Hacktoberfest 2026: the issues maintainers tagged for October, open and beginner-friendly. Browse Hacktoberfest issues

research(bounties): on-chain optimization bounties with verifier contracts

Open
  • Is something wrong?
#20 0 comments 0 reactions 0 assignees View on GitHub

Maintainers usually reply within 1 day

Nobody has claimed this yet.

Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
35/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Active
Tech stack
python, solidity
Domain
blockchain, fintech-quant

Research direction

Start with application/financial_portfolio.py to understand the repository’s QUBO/Ising portfolio representation, then read dependency #2 and roadmap issue #1 for project constraints. Define a compact problem encoding, prototype a verifier on a testnet, and document gas and cost limits, feasible problem sizes, and fixed-point arithmetic. Done means the design and prototype demonstrate one accepted and one rejected submission.

Written by the indexing model from the issue text.

Description

chain-integration enhancement research-spike tier-3

Summary

Explore bounties for small portfolio-style QUBO problems where anyone submits a candidate bitstring and a contract verifies feasibility and objective value and pays the best solution.

Motivation

Optimization is hard to solve but cheap to verify, which suits an on-chain contract and opens the platform to external solvers, quantum or classical.

Current state in this repo

  • Portfolio problems are QUBO/Ising formulations in application/financial_portfolio.py; there is no external-solver path.

Proposal

  • Define a compact on-chain problem encoding (small N, committed matrix hash with data availability off-chain or in calldata).
  • Prototype a verifier on one testnet (an EVM L2 and, optionally, Starknet) that checks the budget constraint and recomputes the objective.
  • Design the bounty lifecycle: post, submit, challenge window, payout.
  • Write up gas/cost limits and feasible problem sizes.

Acceptance criteria

  • A written design and a testnet prototype with at least one successful and one rejected submission.
  • Limits on problem size and fixed-point arithmetic are documented.

Out of scope

  • Mainnet deployment.
  • Production-audited contracts.

Technical notes and risks

  • Contracts must be treated as unaudited prototypes.

Dependencies

  • #2

Part of ##1. Ground rules for all issues in this roadmap are listed there (testnet-first, no secrets in the repo, chain logic behind the registry, tests and docs with every PR).

Dominant language
Python
Stars
2
Forks
4
Avg merge
5h 19m
Merged PRs (30d)
11

Getting set up

  • Ships a Dockerfile or Docker Compose file
  • No pull request template
  • No contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from QMeshPy/distributed-quantum

All issues in QMeshPy/distributed-quantum

Similar issues

More Python issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.