Hybrid Quantum-Classical Monte Carlo Minimization for Error Reduction
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Solution Overview
Problem
Designing quantum circuits is challenging and time-consuming, and existing quantum devices are not fault-tolerant, leading to limitations in quantum computing processing efficiency and accuracy.
Innovation Solution
A system comprising a quantum processor and a classical processor that performs Monte Carlo minimization to reduce errors and noise by computing Nth order moment expectation values, using variational optimization to iteratively determine trial state parameterization and generate quantum measurement data, facilitating reduced error and noise in quantum computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If quantum circuits are designed to leverage exponential state space dimension for computation acceleration, then computational power is improved, but circuit design complexity and time increase significantly
Solution Approach 1:
The patent divides the quantum circuit design into modular components: a quantum processor unit that executes variational quantum circuits and a classical processor unit that performs optimization calculations. This segmentation allows independent development and testing of each module, reducing overall design complexity while maintaining computational power through their coordinated operation in a hybrid architecture.
2Power
If quantum circuits are designed to leverage exponential state space dimension for computation acceleration, then computational power is improved, but design time increases significantly
Solution Approach 1:
The patent employs preliminary action by pre-defining parameterized quantum circuit templates with configurable gates and operations. These pre-designed templates can be rapidly instantiated and adapted for different computational tasks, significantly reducing design time while maintaining the ability to leverage exponential state space dimension for computational power.
3Productivity
If variational quantum circuits are executed on non-fault-tolerant quantum devices, then quantum computation can be performed, but errors and noise increase
Solution Approach 1:
The patent implements a feedback loop where measurement results from the quantum processor are fed back to the classical processor, which then adjusts circuit parameters to minimize the cost function. This iterative feedback mechanism allows the system to compensate for errors and noise by continuously optimizing parameters, enabling productive computation on non-fault-tolerant devices while improving reliability through adaptive error mitigation.
4Measurement precision
If Monte Carlo minimization is used to reduce errors and noise in quantum computation, then computation accuracy is improved, but computational resources and time increase
Solution Approach 1:
The patent applies partial action by performing Monte Carlo minimization only on the classical optimization portion of the hybrid system, rather than attempting to reduce all errors through quantum computation alone. This selective application of error reduction techniques achieves improved computation accuracy while conserving computational resources by leveraging the strengths of both quantum and classical processing.
Data Source
AI summary
Techniques and a system to facilitate quantum computation of Monte Carlo minimization are provided. In one example, a system includes a quantum processor and a classical processor. The quantum processor can perform an Nth order moment expectation computation process to compute an expected value of a quantum state associated with a quantum circuit description. The classical processor can execute computer executable components stored in a memory, where the computer executable components comprise a variational optimization component. The variational optimization component can perform an optimization process associated with a Monte Carlo minimization process to iteratively determine a variational parameterization for an Nth expectation value and associated trial state based on samples of the Nth order moment expectation computation process.