System and method to evaluate communication operations

1 . A system, comprising:

a memory operable to store:

a machine learning algorithm configured, when executed, to evaluate data in accordance with one or more machine learning models; and

at least one processor communicatively coupled to the memory and configured to:

receive, during a first authentication operation, first access feedback from a first user device requesting to access one or more network resources in a communication network;

train, using the first access feedback and historic information associated with authentication operations, a machine learning model to predict user device actions, and operations in the communication network;

monitor, using the machine learning algorithm in accordance with the trained machine learning model, operations triggered in the communication network after receiving the first access feedback for a first period of time;

determine, using the machine learning algorithm in accordance with the trained machine learning model, a first plurality of tracked activities and first metadata triggered after receiving the first access feedback;

in response to determining the first plurality of tracked activities and the first metadata triggered after receiving the first access feedback, generate, using the machine learning algorithm in accordance with the trained machine learning model, a first plurality of predicted activities based on the first metadata;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the first plurality of tracked activities matches the first plurality of predicted activities within a first predefined adverse impact threshold;

in response to determining that the first plurality of tracked activities matches the first plurality of predicted activities within the first predefined adverse impact threshold, generate, using the machine learning algorithm in accordance with the trained machine learning model, a first adverse impact assessment granting preliminary access between the first user device and the one or more network resources;

create a first quantum access command for the first user device, the first quantum access command comprising one or more first keys to access the one or more network resources; and

provide the first quantum access command to the first user device.

2 . The system of claim 1 , wherein the at least one processor is further configured to:

after a second period of time, receive a first request from the first user device comprising the first quantum access command;

evaluate, using the machine learning algorithm in accordance with the trained machine learning model, the one or more first keys in the first quantum access command against one or more qubits;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the one or more first keys in the first quantum access command match the one or more qubits;

in response to determining that the one or more first keys in the first quantum access command match the one or more qubits, determine, using the machine learning algorithm in accordance with the trained machine learning model, that the first quantum access command is authentic; and

provide access between the first user device and the one or more network resources.

3 . The system of claim 1 , wherein the at least one processor is further configured to:

after a second period of time, receive a first request from the first user device comprising the first quantum access command;

evaluate, using the machine learning algorithm in accordance with the trained machine learning model, the one or more first keys in the first quantum access command against one or more qubits;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the one or more first keys in the first quantum access command match the one or more qubits;

in response to determining that the one or more first keys in the first quantum access command do not match the one or more qubits, determine, using the machine learning algorithm in accordance with the trained machine learning model, that the first quantum access command is not authentic; and

deny access between the first user device and the one or more network resources.

4 . The system of claim 1 , wherein the at least one processor is further configured to:

receive, during a second authentication operation, second access feedback from a second user device requesting to access the one or more network resources in the communication network;

train, using the second access feedback and the historic information associated with the authentication operations, the machine learning model to predict the user device actions and the operations in the communication network;

monitor, using the machine learning algorithm in accordance with the trained machine learning model, operations triggered in the communication network after receiving the second access feedback for a second period of time;

determine, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of tracked activities and second metadata triggered after receiving the second access feedback;

generate, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of predicted activities based on the second metadata;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second plurality of tracked activities matches the second plurality of predicted activities within a second predefined adverse impact threshold;

in response to determining that the second plurality of tracked activities matches the second plurality of predicted activities within the second predefined adverse impact threshold, generate, using the machine learning algorithm in accordance with the trained machine learning model, a second adverse impact assessment granting preliminary access between the second user device and the one or more network resources;

create a second quantum access command for the second user device, the second quantum access command comprising one or more second keys to access the one or more network resources; and

provide the second quantum access command to the second user device.

5 . The system of claim 1 , wherein the at least one processor is further configured to:

receive, during a second authentication operation, second access feedback from a second user device requesting to access the one or more network resources in the communication network;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second user device is associated with historical data in the communication network, the historical data indicating whether the second user device is previously associated with the communication network;

in response to determining that the second user device is associated with the historical data, train, using the second access feedback and the historic information associated with the authentication operations, the machine learning model to predict the user device actions and the operations in the communication network;

monitor, using the machine learning algorithm in accordance with the trained machine learning model, the second access feedback for a second period of time;

determine, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of tracked activities and second metadata triggered after receiving the second access feedback;

generate, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of predicted activities based on the second metadata and the historical data;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second plurality of tracked activities matches the second plurality of predicted activities within a second predefined adverse impact threshold;

in response to determining that the second plurality of tracked activities matches the second plurality of predicted activities within the second predefined adverse impact threshold, generate, using the machine learning algorithm in accordance with the trained machine learning model, a second adverse impact assessment granting preliminary access between the second user device and the one or more network resources; and

create a second quantum access command for the second user device, the second quantum access command comprising one or more second keys to access the one or more network resources; and

provide the second quantum access command to the second user device.

