This project uses Qiskit, IBM's quantum computing SDK, to create a conceptual model of a digital market. It's not a literal simulation, but a powerful analogy that uses the principles of quantum mechanics to represent the complex, probabilistic, and interconnected nature of a competitive landscape.
The core idea is to frame a market analysis tool not as a simple data query, but as an act of measurement that collapses a "quantum-like" state of infinite market possibilities into a single, observable reality.
This script models a market with three main competitors, using a 3-qubit quantum circuit. The process mirrors the transition from market uncertainty to a concrete market snapshot.
- Quantum Concept: A qubit can exist as both 0 and 1 simultaneously. The Hadamard (H) gate puts a qubit into an equal superposition of both states.
- Market Analogy: Before we run our analysis, the market exists in a state of pure potential. All outcomes are possible. Is Competitor A strong or weak? Is Competitor C launching a new product? In this model, the market is in a superposition of all 8 possible states at once, representing total uncertainty.
- Quantum Concept: Entanglement is a phenomenon where the state of one qubit becomes intrinsically linked to the state of another, no matter the distance between them.
- Market Analogy: Competitors are never independent. An aggressive ad campaign from one company directly affects the market share of another. We use Controlled-NOT (CNOT/CX) gates to entangle our competitor qubits, simulating these market influences and shifting the probabilities of certain outcomes.
- Quantum Concept: The act of measuring a qubit collapses its superposition into a definite classical state (either 0 or 1).
- Market Analogy: Running our market intelligence application is an act of measurement. We take a "snapshot" of the market at a single point in time. This act forces the cloud of possibilities to collapse into one single, observable reality. The script simulates this measurement 1024 times to reveal the underlying probability distribution of the different market states.
X_Prize Quantum Initial Proposal Submitted to X Prize by Justin Houck on March 31st, 2025
Early, non-invasive Bovine Respiratory Disease (BRD) detection hinges on identifying biomarkers like specific volatile organic compounds (VOCs), but designing sensors requires accurately knowing their spectral signatures. Classical computational chemistry faces a critical bottleneck here: standard DFT often yields inaccurate excited state energies crucial for spectra, while high-fidelity methods scale exponentially, hindering reliable in silico screening of potential BRD biomarker candidates. Our project will demonstrate definitive quantum advantage by calculating key spectral properties specifically, low-lying electronic excitation energies and vibronic coupling constants for a targeted, high-potential BRD VOC biomarker. We employ a hybrid quantum-classical framework utilizing advanced variance-reduced Variational Quantum Eigensolver (VQE) techniques and experimentally validated, multi-stage, hardware-aware error mitigation protocols optimized for excited state calculations on near-term quantum hardware. Compact, chemically-optimal ansätze will be generated adaptively. Our primary goal is achieving spectroscopic accuracy (<0.1 eV error) for the target electronic transitions, surpassing documented classical limitations. This quantum result will be validated via an exhaustive, pre-registered, open-data benchmarking protocol against state-of-the-art classical methods (multiple DFT functionals, EOM-CCSD(T), DMRG/CASPT2). Quantum advantage will be rigorously proven by demonstrating superior accuracy within defined computational resource budgets. The core XPRIZE deliverable is the validated, high-accuracy spectral calculation for the target BRD biomarker, accompanied by the complete benchmark dataset and analysis proving quantum advantage. This provides the first reliable prediction of these spectral features, directly enabling sensor design for early BRD detection. It's a landmark demonstration of quantum computation solving an intractable spectroscopy problem with high-value application in animal health and food security, fulfilling the XPRIZE goal with verified capability. Quantum Simulation (Chemistry-focused) Description: Uses quantum computers to calculate the properties of molecules and chemical reactions by simulating their underlying quantum mechanical electronic structure. Algorithms like VQE (Variational Quantum Eigensolver) and its advanced variants (e.g., ADAPT-VQE) are employed to find molecular energy levels. Proposed Use Cases: Calculating reaction barriers for catalysts (Nitrogen Fixation - SDG 2, 9, 12, 13), predicting molecular spectra for biomarkers (BRD Detection - SDG 2, 3, 9, 12), calculating drug-target binding energies (BRD Therapeutics - SDG 2, 3). Quantum Simulation (Physics/Dynamics-focused): ○ Description: Uses quantum algorithms to model the time evolution or properties of physical systems (other than molecular electronic structure), such as fluid dynamics or field theories. Methods might include quantum walk models, QLBM-inspired approaches, or potentially algorithms for simple field theories. ○ Proposed Use Cases: Simulating complex barn atmospheric conditions (airflow, particle transport) for foundational BRD understanding (SDG 2, 3, 12, 15). Quantum Machine Learning (QML): ○ Description: Leverages quantum computing principles potentially to enhance machine learning tasks. This includes algorithms like Quantum Kernel Methods, Quantum Neural Networks, or variational quantum classifiers designed to find complex patterns or correlations in data, potentially more efficiently or effectively than classical ML for certain problems. ○ Proposed Use Cases: Analyzing high-dimensional sensor/biometric data to find predictive signatures of BRD risk (SDG 2, 3, 12). Quantum Optimization: ○ Description: Uses quantum algorithms (like QAOA - Quantum Approximate Optimization Algorithm, or potentially Quantum Annealing) to find approximate or exact solutions to complex optimization problems, often combinatorial in nature (e.g., finding the best configuration out of many possibilities). ○ Proposed Use Cases: Optimizing operational schedules (e.g., ventilation systems) or resource allocation strategies in farms for better BRD management and efficiency (SDG 2, 3, 9, 12).
