Pauli-String Grouping for Quantum Expectation Value Calculation
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Solution Overview
Problem
Quantum computing simulations face limitations due to high memory requirements and computational complexity, particularly in calculating expectation values for large quantum systems, where the same statevectors are loaded multiple times, leading to inefficiencies in memory operations.
Innovation Solution
The system groups similar Pauli-strings together based on shared bit series or xy masks, allowing for the loading of probability amplitudes once and reducing the number of memory operations by calculating expectation values using pairwise probability amplitudes shared across multiple Pauli-strings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same statevectors are loaded multiple times for calculating expectation values of different Pauli-strings, then the calculation can be performed for each Pauli-string independently, but the memory load operations and computational time increase significantly
Solution Approach 1:
The patent merges Pauli-strings that share the same bit series into groups, where each group can be processed together using a single statevector load. This combining approach allows multiple Pauli-string expectation values to be calculated simultaneously from one loaded statevector, eliminating redundant memory operations and significantly reducing simulation time while maintaining independent calculation capability for each Pauli-string
2Measurement precision
If statevectors are loaded multiple times for each Pauli-string calculation, then each expectation value can be calculated accurately, but the memory operations become inefficient and resource-intensive
Solution Approach 1:
The patent makes the loaded statevector universal by using it to calculate expectation values for multiple Pauli-strings within the same group. A single statevector load serves multiple calculation purposes, allowing the same memory data to be reused across different Pauli-string evaluations, thereby reducing memory operation resources while preserving the accuracy needed for each individual expectation value calculation
Data Source
AI summary
Systems and techniques that facilitate expectation value calculation by grouping Pauli-strings are provided. In various embodiments, a system can comprise an expectation component that calculate expectation values of two Pauli-strings based on a first bit series of a first Pauli-string of the two Pauli-strings and a second bit series of a second Pauli-string of the two Pauli-strings, wherein bit series comprise a first value for a position of an x or y in a Pauli string and a second value for a position of a non-x or y in the Pauli-string.