Decentralized QUBO Solver for Energy-Efficient Routing
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Solution Overview
Problem
Quantum computing's processing efficiency for complex problems is hindered by the need for costly and energy-intensive Bitcoin mining, which is detrimental to the environment, and existing methods struggle to express real-world problems in a resolvable manner for quantum computers.
Innovation Solution
A method involving a classical computer to determine non-convex sub-problems, generate smart contracts, and transmit them to a distributed ledger for solving by multiple solvers, with the most suitable solution selected based on energy and time, optimizing vehicle routes and generating cryptocurrency tokens using quadratic unconstrained binary optimization (QUBO) and alternating direction method of multipliers (ADMM) techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum computing is used to solve complex optimization problems, then processing efficiency is improved, but energy consumption increases due to Bitcoin mining requirements
Solution Approach 1:
The patent segments the solution process into two distinct phases: a classical computer phase for problem formulation and smart contract generation, and a distributed quantum computing phase for solution execution. This segmentation allows each component to operate in its optimal environment, reducing overall energy consumption while maintaining quantum processing efficiency for the actual optimization problem solving.
Solution Approach 2:
The patent introduces a distributed ledger blockchain as an intermediary layer between the classical computer and quantum computers. This intermediary automatically manages task distribution, solution verification, and incentive coordination through smart contracts, eliminating the need for energy-intensive Bitcoin mining while maintaining security and decentralization.
2Reliability
If Bitcoin mining is used to secure the distributed ledger, then network security is improved, but environmental impact worsens due to high energy consumption
Solution Approach 1:
The patent changes the fundamental parameter of consensus mechanism from proof-of-work (energy-intensive Bitcoin mining) to proof-of-solution (quantum problem solving). Instead of competing to solve arbitrary hashing problems, nodes compete to solve actual optimization problems using quantum computing, maintaining security through computational difficulty while eliminating environmental harm.
Solution Approach 2:
The patent converts the previously harmful energy consumption of Bitcoin mining into beneficial quantum computational resources. The distributed ledger nodes that would have been wasting energy on mining now contribute quantum computing power to solve real-world optimization problems, transforming environmental harm into scientific and practical benefit.
3Measurement precision
If real-world problems are expressed in a resolvable manner for quantum computers, then solution accuracy is improved, but problem formulation complexity increases
Solution Approach 1:
The patent extracts the complex problem formulation task from the quantum computing process and assigns it to classical computers. Classical computers handle the formulation of optimization problems in standard formats, while quantum computers focus solely on executing the solving process. This extraction reduces the complexity burden on quantum systems while maintaining solution accuracy.
Solution Approach 2:
The patent creates a universal interface layer through smart contracts that can handle multiple types of optimization problems (routing, scheduling, resource allocation) through a standardized quantum problem formulation. This universal approach allows diverse real-world problems to be expressed in a consistent resolvable manner without increasing individual problem complexity.
Data Source
AI summary
A routing optimization computer implemented method is provided, comprising: determining a non-convex sub-problem and a convex sub-problem of a constrained optimization problem; generating a smart contract corresponding to the non-convex sub-problem; transmitting the smart contract to a distributed ledger; broadcasting the smart contract to a plurality of solvers; receiving a first binary solution to the non-convex sub-problem from a first solver; receiving a further binary solution to the non-convex sub-problem from a second solver; determining a more suitable solution of the first binary solution and the further binary solution; transmitting a payment associated with the smart contract; transmitting the more suitable binary solution; determining a time solution to the convex sub-problem using the more suitable binary solution; repeating steps using the time solution as the continuous variable, until a threshold is met; and mapping the binary solution and the time solution to the constrained optimization problem.