Quantum Circuit Normalization for Cached Result Reuse
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Solution Overview
Problem
Current quantum computing technologies face high costs and resource inefficiencies due to the expensive nature of executing quantum programs on existing hardware, necessitating further optimization techniques beyond conventional quantum circuit optimization.
Innovation Solution
A system that converts input quantum circuits into normalized forms, allows for cached result usage by detecting matches with pre-executed circuits, and provides these cached results to reduce computational load and costs, while also offering user authorization and explanation features for enhanced control and insight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum programs are executed on current quantum hardware, then computational results are obtained, but power consumption and operational costs increase significantly
Solution Approach 1:
The system performs preliminary actions by converting quantum circuits to normalized forms before execution and caching the results. When a matching circuit is found in the cache, the pre-computed result is reused instead of executing the circuit again, thereby avoiding repeated power consumption while maintaining result accuracy.
Solution Approach 2:
The system creates copies of quantum circuit execution results and stores them in a cache. Instead of re-executing identical or equivalent quantum circuits, the system retrieves and uses the cached copy of the result, significantly reducing power consumption while maintaining computational reliability.
2Productivity
If quantum circuits are optimized through conventional techniques, then gate count is reduced, but further cost reduction is limited
Solution Approach 1:
The system performs preliminary normalization of quantum circuits before execution and caches the results. This preliminary action enables future circuits to be compared and matched against the cache, allowing result reuse that goes beyond conventional gate optimization and achieves further operational cost reduction.
Solution Approach 2:
The system changes the parameter representation of quantum circuits by normalizing them to a standard form. This parameter transformation enables effective comparison and matching in the cache, allowing the system to identify equivalent circuits and reuse results, thereby reducing operational costs beyond what conventional optimization achieves.
3Loss of energy
If cached results are reused, then power consumption and costs are reduced, but system complexity increases
Solution Approach 1:
The system segments the quantum computing workflow into distinct components: circuit normalization, cache lookup, result provision, and optional authorization. This segmentation allows each component to be optimized independently and enables modular implementation that manages system complexity while achieving power consumption reduction through result caching.
Solution Approach 2:
The system introduces an intermediary normalization layer between circuit submission and execution. This intermediary component converts circuits to a standard form before caching and lookup, enabling efficient result reuse while keeping the overall system architecture manageable through clear separation of concerns.
4Loss of energy
If normalization and cache lookup are performed, then result reuse increases, but processing time before execution increases
Solution Approach 1:
The system performs normalization as a preliminary action that enables future quick lookups. While normalization takes time upfront, it transforms the circuit into a standardized form that can be rapidly compared against cached results, reducing the time cost for subsequent equivalent circuits through efficient cache hits.
Solution Approach 2:
The system changes circuit parameters to a normalized standard form, which enables efficient comparison and matching operations in the cache. Although normalization requires initial processing time, it significantly reduces the time required for circuit analysis and execution by allowing direct cache lookup of equivalent circuits.
Data Source
AI summary
Techniques facilitating cached result use through quantum gate rewrite are provided. In one example, a computer-implemented method comprises converting, by a device operatively coupled to a processor, an input quantum circuit to a normalized form, resulting in a normalized quantum circuit; detecting, by the device, a match between the normalized quantum circuit and a cached quantum circuit among a set of cached quantum circuits; and providing, by the device, a cached run result of the cached quantum circuit based on the detecting.