Quantum Logic Circuit Characterization Using Quasienergy Eigenvalues
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Solution Overview
Problem
Existing methods for characterizing quantum logic circuits (QLCs) do not effectively account for the spatial and temporal context of the circuits, leading to inefficiencies and inaccuracies in calibrating quantum operations.
Innovation Solution
A method is introduced that characterizes QLCs by selecting control vectors associated with phase shifts for intrinsic parameters, determining estimated eigenvalues from qubit measurements, calculating quasienergy level differences, and using these to define a characteristic polynomial and cost function, ultimately determining optimal intrinsic parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing characterization methods are used, then the process is simpler, but the precision and accuracy of QLC characterization deteriorates
Solution Approach 1:
The characterization process is segmented into distinct phases: selecting control vectors, determining estimated eigenvalues from qubit measurements, calculating quasienergy level differences, defining characteristic polynomials, and optimizing cost functions. Each phase processes specific parameters independently, improving measurement precision while managing complexity through structured decomposition.
Solution Approach 2:
The method transforms the characterization problem by changing parameters from direct circuit measurements to spectral properties (eigenvalues and quasienergy levels). By selecting control vectors that induce specific phase shifts on intrinsic parameters, the system extracts circuit characteristics through spectral analysis, achieving higher precision in characterizing quantum logic circuits.
2Reliability
If context-dependent characterization is implemented, then the accuracy scales better with circuit depth, but the computational overhead increases
Solution Approach 1:
The method employs iterative feedback through cost function optimization. Control vectors are selected based on previous characterization results, and the process refines estimates of intrinsic parameters by minimizing the cost function derived from characteristic polynomials. This feedback loop improves reliability with circuit depth by continuously adapting to observed spectral properties.
Solution Approach 2:
The system performs preliminary selection of control vectors that are specifically designed to probe different aspects of the quantum circuit's intrinsic parameters. By pre-planning the sequence of control vector applications and associated measurements, the method reduces computational overhead while maintaining context-dependent accuracy scaling.
3Measurement precision
If spectral properties are used for characterization, then the method coherently amplifies errors, but the sensitivity to measurement noise increases
Solution Approach 1:
The method merges multiple measurements across different control vectors to estimate a single set of intrinsic parameters. By combining spectral information from multiple controlled experiments and processing them through characteristic polynomial analysis, the system coherently amplifies true signal while averaging out random measurement noise, improving precision without proportionally increasing noise sensitivity.
Data Source
AI summary
The disclosure is directed to characterizing a quantum logic circuit (QLC), via a set of intrinsic parameters. One method includes selecting control vectors that are associated with phase shifts for the intrinsic parameters such that experimental unitary operators for the QLC are defined. Each experimental unitary operator is based on the intrinsic parameters and phase shifts associated with a corresponding control vector. For each control vector, eigenvalues for the corresponding unitary operator are estimated based on qubit measurements performed subsequent to tuning the QLC in accordance with the control vector. The eigenvalues correspond to quasienergy levels of the qubits. Values for the set of intrinsic parameters may be determined based on the eigenvalues.