Quantum Annealer Hamiltonian Tuning with Oracle Feedback
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Solution Overview
Problem
Conventional quantum annealing methods require direct mapping of optimization problem variables into qubits, often necessitating approximation of cost functions and may be impractical or inaccurate for certain phenomena, and secrecy requirements limit the viability of optimization methods.
Innovation Solution
Update parameters of a Hamiltonian based on oracle function outputs, using a trained system to optimize configuration parameters even when the underlying models or rules are unknown, and perform annealing operations to generate candidate solutions, updating parameters like coupling and field values based on oracle expectations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If direct mapping of optimization problem variables into qubits is used, then the quantum annealing can be implemented, but the cost function approximation becomes inaccurate or impractical for certain phenomena
Solution Approach 1:
The patent segments the optimization problem into two parts: (1) a Hamiltonian system that can be directly mapped to qubits, and (2) an oracle function that handles the complex cost function evaluation. This segmentation allows the quantum annealer to work with a simplified Hamiltonian while the oracle provides accurate cost function values, resolving the contradiction between mapping feasibility and modeling accuracy.
Solution Approach 2:
The patent introduces an oracle function as an intermediary between the quantum annealer and the optimization problem. The oracle receives candidate solutions from the quantum annealer, evaluates them using the accurate but complex cost function, and returns the evaluation results. This intermediary enables the use of accurate cost functions without requiring direct mapping of all problem variables to qubits.
2Productivity
If variables of underlying models are converted into qubits and qubit interactions, then optimization can be performed, but secrecy requirements are compromised
Solution Approach 1:
The oracle function serves as a secrecy-preserving intermediary. The underlying model variables never need to be converted to qubits; instead, the oracle receives processed candidate solutions, evaluates them using the protected model, and returns only the evaluation results. This maintains optimization capability while preserving model secrecy.
Solution Approach 2:
The patent extracts the secret model evaluation logic from the quantum annealing process itself and places it in a separate oracle component. This extraction allows the quantum annealer to perform optimization without needing access to or knowledge of the underlying model details, thereby maintaining secrecy while preserving productivity.
3Productivity
If conventional quantum annealing methods are used, then annealing operations can be performed, but the solution accuracy is limited by the Hamiltonian approximation
Solution Approach 1:
The patent implements a feedback loop where the oracle evaluates candidate solutions and provides accurate cost function values back to the system. This feedback allows for iterative refinement of the Hamiltonian parameters and annealing schedule, improving solution accuracy while maintaining the efficiency of quantum annealing operations.
Solution Approach 2:
The patent replaces the mechanical approximation of cost functions within the Hamiltonian with an oracle-based evaluation system. Instead of embedding complex cost function calculations directly in the quantum system, the oracle substitutes this function, allowing accurate evaluation without compromising annealing efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and robustness of quantum annealing by allowing optimization without direct knowledge of the underlying models, improving the solution quality and efficiency of quantum annealers.
Implementation Method 1
quantum annealing models find global minimum energy states by taking advantage of quantum tunneling phenomenon between energy states as modeled by qubits
Data Source
AI summary
A method includes configuring a quantum annealer based on a parameter of a Hamiltonian and performing annealing using the configured quantum annealer to obtain output samples, wherein each sample of the output samples indicates state values of elements of the quantum annealer for a set of indices of the output samples. The method also includes providing, to an oracle, the output samples to obtain a set of oracle outputs. The method also includes determining an expectation based on the set of oracle outputs and updating a parameter indexed by the set of indices based on the expectation and a learning rate parameter.