Neutral-Atom Quantum Classifier Optimization for Faster Classification
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Solution Overview
Problem
Existing methods for solving complex classification problems with numerous variables require significant processing power and result in delayed solutions, impacting the efficiency and reliability of process, apparatus, and system control.
Innovation Solution
A method utilizing a quantum register formed by trapped neutral atoms and a hybrid approach combining classical and quantum processing devices to generate a boosted classifier, optimizing weighting factors through a QAOA routine, enabling quicker and more accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical processing devices are used to solve classification problems with numerous variables, then the equations can be solved with sufficient processing power, but the solution time is delayed and productivity is reduced
Solution Approach 1:
The patent replaces classical mechanical computing systems with a quantum computing system that uses quantum bits (qubits) and quantum mechanical phenomena (superposition, entanglement, interference) to perform classification computations. This substitution enables parallel processing of multiple computational paths simultaneously, dramatically reducing solution time while maintaining classification accuracy for complex datasets with numerous variables
2Measurement precision
If the number of variables in classification equations increases to account for different features, then measurement precision is improved, but processing power requirements increase and time loss occurs
Solution Approach 1:
The patent transitions from classical binary bits to quantum bits that exist in superposition states, effectively adding a dimensional aspect to data representation. This allows the quantum system to process multiple variable combinations simultaneously through quantum parallelism, maintaining high classification precision for datasets with many features while significantly reducing the time required to analyze complex multi-variable relationships
3Productivity
If quantum computing is used to shorten solution time, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the classification problem into distinct quantum computational components: feature encoding into quantum states, quantum circuit implementation for classification logic, and measurement/readout phases. This segmentation allows each component to be optimized independently and facilitates the use of modular quantum circuit designs, reducing overall system complexity while maintaining high solution speed for classification tasks
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 method provides enhanced classification performance with reduced computational cost, allowing for rapid detection of sub-optimal configurations and anomalies, and enabling more precise control of processes and systems.
Implementation Method 1
Trapping means, such as a magneto-optical trap, is set in the vacuum chamber (for example an ultra-high vacuum chamber) by way of at least a first laser (it could be one laser or a plurality of lasers) trapping neutral atoms of the ensemble of atoms in optical tweezers.
Implementation Method 2
The at least one second laser radiates the entire ensemble of atoms in accordance with the at least one laser parameter and, depending on the behavior of the atoms that is precisely at least dependent upon the at least one laser parameter, certain atoms of the ensemble of atoms will get excited
Data Source
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AI summary
The invention relates to a method comprising: setting (11), by one or more processing devices (6), a plurality of classifiers for classification of data in two or more classes, each of the two or more classes being associated to a numeric value, and each classifier of the plurality of classifiers being associated to one or more weighting factors; defining (13), by one or more processing devices (6), a cost function; optimizing, by one or more processing devices (6) and a quantum circuit (2), the one or more weighting factors associated to each classifier of the plurality of classifiers, by minimizing the defined cost function as follows: radiating (21) a vacuum chamber (3) comprising an ensemble of neutral atoms with a laser (4a) so as to trap atoms of the ensemble of neutral atoms in an array of optical tweezers, thereby providing a quantum register (60a), and each optical tweezer comprising a single neutral atom; digitally configuring (22) at least one laser parameter for implementing one or more unitary operations (L), wherein the one or more unitary operations (L) are dependent at least upon the at least one laser parameter; radiating (31) the ensemble of atoms with laser light so as to excite at least some atoms of the quantum register (60a), with a laser (4b) being operated in accordance with the at least one laser parameter to implement one or more unitary operations (L) in the quantum circuit (2); reading (41) the quantum register (60b) with optical means, thereby obtaining a string of bits based on an amount of light produced by the atoms, thus mapping binary values of the weighting factors of the classifiers to a state of qubits in the quantum register (60b); using the string of bits to compute the cost function; updating, for reducing the cost function, the at least one laser parameter of the laser (4b) by an optimization algorithm until the weighting factors are optimized; digitally storing (43) at least a result of the cost function as last computed; and setting (45), by one or more processing devices (6), a boosted classifier based on the optimized weighting factors.