A Game Theoretic Approach for Optimizing Quantum Error Budget Distribution
Asif Akhtab Ronggon, Tasnuva Farheen
TLDR
This paper uses game theory to optimize quantum error budget allocation, reducing physical resource overhead by over 30%.
Key contributions
- Formulates quantum error budget allocation as a potential game.
- Achieves Pareto-optimal error distribution via Nash Equilibrium across logical ops, T-state, and rotation.
- Proposes an Iterated Best Response (IBR) algorithm for convergence.
- Reduces physical resource requirements by 30.22% on average across 433 benchmarks.
Why it matters
This work addresses a critical inefficiency in fault-tolerant quantum computing by moving beyond uniform error budget allocation. By applying game theory, it significantly reduces the physical resources needed for quantum circuits. This advancement is crucial for making future quantum computers more practical and scalable.
Original Abstract
Current fault-tolerant quantum compilers allocate error budgets uniformly during resource estimation, causing suboptimal physical resource overhead. We optimize this allocation using a potential game formulation, where Nash Equilibrium yields a Pareto-optimal distribution across logical operations, T-state distillation, and rotation synthesis. An iterated best response (IBR) algorithm converges to this equilibrium through monotonic descent of the shared cost function. Evaluation across 433 MQT benchmarks demonstrates an average reduction of 30.22\% in physical resource requirements relative to uniform baselines, with peak improvements of 97.81\% for specific circuit instances. This establishes a game-theoretic foundation for strategic error budget optimization in fault-tolerant quantum design automation.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.