Quantum Model Training via Dynamic Circuit Depth
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Solution Overview
Problem
Existing quantum computers, classified as noisy intermediate-scale quantum (NISQ) devices, face challenges in training parameters of computationally demanding quantum computing system models due to noise, error-prone qubits, and issues like vanishing gradients and local minima/maxima traps.
Innovation Solution
A method involving selecting multiple data subsets from a set of data, training multiple parameters of a quantum computing system model using these subsets and adjusting the quantum circuit depth, and retraining on a quantum computer to improve training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If quantum circuit depth is increased to improve model training accuracy, then training precision improves, but noise and errors increase due to NISQ device limitations
Solution Approach 1:
The training process is segmented into multiple iterations with progressively deeper quantum circuits. Each iteration trains on a subset of data with a specific circuit depth, and the process repeats with increased depth. This segmentation allows the system to gradually improve accuracy while managing noise accumulation at each stage.
Solution Approach 2:
The method performs preliminary training with shallow quantum circuits before progressing to deeper circuits. This preliminary action establishes a baseline model that can be progressively refined, allowing the system to benefit from deeper circuits only after the foundation is solid, thereby reducing the impact of noise in early training stages.
2Manufacturing precision
If quantum circuit depth is increased to overcome vanishing gradients, then training effectiveness improves, but computational time increases
Solution Approach 1:
The training process uses periodic action by alternating between classical optimization steps and quantum circuit evaluation. The quantum circuits are executed periodically at specific intervals during the training process, allowing the system to benefit from quantum computation only when necessary, thereby reducing total computational time while still overcoming vanishing gradients.
Solution Approach 2:
The quantum circuit depth is made dynamic rather than static, adjusting the circuit depth based on the training iteration number and performance requirements. This dynamic adjustment allows the system to use deeper circuits only when needed to overcome vanishing gradients, rather than consistently using maximum depth, thereby reducing overall computational time.
3Reliability
If multiple data subsets are used for training iterations, then model robustness improves, but training complexity increases
Solution Approach 1:
The training data is segmented into multiple subsets that are processed in different iterations. Each subset is used to train the quantum model with specific circuit depth configurations. This segmentation approach improves model robustness by exposing it to diverse data patterns while managing training complexity through structured organization of the training process.
Solution Approach 2:
The training process incorporates feedback mechanisms where the performance on each data subset is evaluated and used to adjust subsequent training iterations. This feedback loop allows the system to improve robustness by identifying and addressing weaknesses in specific data subsets while maintaining manageable complexity through systematic performance monitoring and adjustment.
Data Source
AI summary
A method may include selecting multiple data subsets from a data set and training multiple parameters of a quantum computing system model using the multiple data subsets and a quantum circuit depth using a quantum computer over multiple iterations. The method may include generating a solution for the plurality of data subsets using the quantum computing system model and the quantum computer. The method may also include comparing the solution to a threshold solution. The method may include adjusting the quantum circuit depth in response to the solution of the quantum computing system model not satisfying the threshold solution. The method may additionally include retraining, using the quantum computer, the multiple parameters of the quantum computing system model using the multiple data subsets and adjusted quantum circuit depth.