BidScout.io is a bid scouting platform that helps small businesses and contract

managers find and win corporate and government contracts more efficiently. The

current bid discovery and analysis process relies on classical computing techniques,

which are limited in processing speed, predictive accuracy, and optimization

capabilities when handling vast datasets of procurement opportunities.

With the rise of quantum computing, we see a major opportunity to revolutionize bid

discovery and prediction by:

● Improving bid relevance matching across 30+ procurement portals.

● Enhancing win probability analysis based on historical bidding data.

● Optimizing bid pricing strategies by analyzing market trends and competitor

behaviors.

● Strengthening compliance verification by cross-referencing regulations and

eligibility criteria at scale.

BidScout.io is seeking a collaboration with quantum computing students and

researchers to explore how quantum algorithms can be applied to procurement data

analysis, machine learning models, and optimization techniques to create a smarter

and more efficient bid scouting process.

The goal of this project is to explore, prototype, and validate how quantum computing

can enhance bid discovery, analysis, and decision-making in procurement.

Key Objectives:

1. Quantum-Powered Bid Matching – Develop a quantum-enhanced algorithm that

improves the accuracy of bid-to-business matching based on industry

classification codes (NAICS, NIGP, SIC, UNSPSC).

2. Quantum Machine Learning for Win Probability – Train quantum-enhanced ML

models to predict contract win probability using historical bid data and

competitor trends.

3. Bid Pricing Optimization – Use quantum computing for real-time contract

pricing analysis, helping businesses determine optimal bid pricing strategies.

4. Quantum-Based Risk & Compliance Assessment – Apply quantum algorithms to

automate compliance checks against bid requirements (size, revenue,

certifications).

5. Scalability & Performance Testing – Evaluate the computational efficiency of

quantum methods compared to classical computing in large-scale procurement

datasets.

Scope of Work:

This project will focus on applying quantum computing techniques to improve

procurement data analysis in the following key areas:

✅ Bid Discovery – Enhancing keyword-based bid matching through quantum search.

✅ Win Probability Estimation – Using quantum ML to refine success predictions.

✅ Bid Pricing Strategy – Identifying optimal pricing using quantum optimization.

✅ Regulatory Compliance Checks – Faster eligibility verification via quantum

algorithms.

Deliverables:

🔹 Phase 1 – Research & Feasibility Study (4-6 weeks)

● Literature review on quantum computing in procurement & bidding.

● Identification of quantum algorithms relevant to bid discovery & prediction.

🔹 Phase 2 – Data Processing & Model Design (6-8 weeks)

● Apply quantum techniques to process historical bid data for pattern recognition.

● Prototype a quantum-enhanced bid matching model.

🔹 Phase 3 – Prototype Development & Testing (8-12 weeks)

● Develop a quantum-powered bid discovery algorithm for real-world bid

matching.

● Compare quantum vs. classical computing efficiency on large procurement

datasets.

🔹 Phase 4 – Final Report & Recommendations (4 weeks)

● Compile findings into a technical report & presentation.

● Provide recommendations on scalability & integration into BidScout.io’s

platform.