LLY-DML is part of the LILY project and is a Quantum Machine Learning model. It uses so-called L-Gates. These gates are Machine Learning gates that modify their state based on an input to map to a desired state of an input.
The Preoptimize class is designed to optimize quantum states by processing and adjusting measurement data. It includes several attributes and methods that are carefully validated to ensure error-free execution. Comprehensive logging is essential to make the optimization process transparent and traceable. In addition to error and success codes, relevant status information and process steps must be logged.
Attributes
qubits_measurement
Description: Stores the measurement results for each qubit.
target_state
Description: Defines the desired target state for optimization.
train_matrix
Description: Contains the current training matrix, loaded from train.json.
qubits
Description: Indicates the number of qubits in the system.
depth
Description: Determines the depth of the quantum process.
Methods
read()
Description:
This method reads the number of qubits and the depth (qubits and depth) from the data.json file. It then extracts the latest training matrix from the train.json file.
Checks and Codes:
Existence Check for data.json:
Error Code 1060:data.json not found.
Existence Check for train.json:
Error Code 1070:train.json not found.
Success Code 2070:train.json successfully found.
Data Validation:
Verifies that the loaded data has the required structure and types.
Error Code 1061: Invalid format in data.json.
Logging:
Start and completion of the read operation.
Details of loaded qubits and depth.
Results of existence and data validation checks.
start(optimizer, target_state)
Description:
Initiates the optimization process with the specified optimizer and target state.
Checks and Codes:
Validation of Target State:
Checks if the length of the target state matches the number of qubits.
Error Code 1071: Target state has incorrect formatting.
Optimizer Verification:
Verifies if the specified optimizer exists in the module/optimizer directory.
Error Code 1072: Optimizer not found.
Initialization Confirmation:
Success Code 2071: Target state and optimizer successfully validated and initialized.
Success Code 2070:train.json successfully found and loaded.
Logging:
Details of the selected optimizer and target state.
Results of validation checks.
Timestamp when the optimization process starts.
optimize(measurement)
Description:
Performs the actual optimization by iteratively processing new measurement values and adjusting the training matrix.
Checks and Codes:
Start of Data Processing:
Success Code 2073: Successful data collection at the beginning of optimization.
Processing Measurement Data:
Error Code 1074: Inconsistent data structure after assigning measurement values.
Optimization Execution:
Error Code 1077: Optimization error while writing the training matrix.
Success Code 2077: Optimization successfully completed and training matrix updated.
Workflow:
Read Current Data:
Calls the read() method.
Encode Measurements:
Calls the encode_measurements(measurement) method.
Assign Measurement Values to Qubits:
Assigns each row of the matrix to a specific qubit, including the distribution of measurements and the desired target value.
Example: If the first row has 3 values, it might look like (1, 2, 3) Qubit_0 (1:200; 0:50) (S:1) where the first numbers represent the training matrix values, followed by measurement distribution and the target state.
Validate Data Structure:
Ensures the consistency of data after assignment.
Instantiate Optimizer:
Creates an instance of the selected optimizer and provides the necessary data.
Perform Optimization:
Iteratively adjusts qubit values and aggregates the optimized values into the training matrix.
Save Updated Training Matrix:
Writes the optimized matrix back to train.json.
Logging:
Start and end of each iteration.
Results of measurement data processing and assignment.
Status updates during optimization steps.
Error details if any errors occur.
encode_measurements(measurement)
Description:
Converts raw measurement data into a structured format suitable for optimization.
Workflow:
Input Format:
Expects a dictionary with states and their frequencies, e.g., {'000': 512, '111': 488}.
Evaluate Measurements:
Counts how often each digit (0 or 1) appears in each qubit position across all states.
Store Measurement Results:
Saves the counted values under qubits_measurement for each qubit.
Validate Number of Qubits:
Checks if the number of qubits matches the specified qubits value.
Error Code 1073: Inconsistent data due to incorrect number of qubits.
Logging:
Received measurement data and processing steps.
Results of counting qubit states.
Validation results for the number of qubits.
Logging Requirements
Comprehensive logging is essential for the entire optimization process to quickly identify error sources and track progress. The following information should be logged in addition to error and success codes:
Timestamp: Each log entry should include a timestamp to trace the sequence of events.
Process Steps: Description of the currently executed steps and methods.
Data States: Relevant data before and after significant processing steps, without revealing sensitive information.
Error Details: Detailed information about any errors that occur, including context and possible causes.
Performance Metrics: Information on runtime and resource usage to identify optimization opportunities.
User Actions: Records of inputs and actions that influence the optimization process.
Summary
The Preoptimize class is a sophisticated module for adjusting and improving quantum states through iterative measurement data analysis and optimization techniques. Through extensive checks and clear error and success codes, the robustness and reliability of the process are ensured. Comprehensive logging guarantees that every step of the process is transparently documented, enabling efficient error resolution and process optimization.
Class: Preoptimize
The
Preoptimizeclass is designed to optimize quantum states by processing and adjusting measurement data. It includes several attributes and methods that are carefully validated to ensure error-free execution. Comprehensive logging is essential to make the optimization process transparent and traceable. In addition to error and success codes, relevant status information and process steps must be logged.Attributes
qubits_measurement
target_state
train_matrix
train.json.qubits
depth
Methods
read()Description:
qubitsanddepth) from thedata.jsonfile. It then extracts the latest training matrix from thetrain.jsonfile.Checks and Codes:
data.json:data.jsonnot found.train.json:train.jsonnot found.train.jsonsuccessfully found.data.json.Logging:
start(optimizer, target_state)Description:
Checks and Codes:
module/optimizerdirectory.train.jsonsuccessfully found and loaded.Logging:
optimize(measurement)Description:
Checks and Codes:
Workflow:
read()method.encode_measurements(measurement)method.(1, 2, 3) Qubit_0 (1:200; 0:50) (S:1)where the first numbers represent the training matrix values, followed by measurement distribution and the target state.train.json.Logging:
encode_measurements(measurement)Description:
Workflow:
{'000': 512, '111': 488}.qubits_measurementfor each qubit.qubitsvalue.Logging:
Logging Requirements
Comprehensive logging is essential for the entire optimization process to quickly identify error sources and track progress. The following information should be logged in addition to error and success codes:
Summary
The
Preoptimizeclass is a sophisticated module for adjusting and improving quantum states through iterative measurement data analysis and optimization techniques. Through extensive checks and clear error and success codes, the robustness and reliability of the process are ensured. Comprehensive logging guarantees that every step of the process is transparently documented, enabling efficient error resolution and process optimization.