Quantum Process Learning Using Gradient Estimates for Lindblad Errors
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Solution Overview
Problem
Existing quantum computing techniques struggle to accurately learn Lindblad parameters due to reliance on knowledge of initial quantum states and time-evolved states, and are limited to Clifford gates, failing to correct coherent and dissipative errors effectively.
Innovation Solution
A system and method that computes gradients based on expectation values of observables at discrete time points, estimating gradient values to learn Lindblad parameters, which are used to construct a parametrized Lindblad model applicable to both single-qubit and multi-qubit systems, enabling error mitigation and noise identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing quantum computing techniques are used to learn Lindblad parameters, then the process is limited to Clifford gates and relies on knowledge of initial quantum states, but the accuracy and applicability to general quantum systems deteriorates
Solution Approach 1:
The patent develops a quantum process learning framework that works universally for both Clifford gates and general quantum systems. By formulating the learning problem in terms of observable expectations and process matrices rather than gate-specific representations, the method achieves multi-functionality across different quantum system types, resolving the contradiction between versatility and accuracy.
Solution Approach 2:
The patent transforms the learning approach by changing parameters from gate-based representations to observable-based representations. By parameterizing the quantum process in terms of observable expectations at different times and using process matrices, the method achieves accurate Lindblad parameter learning for general quantum systems without being limited to Clifford gates.
2Reliability
If existing techniques rely on knowledge of initial quantum states and time-evolved states, then the method is simpler to implement, but the robustness to state preparation errors deteriorates
Solution Approach 1:
The patent introduces process matrices as an intermediary representation that connects observable expectations to Lindblad parameters without requiring direct knowledge of initial and time-evolved quantum states. This intermediary framework enables robust parameter learning by measuring only observable expectations, which are less sensitive to state preparation errors, while maintaining a systematic implementation approach.
3Reliability
If existing techniques are used for error correction, then the approach is limited to specific gate types, but the effectiveness in correcting coherent and dissipative errors deteriorates
Solution Approach 1:
The patent develops a universal error correction framework that effectively corrects both coherent and dissipative errors across different gate types. By using process matrices and observable-based learning, the method achieves multi-functionality that encompasses Clifford gates, non-Clifford gates, and general quantum operations, resolving the contradiction between error correction effectiveness and gate type applicability.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to quantum process learning based on gradient value estimates. A system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute the computer executable components stored in the memory, where the computer executable components can comprise a measurement component that generates respective expectation values by measuring a plurality of observables at respective discrete time points for a plurality of initial quantum states in a quantum system. The computer executable components can also comprise a computation component that can compute a gradient based on respective expectation values corresponding to respective discrete time points. The computer executable components can further comprise an estimation component that can estimate a set of gradient values by evaluating the gradient at a set of time points.