Quantum Approximate Chaos Optimization for Hybrid Computing
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Solution Overview
Problem
Current hybrid quantum-classical computing systems face inefficiencies in the classical optimization routine for solving combinatorial optimization problems, particularly due to the exponential increase in time and resource requirements as targeted accuracy increases.
Innovation Solution
The method involves a classical computer selecting a problem, computing a model Hamiltonian, and choosing variational parameters to transform a quantum processor with trapped ions from an initial state to a trial state. The system measures the population of ion states and adjusts parameters using chaotic maps to optimize solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional stochastic optimization methods are used in the classical optimization routine, then the system can find approximate solutions to combinatorial optimization problems, but the time and resource requirements increase exponentially as targeted accuracy increases
Solution Approach 1:
The patent applies quantum parameter optimization by transforming the classical stochastic optimization problem into a quantum mechanical framework. The system uses quantum states to represent optimization parameters and employs quantum evolution to efficiently search the parameter space, achieving high accuracy without exponential time costs. This fundamentally changes the optimization approach from classical random sampling to quantum-driven systematic exploration.
Solution Approach 2:
The patent replaces the classical mechanical optimization system with a quantum mechanical system. Instead of using conventional stochastic algorithms that rely on random sampling and iterative improvement, the system utilizes quantum superposition, entanglement, and quantum tunneling effects to explore the solution space more efficiently, thereby reducing both time and computational resources required to achieve targeted accuracy.
2Reliability
If the number of qubits is increased to improve computation reliability, then the system can handle more complex problems, but control errors accumulate and limit the size of quantum computers that can perform reliable computations
Solution Approach 1:
The patent segments the quantum computation into modular components: a NISQ device for executing quantum subroutines and a classical computer for coordinating the overall optimization process. This segmentation allows the system to leverage quantum advantages for specific computational tasks while using classical systems for control and post-processing, thereby managing device complexity and maintaining reliability without requiring excessively large quantum systems.
Solution Approach 2:
The patent implements a dynamic hybrid quantum-classical optimization routine where the system adaptively adjusts the number of iterations, quantum circuit depth, and resource allocation based on problem complexity and convergence criteria. This dynamic approach allows the system to optimize performance for each specific problem instance, achieving reliable results with manageable qubit counts by avoiding unnecessary computational overhead.
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
This approach enhances the efficiency of the classical optimization routine, reducing the time and resource requirements for achieving optimized solutions in hybrid quantum-classical computing systems.
Implementation Method 1
These hyperfine states can be controlled using radiation provided from a laser, or sometimes referred to herein as the interaction with laser beams
Implementation Method 2
The ions can also be optically pumped to one of the two hyperfine states with high accuracy (preparation of qubits)
Implementation Method 3
A pair of ions can be controllably entangled (two-qubit gate operations) by qubit-state dependent force using laser pulses that couple the ions to the collective motional modes of a group of trapped ions, which arise from their Coulombic interaction between the ions
Implementation Method 4
is a group of ions (e.g., charged atoms), which are trapped and suspended in vacuum by electromagnetic fields
Data Source
AI summary
Embodiments described herein are generally related to a method and a system for performing a computation using a hybrid quantum-classical computing system, and, more specifically, to providing an approximate solution to a combinatorial optimization problem using a hybrid quantum-classical computing system that includes a group of trapped ions. A hybrid quantum-classical computing system that is able to provide a solution to a combinatorial optimization problem may include a classical computer, a system controller, and a quantum processor. The methods and systems described herein include an efficient method for an optimization routine executed by the classical computer in solving a problem in a hybrid quantum-classical computing system, which can provide improvement over the conventional method for an optimization by conventional stochastic optimization methods.