Quantum Gate Decomposition With Budgeted Error Mitigation
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Solution Overview
Problem
Near-term quantum computers are hindered by noise and errors, which prevent them from outperforming classical computers in computation tasks, and full fault tolerance through quantum error correction requires substantial resources not available in near-term systems.
Innovation Solution
A system and method for quantum error mitigation using an approximate decomposition of quantum gates with a budgeted C-factor, optimizing quantum channels and distributing errors across multiple gates to reduce total error, employing Stinespring dilation and variational unitary approximation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum error correction is implemented to reduce noise and errors, then computation accuracy is improved, but resource requirements (qubits, gates, circuit depth) increase substantially
Solution Approach 1:
The patent segments the quantum circuit into multiple layers or levels, applying different error mitigation strategies to different segments. This allows selective application of resource-intensive error correction only where most needed, rather than uniformly across the entire circuit, thus reducing overall resource requirements while maintaining computation accuracy.
Solution Approach 2:
The patent applies local error mitigation techniques tailored to specific quantum gates or circuit regions with higher error rates. By identifying and targeting problematic areas with enhanced error correction while using lighter mitigation elsewhere, the system achieves improved computation accuracy without uniformly increasing resource consumption across the entire system.
2Reliability
If approximate decomposition of quantum gates is used to reduce C-factor, then variance in quasi-probability sampling is reduced, but approximation error increases
Solution Approach 1:
The patent applies approximate decomposition selectively to only those quantum gates where the C-factor is sufficiently high to justify the approximation. By identifying gates with large quasi-probability coefficients and applying approximation only there, the system reduces overall sampling variance while introducing minimal approximation error in critical gate operations.
Solution Approach 2:
The patent dynamically adjusts the approximation parameter ε (error tolerance) based on the specific gate being decomposed and its importance in the overall circuit. For gates with lower impact on final results, larger approximation errors are tolerated, while critical gates use smaller ε values, thus optimizing the trade-off between sampling accuracy and decomposition precision.
Data Source
AI summary
Techniques facilitating error mitigation for quantum computing devices. In one example, a system can comprise a process that executes computer executable components stored in memory. The computer executable components comprise: an approximation component; a budget component; and an optimization component. The approximation component can generate an approximate decomposition of a quantum gate. The budget component can set a budget value (Cbudget) for a C-factor that is a metric for increase in variance of quasi-probability sampling. The optimization component can determine an optimal decomposition for the quantum gate as a function of Cbudget.