6 . The system of claim 1 , the at least one processor is further configured to:

receive, during a second authentication operation, second access feedback from a second user device requesting to access the one or more network resources in the communication network;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second user device is associated with historical data in the communication network, the historical data indicating whether the second user device is previously associated with the communication network;

in response to determining that the second user device is not associated with the historical data, train, using the second access feedback and the historic information associated with the authentication operations, the machine learning model to predict the user device actions and the operations in the communication network;

monitor, using the machine learning algorithm in accordance with the trained machine learning model, the second access feedback for a second period of time;

determine, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of tracked activities and second metadata triggered after receiving the second access feedback;

generate, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of predicted activities based on the second metadata;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second plurality of tracked activities matches the second plurality of predicted activities within a second predefined adverse impact threshold;

in response to determining that the second plurality of tracked activities matches the second plurality of predicted activities within the second predefined adverse impact threshold, generate, using the machine learning algorithm in accordance with the trained machine learning model, a second adverse impact assessment granting preliminary access between the second user device and the one or more network resources; and

create a second quantum access command for the second user device, the second quantum access command comprising one or more second keys to access the one or more network resources;

provide the second quantum access command to the second user device; and

store the second plurality of predicted activities as historical data associated with the second user device.

7 . The system of claim 6 , wherein the at least one processor is further configured to:

store the second plurality of predicted activities as historical data associated with the second user device in accordance with one or more quantum encryption protocols.

8 . A method, comprising:

receiving, during a first authentication operation, first access feedback from a first user device requesting to access one or more network resources in a communication network;

training, using the first access feedback and historic information associated with authentication operations, a machine learning model to predict user device actions, and operations in the communication network;

monitoring, using a machine learning algorithm in accordance with the trained machine learning model, operations triggered in the communication network after receiving the first access feedback for a first period of time;

determining, using the machine learning algorithm in accordance with the trained machine learning model, a first plurality of tracked activities and first metadata triggered after receiving the first access feedback;

generating, using the machine learning algorithm in accordance with the trained machine learning model, a first plurality of predicted activities based on the first metadata;

determining, using the machine learning algorithm in accordance with the trained machine learning model, whether the first plurality of tracked activities matches the first plurality of predicted activities within a first predefined adverse impact threshold;

in response to determining that the first plurality of tracked activities matches the first plurality of predicted activities within the first predefined adverse impact threshold, generating, using the machine learning algorithm in accordance with the trained machine learning model, a first adverse impact assessment granting preliminary access between the first user device and the one or more network resources;

creating a first quantum access command for the first user device, the first quantum access command comprising one or more first keys to access the one or more network resources; and

providing the first quantum access command to the first user device.

9 . The method of claim 8 , further comprising:

after a second period of time, receiving a first request from the first user device comprising the first quantum access command;

evaluating, using the machine learning algorithm in accordance with the trained machine learning model, the one or more first keys in the first quantum access command against one or more qubits;

determining, using the machine learning algorithm in accordance with the trained machine learning model, whether the one or more first keys in the first quantum access command match the one or more qubits;

in response to determining that the one or more first keys in the first quantum access command match the one or more qubits, determining, using the machine learning algorithm in accordance with the trained machine learning model, that the first quantum access command is authentic; and

providing access between the first user device and the one or more network resources.

10 . The method of claim 8 , further comprising:

after a second period of time, receiving a first request from the first user device comprising the first quantum access command;

evaluating, using the machine learning algorithm in accordance with the trained machine learning model, the one or more first keys in the first quantum access command against one or more qubits;

determining, using the machine learning algorithm in accordance with the trained machine learning model, whether the one or more first keys in the first quantum access command match the one or more qubits;

in response to determining that the one or more first keys in the first quantum access command do not match the one or more qubits, determining, using the machine learning algorithm in accordance with the trained machine learning model, that the first quantum access command is not authentic; and

denying access between the first user device and the one or more network resources.

11 . The method of claim 8 , further comprising:

receiving, during a second authentication operation, second access feedback from a second user device requesting to access the one or more network resources in the communication network;

training, using the second access feedback and the historic information associated with the authentication operations, the machine learning model to predict the user device actions and the operations in the communication network;

monitoring, using the machine learning algorithm in accordance with the trained machine learning model, operations triggered in the communication network after receiving the second access feedback for a second period of time;

determining, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of tracked activities and second metadata triggered after receiving the second access feedback;

generating, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of predicted activities based on the second metadata;

determining, using the machine learning algorithm in accordance with the trained machine learning model, whether the second plurality of tracked activities matches the second plurality of predicted activities within a second predefined adverse impact threshold;

in response to determining that the second plurality of tracked activities matches the second plurality of predicted activities within the second predefined adverse impact threshold, generating, using the machine learning algorithm in accordance with the trained machine learning model, a second adverse impact assessment granting preliminary access between the second user device and the one or more network resources;

creating a second quantum access command for the second user device, the second quantum access command comprising one or more second keys to access the one or more network resources; and

providing the second quantum access command to the second user device.

