Hybrid Quantum Amplitude Estimation via Classical Stochastic Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current quantum computing methods face challenges in efficiently estimating a quantum state's phase due to the complexity of quantum circuit design and the need for high circuit depth, which is technologically unfeasible with near-term quantum computing.
Innovation Solution
A hybrid quantum-classical approach that utilizes stochastic inference and Bayesian learning to determine an expectation value based on an uncollapsed eigenvalue pair, encoding it as a phase, allowing for probabilistic measurement independent of the input state, and incorporating a classical statistical model to reduce circuit depth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional quantum phase estimation is used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces a classical statistical model as an intermediary between the quantum system and the phase estimation process. This classical model processes measurement outcomes and performs stochastic inference to determine eigenvalues, replacing the need for complex deep quantum circuits while maintaining estimation accuracy. The classical intermediary handles the computational burden that would otherwise require high circuit depth.
Solution Approach 2:
The patent segments the phase estimation task into two parts: a shallow quantum circuit that prepares states and performs initial measurements, and a classical statistical model that completes the eigenvalue determination through stochastic inference. This segmentation allows each component to be optimized independently, with the quantum portion requiring minimal depth and the classical portion handling the complex inference.
2Measurement precision
If high circuit depth is used, then measurement precision is improved, but productivity decreases
Solution Approach 1:
By introducing a classical statistical model as an intermediary, the patent enables rapid eigenvalue determination without requiring repeated deep quantum circuit executions. The classical model processes measurement data efficiently through stochastic inference, significantly reducing the total computational time and improving productivity while maintaining precision.
Solution Approach 2:
The patent uses a shallow quantum circuit that performs only the necessary state preparation and initial measurements, avoiding the excessive circuit depth of traditional QPE. The remaining computational task is transferred to the classical statistical model, which completes the eigenvalue determination with fewer quantum resources while achieving the same precision.
3Loss of information
If quantum measurement is performed, then information is obtained, but eigenstate collapse occurs
Solution Approach 1:
The patent implements a feedback mechanism where the classical statistical model uses measurement outcomes to update its stochastic inference process. By iteratively refining eigenvalue estimates based on measurement data without requiring repeated quantum measurements, the system extracts maximum information from limited measurements while minimizing state collapse and preserving quantum state stability.
Solution Approach 2:
The classical statistical model acts as an intermediary that processes measurement information without requiring additional quantum measurements. This intermediary extracts eigenvalue information through stochastic inference, reducing the need for repeated quantum measurements that would cause eigenstate collapse, thereby preserving quantum state stability while obtaining necessary information.
Data Source
AI summary
Techniques and a system to facilitate estimation of a quantum phase, and more specifically, to facilitate estimation of an expectation value of a quantum state, by utilizing a hybrid of quantum and classical methods are provided. In one example, a system is provided. The system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include an encoding component and a learning component. The encoding component can encode an expectation value associated with a quantum state. The learning component can utilize stochastic inference to determine the expectation value based on an uncollapsed eigenvalue pair.