Quantum Process Construction with Noise-Aware Optimization
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Solution Overview
Problem
Existing technologies face challenges in constructing optimal quantum processes for quantum processors, particularly in efficiently generating desired output data sets with specified error levels, and in optimizing quantum logic circuits for execution on small and noisy quantum processor chips.
Innovation Solution
The approach involves constructing quantum processes using an objective function over input and output data sets, allowing for training processes to improve the objective function using classical simulators or quantum hardware. This method enables the optimization of quantum logic circuits without requiring complex theoretical characterizations of the quantum processor's execution, and allows for the direct incorporation of noise into the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If quantum logic circuits are constructed without incorporating noise models, then the theoretical characterization complexity is reduced, but the performance on small and noisy quantum processor chips deteriorates
Solution Approach 1:
The patent converts the harmful effect of noise into a beneficial training signal by directly incorporating noise models into the quantum process construction. Instead of avoiding noise complexity, the system uses noise characterizations to train quantum circuits to perform reliably on actual hardware, transforming the previously harmful noise factor into a useful training resource that improves circuit robustness.
Solution Approach 2:
The patent applies preliminary action by constructing and training quantum processes in advance using simulated noise models before deployment on actual quantum hardware. The system pre-characterizes noise properties and incorporates them into the training process, so that when the quantum circuit is executed on real noisy devices, it already has optimized parameters that account for expected noise behavior.
2Productivity
If quantum processes are optimized for specific hardware architectures, then the execution efficiency on target devices is improved, but the adaptability to different quantum processor systems deteriorates
Solution Approach 1:
The patent employs parameter changes by allowing the quantum process parameters (such as gate sequences, pulse durations, and optimization weights) to be adjusted based on the specific noise characteristics and architecture of the target quantum hardware. The system maintains a library of noise models and selects or adapts parameters accordingly, enabling efficient optimization for each hardware platform while retaining the ability to switch between different hardware architectures by changing the underlying noise model and parameters.
Data Source
AI summary
In a general aspect, a quantum process for execution by a quantum processor is generated. In some instances, test data representing a test output of a quantum process are obtained. The test data are obtained based on a value assigned to a variable parameter of the quantum process. An objective function is evaluated based on the test data, and an updated value is assigned to the variable parameter based on the evaluation of the objective function. The quantum process is provided for execution by a quantum processor, and the quantum process provided for execution has the updated value assigned to the variable parameter.