Random-Walk Phase Estimation for Low-Memory Quantum Hardware
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing phase estimation methods for quantum computing are sub-optimal, difficult to program, require excessive classical computing resources, and are not robust to noise and decoherence, making them unsuitable for near-term quantum devices.
Innovation Solution
A deterministic random walk approach for phase estimation that uses a classical computer to cooperatively control a quantum computing device, minimizing memory requirements and executing within nanosecond timescales, with an unwinding procedure to correct for inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing phase estimation methods are used, then phase estimation can be performed, but they require excessive classical computing resources and memory
Solution Approach 1:
The patent extracts the essential phase estimation function from complex existing algorithms and implements it using a simplified random walk approach on quantum hardware, separating the core estimation logic from resource-intensive classical processing
Solution Approach 2:
The patent changes the algorithmic parameters by using a random walk-based estimation method with limited memory states instead of full quantum state simulation, reducing classical memory requirements while maintaining estimation accuracy
2Measurement precision
If existing phase estimation methods are used, then phase estimation can be performed, but they are not robust to noise and decoherence
Solution Approach 1:
The patent implements feedback through iterative random walk steps where each measurement outcome influences subsequent walk directions, allowing the system to adapt and maintain accuracy despite noise and decoherence effects
Solution Approach 2:
The patent uses dynamic random walk paths that adapt based on measurement outcomes, making the estimation process flexible and resilient to environmental noise rather than following fixed deterministic sequences
3Measurement precision
If existing phase estimation methods are used, then phase estimation can be performed, but they are difficult to program and execute
Solution Approach 1:
The patent employs self-service through automated random walk generation and update mechanisms that require minimal manual programming intervention, with the system automatically adapting its estimation process based on measurement feedback
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The disclosed technology concerns example embodiments for estimating eigenvalues of quantum operations using a quantum computer. Such estimations are useful in performing Shor's algorithm for factoring, quantum simulation, quantum machine learning, and other various quantum computing applications. Existing approaches to phase estimation are sub-optimal, difficult to program, require prohibitive classical computing, and/or require too much classical or quantum memory to be run on existing devices. Embodiments of the disclosed approach address one or more (e.g., all) of these drawbacks. Certain examples work by using a random walk for the estimate of the eigenvalue that (e.g., only) keeps track of the current estimate and the measurement record that it observed to reach that point.