Adaptive Quantum Signal Processor for Efficient Interface
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Solution Overview
Problem
Existing technologies face challenges in effectively interfacing quantum systems with classical technologies, limiting their ability to process and analyze quantum signals efficiently.
Innovation Solution
The adaptive quantum signal processing (AQSP) system integrates a physics system, a machine learning engine, and an output generator to process quantum signals. It combines incoming signals with control functions to produce recipe functions, which are applied to initial wavefunction states to cause transitions and characterize wavefunctions, with the machine learning engine updating control functions based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantum systems are interfaced with classical technologies, then quantum signals can be processed and analyzed, but the efficiency and effectiveness of processing are limited
Solution Approach 1:
The patent introduces a hybrid quantum-classical processing system where quantum processors handle specific quantum signal processing tasks while classical processors manage other operations. A controller coordinates between the two processors, acting as an intermediary that enables efficient quantum signal processing without requiring complete quantum-classical integration, thus improving productivity while managing complexity.
2Measurement precision
If adaptive control is implemented in quantum signal processing, then sensitivity and performance are improved, but system complexity increases
Solution Approach 1:
The patent implements adaptive control where the controller receives output from the quantum processor and uses this feedback to adjust control signals for subsequent processing steps. This feedback mechanism enables the system to adapt to quantum signal characteristics dynamically, improving measurement precision and sensitivity while the controller manages the complexity of coordinating quantum and classical operations.
Data Source
AI summary
An adaptive quantum signal processor (AQSP) includes a signal combiner, a physics station, a measurement system, a machine-learning engine and an output generator. The signal combiner combines incoming signals with control functions to yield recipe functions. For example, the recipe functions can be “shaking” functions used to change the wavefunctions of atoms entrained in an optical lattice. The recipe functions are applied to wavefunctions in initial wavefunction states causing the wavefunctions to transition to signal-impacted states. The measurement system measures the wavefunctions in their signal-impacted quantum states to yield wavefunction characterizations. The machine-learning engine updates control functions based on the wavefunction characterizations. The output generator outputs results based on the wavefunction characterizations and/or control function characterizations. In a matched-filter application, the outputs characterize (e.g., identify, classify, rate) the incoming signals.