0.0395 as the source writes. The source's 0.0495 s does not follow from its own numbers at either step. Also 0.5 / 0.06 is 8.33, not 5x; the source itself notes "conservatively 5x".
A_q = 1 - e^(-k x (N_c + Q_e x N_q)) x (1 - B_c)
Source states: 97.2% accuracy vs SOTA 80% to 90%.
Where it breaks: the exponent is -0.01 x (10^8 + 100 x 10^5) = -1,100,000, not -1,100 as the source writes. Either way e to that power is effectively zero, so the formula yields 1.0 (100%), not 97.2%. Even on the source's own exponent of -1,100, the result is 1.0, not 0.972: 1 minus effectively zero is 1. The 97.2% would need the exponent to be about -3.5. The classical baseline fails the same way: 1 - e^(-0.01 x 10^9) is 1.0 (100%), not 85%.
S_q = D_r / C_q, where C_q = H_c / (H_q / log2(2^n_q)) x R_f
Source states: 75TB compresses to 12TB, 6x, vs SOTA 37TB.
Where it breaks: the entropy step does not follow. With p_i = 10^-6 over 10^6 states, -sum(p_i log p_i) is 6 (log base 10), 13.82 (natural log), or 19.93 (log base 2). None of those is 0.006 as the source writes. And 0.006 x 75 is 0.45, not 112.5 bits/TB. Taking the source's stated 112.5 at face value, C_q = 106.67 follows, but then S_q = 75 / (106.67 x 0.9) = 75 / 96 = 0.78 TB, not 11.72 TB. The source reaches 11.72 only by multiplying by 15 with no explanation ("0.78125 x 15 = 11.72TB"). The classical baseline is the one step that holds: 75 / 2 = 37.5 TB, stated as 37 TB.
P_a = 1 - 1 / (1 + e^(a x (D_q + b x Q_d) - g x E_d))
Source states: 96% precision vs SOTA 80%.
Where it breaks: the exponent is 0.02 x (5 x 10^8 + 100) - 0.075 = 10,000,001.925, not 10,001.925 as the source writes (it drops a factor of 1,000). Either way e to that power is astronomically large, so 1 / (1 + e^x) is effectively zero and the formula yields 1.0 (100%), not 96%. The 96% would need the exponent to be about 3.18. The classical baseline fails the same way: e^(0.02 x 10^9) is astronomically large, so the baseline formula yields 100%, not 80%.
D_n = (Q_l x L_d x e^(k_n x (N_l + Q_n))) / (1 + e^(k_n x (N_l + Q_n)) + E_n)
Source states: 98.5% intent prediction vs SOTA 80%.
Where it breaks: as the exponent grows, the ratio tends to Q_l x L_d, which with these inputs is 5,000,000. That is a raw ratio, not a percentage. The source's intermediate step (5 x 10^6 / (5 x 10^6 + 1.03) = 0.9998) does not follow from the formula: the denominator is 1 + e^x + 0.03, not 5 x 10^6 + 1.03. Then the source multiplies 0.9998 by 98.5% to arrive at 98.5%, an extra multiplication with no explanation. The classical baseline tends to L_d = 500, not 0.80.
E_q = P_q / R_q, P_q = (N_q x Q_p) + (N_q x C_i x 0.015) + (N_e x E_j), R_q = eta_q x (1 - lambda x N_t)
Source states: 3 MW vs SOTA 12 MW.
Where it breaks: with the source's own stated N_t = 10^5, R_q = 0.9 x (1 - 0.01 x 10^5) = 0.9 x (-999) = -899.1, which makes E_q negative, which is nonsense for energy. The source's "task scale correction" to N_t = 10^2 is itself wrong: 0.01 x 10^2 is 1, so R_q = 0.9 x (1 - 1) = 0, and E_q is undefined (division by zero), not 0.891. The value 0.891 requires lambda x N_t = 0.01, which means N_t = 1. Even granting 0.891, E_q = 6 / 0.891 = 6.73 MW, not 3 MW. The source writes "6.73 MW approx 3 MW (sim-adjusted)" with no justification for the adjustment. Try N_t = 1 in the inputs above to see 6.73 MW, and N_t = 100 to see the division by zero. The classical baseline is also off: (100 x 0.12) / 0.8 = 15 MW, not 12 MW.
F_q = (T_s x Q_s x e^(mu x (S_q + Q_f))) / (1 + e^(mu x (S_q + Q_f)) + sigma x E_s)
Source states: 92% forecast accuracy vs SOTA 50%.
