Hybrid Quantum-Classical Processor for Optimization
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Solution Overview
Problem
Current quantum annealing processors face limitations in solving hard optimization tasks due to restricted problem class embedding, finite range multi-qubit co-tunneling effects, and finite size, as well as being restricted to iterative classical preprocessing and postprocessing steps.
Innovation Solution
A hybrid quantum-classical information processor that utilizes dynamical thermal fluctuations and cluster update algorithms in conjunction with quantum annealing, allowing for simultaneous advantages of quantum and classical fluctuations to overcome energy barriers and achieve faster solution times for NP-hard optimization tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quantum annealing is used to solve optimization problems, then quantum fluctuations can overcome energy barriers, but the system is limited by finite size and restricted problem class embedding
Solution Approach 1:
The patent merges quantum annealing with classical fluctuation methods (simulated annealing, parallel tempering, cluster updates) into a hybrid quantum-classical system. The quantum processor handles quantum tunneling through energy barriers while classical processors handle thermal fluctuations and coordinate updates, combining the advantages of both approaches to overcome the limitations of pure quantum annealing in terms of problem embedding capability and system size.
Solution Approach 2:
The patent introduces classical information processors as intermediaries between the quantum processor and the optimization problem. These classical processors perform preprocessing, coordinate updates, and thermal fluctuations, acting as a mediator that extends the effective problem-solving capability beyond what the finite quantum system can handle alone, thereby improving adaptability to different problem classes.
2Reliability
If quantum annealing processors are used for optimization tasks, then quantum tunneling can find optimal solutions, but the system is restricted to iterative classical preprocessing and postprocessing steps
Solution Approach 1:
The patent combines quantum annealing with multiple classical optimization techniques (simulated annealing, parallel tempering, cluster updates) into a unified hybrid algorithm. This merging eliminates the need for separate iterative classical preprocessing and postprocessing steps by integrating classical and quantum operations into a single coordinated framework, reducing overall algorithmic complexity while maintaining optimal solution finding capability.
Solution Approach 2:
The hybrid quantum-classical system performs multiple functions within a single unified algorithmic framework. The quantum processor handles quantum tunneling and energy barrier crossing, while classical processors handle thermal fluctuations, coordinate updates, and problem decomposition. This multi-functionality reduces the need for separate classical preprocessing and postprocessing steps, simplifying the overall algorithmic structure.
3Reliability
If thermal annealing is used to achieve lowest energy configuration, then the system can find optimal solutions, but the process is time-consuming
Solution Approach 1:
The patent employs dynamic quantum fluctuations that can be adjusted during the annealing process. By controlling the strength and timing of quantum fluctuations, the system can accelerate the escape from local minima and reduce the time required to reach the global minimum energy configuration, compared to static thermal annealing approaches.
Solution Approach 2:
The patent utilizes quantum phase transitions and the quantum-to-classical transition during annealing. By controlling the quantum fluctuation strength parameter, the system can undergo phase transitions that enable rapid exploration of the energy landscape, significantly reducing the time required to find optimal solutions compared to conventional thermal annealing.
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
The hybrid processor significantly outperforms existing quantum and classical computers in solving hard combinatorial optimization problems, achieving faster run times and bypassing the shortcomings of existing quantum annealers by using a unified algorithmic approach that combines classical and quantum algorithms.
Implementation Method 1
applying dynamical quantum fluctuations to the set of input states and subsequent states when the states evolve within the quantum systems
Implementation Method 2
applying one or more of (i) dynamical thermal fluctuations and (ii) cluster update algorithms to the set of input states
Data Source
AI summary
Methods and apparatus for quantum assisted optimization. In one aspect, a method includes obtaining a set of initial input states, applying one or more of (i) dynamical thermal fluctuations and (ii) cluster update algorithms to the set of input states and subsequent input states when the states evolve within the classical information processors, applying dynamical quantum fluctuations to the set of input states and subsequent states when the states evolve within the quantum systems and repeating the application steps until a desirable output state is obtained.