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TEAM LEAD: Global Hawk Solutions TRACK: XPRIZE Quantum Applications IMPACT FOCUS: SDG 2 (Zero Hunger), SDG 3 (Good Health), SDG 12 (Responsible Consumption)
The Global Challenge Bovine Respiratory Disease (BRD) is the primary driver of morbidity in the global cattle industry, costing the U.S. economy >$750 million annually. Effective mitigation requires early detection via Volatile Organic Compounds (VOCs) in breath. However, current sensor technology fails in the chemically noisy environment of a feedlot. Material scientists are currently unable to engineer sensors that reliably distinguish trace biomarkers (specific ketones) from high-concentration background interference like methane and ammonia. This lack of selectivity results in high false-positive rates, delayed intervention, and antibiotic overuse.
The Computational Bottleneck
The solution lies in Metal-Organic Frameworks (MOFs)—porous materials engineered to chemically "trap" specific biomarkers. The selectivity of a MOF is governed by the quantum mechanical interaction between the target gas and the Transition Metal nodes (e.g., Cobalt, Copper) within the framework.
The Problem: Classical computers cannot accurately simulate this interaction. Transition metals possess open
The Quantum Solution Global Hawk Solutions proposes a hybrid quantum-classical workflow to simulate the ground state energy and binding affinity of BRD biomarkers on Cobalt-based MOF nodes with chemical accuracy. By solving the many-body electron problem that scales exponentially on classical supercomputers, we will unlock the design of ultra-selective, interference-immune sensors for livestock health.
2.1. The Scientific Objective
We will compute the Binding Energy (
2.2. Justification for Quantum Advantage
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The System: A Cobalt(II) active site (
$d^7$ configuration) interacting with an acetone ligand. -
Why Classical Fails: This system exhibits multi-reference character due to the near-degeneracy of the Cobalt
$d$ -orbitals. Classical Single-Reference methods (like DFT) suffer from massive self-interaction error, often predicting incorrect magnetic spin states. Correcting this classically requires Full Configuration Interaction (FCI), which scales factorially ($N!$ ) and is computationally intractable for systems of this size. - The Quantum Target: We target a binding energy precision of <0.1 eV (Chemical Accuracy). This is the specific engineering threshold required to distinguish the target VOC from background isomers.
2.3. Methodology We utilize a variance-reduced Variational Quantum Eigensolver (VQE) framework optimized for Noisy Intermediate-Scale Quantum (NISQ) hardware:
- Algorithm: ADAPT-VQE (Adaptive Derivative-Assembled Pseudo-Trotter VQE). Unlike fixed ansatzes, ADAPT-VQE grows the wavefunction dynamically, adding operators only where strong correlation is detected. This minimizes circuit depth—a critical requirement for maintaining coherence on near-term devices.
- Ansatz: A Unitary Coupled Cluster (UCCSD) pool tailored to the active space of the metal-ligand bond.
- Error Mitigation: Implementation of Zero-Noise Extrapolation (ZNE) to recover noise-free expectation values by extrapolating measurements across varying noise scales.
2.4. Validation Protocol
- Benchmark: Results will be validated against high-cost classical DMRG (Density Matrix Renormalization Group) calculations on the active site cluster, which serves as the "ground truth" for strongly correlated systems.
- Success Metric: Demonstrating that the quantum workflow predicts binding energy within 0.1 eV of the benchmark using significantly fewer wavefront parameters than classical CI expansions.
While the Core Deliverable designs the material, the Supporting Module interprets the sensor output.
- The Use Case: High-fidelity MOF sensors produce noisy, non-linear electrical response curves (impedance/capacitance) subject to humidity and temperature drift.
- The Approach: We employ Quantum Kernel Methods (QSVM) to classify this noisy sensor data.
- The Advantage: Quantum kernels map low-dimensional sensor data into a functionally infinite-dimensional Hilbert space. This enables the detection of complex, non-linear correlations between "impedance," "temperature," and "biomarker presence" that classical RBF kernels miss, resulting in higher sensitivity and specificity for early-stage disease detection.
4.1. Global Hawk Solutions (Lead Entity)
- Role: Problem Owner & Commercial Integrator.
- Responsibilities: Definition of biological targets, sensor hardware constraints, commercialization pathway, and field validation in Texas feedlots.
4.2. Technical Consortium Strategy
- Role: Quantum Algorithm Implementation.
- Strategy: To execute the ADAPT-VQE and Error Mitigation protocols, Global Hawk Solutions will partner with specialized quantum software entities.
- Synergy: This consortium model bridges the gap between "Abstract Quantum Physics" and "Applied Agricultural Engineering," ensuring the XPRIZE solution results in a deployable physical product rather than just theoretical code.
This project avoids "toy problems." We are not simulating simple gases; we are targeting the Transition Metal
Ion Trap (like IonQ or Quantinuum) or Superconducting (IBM Eagle/Heron)