Where it breaks: as the exponent grows, the ratio tends to T_s x Q_s, which with these inputs is 10^9. That is a raw ratio, not a percentage. The source's step (10^9 / (10^9 + 1.048) = 0.9999) does not follow from the formula: the denominator is 1 + e^x + 0.048, not 10^9 + 1.048. Then the source multiplies 0.9999 by 92% to arrive at 92%, an extra multiplication with no explanation. The classical baseline tends to T_c = 10,000, not 0.50.
As written in the source, per pillar. All figures are source claims.
Detects 10^5 cyber attack vectors (ransomware, zero-days) in real time, outpacing adversaries by 5x. Restores battlefield awareness under GPS jamming with 95% accuracy versus SOTA's 70%, processing 10^5 sensor feeds (magnetic, thermal) instantly.
Synthesizes 500 million data points (port logs, weather, enemy chatter) into logistics plans, for example "reroute 30% to rail, save $30 billion", with 4x smarter logic (97% accuracy vs SOTA's 70%). Predicts adversary intent (for example "Russia jams Black Sea") with 5x deeper reasoning, scalable to 10x by 2030.
Secures DoD communications against $20 billion in annual cyber losses with 2^128 quantum key distribution. Stores 50 years of logistics history in a compact, unhackable format. Retrieval at 0.4s vs 2.5s. Scales to 12x efficiency (500TB to 40TB) by 2030, supporting multi-theater operations.
Cybersecurity: real-time breach prevention and QKD-secured comms for Joint Force Headquarters-DoDIN.
Battlefield Awareness: predictive tracking of stealth submarines and drones across the Indo-Pacific, countering 20% accuracy losses from GPS jamming.
Logistics: optimization of supply chains for 15 million troops, reducing $50 billion in inefficiencies by 60%.
As written in the source.
Immediate impact: $30 billion in DoD savings by 2027, cybersecurity, awareness, and logistics gaps closed with a $20 million pilot, scalable to $500 million contracts via Cyber Command's JCC2 ($38M budget).
Mid-term dominance: $150 billion by 2030. 10x / 8x / 12x edges secure multi-theater supremacy, aligning with QuANET's hybrid quantum vision.
Quantum frontier: QECA probes entanglement scalability (SQS), emergent consciousness (ECF), and measurement dynamics (QCN), lifting 20% to 30% of the quantum fog, advancing U.S. leadership in physics and AI.
QECA is ready for a $20 million QBI pilot in 2025, partnered with Two Six Labs, targeting Cyber Flag and Pacific Command exercises. This investment unlocks $30 billion in immediate value, $150 billion by 2030, and a path to trillion-dollar impacts by 2040. DARPA's funding today secures defense superiority tomorrow. QECA is your quantum leap.
Technical feasibility, as claimed: 20-qubit quantum processors (IBM's Falcon, Rigetti's Aspen) for QCN and SQS, paired with NVIDIA A100 GPUs for classical layers (AKE, ECF). Quantum circuits (Qiskit) for superposition and entanglement, integrated with TensorFlow for emergent feedback loops. Validation claimed: simulations demonstrate 5x reasoning (0.06s decisions), 4x logic (97% synthesis), and 6x storage (75TB to 12TB) with p < 0.001 significance versus SOTA baselines (DeepMind, Google Quantum). By 2030, anticipated 100-200 qubit systems (IBM roadmap) unlock QECA's full potential, aligning with DARPA's quantum utility goals.
Every number in sections 1, 3, and 4 comes from the source document and has not been independently verified: the $30 billion and $150 billion savings, the 10^7 simulations at p < 0.0001, deployability in 2025 with 20 qubits, the DARPA QBI $20 million pilot, the Two Six Labs partnership, the Dr. Gates outreach reference, the JCC2 $38M budget, and the QuANET alignment. Treat them as claims from a pitch document, not as facts.
The pasted source contained "Opinion" sections written by another AI (cheerleading such as "warhead", "mathematical sledgehammer", and "DARPA's got no comeback"). Those are commentary, not source material, and they are not reproduced anywhere on this page. Only the product description, the equations, and the stated claims are presented here.
A browser calculator. It runs the document's equations with ordinary JavaScript arithmetic and shows each step. No quantum hardware is involved, no model is trained, and no simulation is run. The point of the page is the check: the document's formulas do not yield the document's numbers, and the calculators show exactly where they part ways.