12 . The method of claim 8 , further comprising:

receiving, during a second authentication operation, second access feedback from a second user device requesting to access the one or more network resources in the communication network;

determining, using the machine learning algorithm in accordance with the trained machine learning model, whether the second user device is associated with historical data in the communication network, the historical data indicating whether the second user device is previously associated with the communication network;

in response to determining that the second user device is associated with the historical data, training, using the second access feedback and the historic information associated with the authentication operations, the machine learning model to predict the user device actions and the operations in the communication network;

monitoring, using the machine learning algorithm in accordance with the trained machine learning model, the second access feedback for a second period of time;

determining, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of tracked activities and second metadata triggered after receiving the second access feedback;

generating, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of predicted activities based on the second metadata and the historical data;

determining, using the machine learning algorithm in accordance with the trained machine learning model, whether the second plurality of tracked activities matches the second plurality of predicted activities within a second predefined adverse impact threshold;

in response to determining that the second plurality of tracked activities matches the second plurality of predicted activities within the second predefined adverse impact threshold, generating, using the machine learning algorithm in accordance with the trained machine learning model, a second adverse impact assessment granting preliminary access between the second user device and the one or more network resources;

creating a second quantum access command for the second user device, the second quantum access command comprising one or more second keys to access the one or more network resources; and

providing the second quantum access command to the second user device.

13 . The method of claim 8 , further comprising:

receiving, during a second authentication operation, second access feedback from a second user device requesting to access the one or more network resources in the communication network;

determining, using the machine learning algorithm in accordance with the trained machine learning model, whether the second user device is associated with historical data in the communication network, the historical data indicating whether the second user device is previously associated with the communication network;

in response to determining that the second user device is not associated with the historical data, training, using the second access feedback and the historic information associated with the authentication operations, the machine learning model to predict the user device actions and the operations in the communication network;

monitoring, using the machine learning algorithm in accordance with the trained machine learning model, the second access feedback for a second period of time;

determining, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of tracked activities and second metadata triggered after receiving the second access feedback;

generating, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of predicted activities based on the second metadata;

determining, using the machine learning algorithm in accordance with the trained machine learning model, whether the second plurality of tracked activities matches the second plurality of predicted activities within a second predefined adverse impact threshold;

in response to determining that the second plurality of tracked activities matches the second plurality of predicted activities within the second predefined adverse impact threshold, generating, using the machine learning algorithm in accordance with the trained machine learning model, a second adverse impact assessment granting preliminary access between the second user device and the one or more network resources;

creating a second quantum access command for the second user device, the second quantum access command comprising one or more second keys to access the one or more network resources;

providing the second quantum access command to the second user device; and

storing the second plurality of predicted activities as historical data associated with the second user device.

14 . The method of claim 13 , further comprising:

storing the second plurality of predicted activities as historical data associated with the second user device in accordance with one or more quantum encryption protocols.

15 . A non-transitory computer-readable medium storing instructions that when executed by a processor cause the processor to:

receive, during a first authentication operation, first access feedback from a first user device requesting to access one or more network resources in a communication network;

train, using the first access feedback and historic information associated with authentication operations, a machine learning model to predict user device actions, and operations in the communication network;

monitor, using a machine learning algorithm in accordance with the trained machine learning model, operations triggered in the communication network after receiving the first access feedback for a first period of time;

determine, using the machine learning algorithm in accordance with the trained machine learning model, a first plurality of tracked activities and first metadata triggered after receiving the first access feedback;

generate, using the machine learning algorithm in accordance with the trained machine learning model, a first plurality of predicted activities based on the first metadata;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the first plurality of tracked activities matches the first plurality of predicted activities within a first predefined adverse impact threshold;

in response to determining that the first plurality of tracked activities matches the first plurality of predicted activities within the first predefined adverse impact threshold, generate, using the machine learning algorithm in accordance with the trained machine learning model, a first adverse impact assessment granting preliminary access between the first user device and the one or more network resources;

create a first quantum access command for the first user device, the first quantum access command comprising one or more first keys to access the one or more network resources; and

provide the first quantum access command to the first user device.

16 . The non-transitory computer-readable medium of claim 15 , wherein, when executed by the processor, the instructions further cause the processor to:

after a second period of time, receive a first request from the first user device comprising the first quantum access command;

evaluate, using the machine learning algorithm in accordance with the trained machine learning model, the one or more first keys in the first quantum access command against one or more qubits;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the one or more first keys in the first quantum access command match the one or more qubits;

in response to determining that the one or more first keys in the first quantum access command match the one or more qubits, determine, using the machine learning algorithm in accordance with the trained machine learning model, that the first quantum access command is authentic; and

provide access between the first user device and the one or more network resources.

17 . The non-transitory computer-readable medium of claim 15 , wherein, when executed by the processor, the instructions further cause the processor to:

after a second period of time, receive a first request from the first user device comprising the first quantum access command;

evaluate, using the machine learning algorithm in accordance with the trained machine learning model, the one or more first keys in the first quantum access command against one or more qubits;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the one or more first keys in the first quantum access command match the one or more qubits;

in response to determining that the one or more first keys in the first quantum access command do not match the one or more qubits, determine, using the machine learning algorithm in accordance with the trained machine learning model, that the first quantum access command is not authentic; and

deny access between the first user device and the one or more network resources.

18 . The non-transitory computer-readable medium of claim 15 , wherein, when executed by the processor, the instructions further cause the processor to:

receive, during a second authentication operation, second access feedback from a second user device requesting to access the one or more network resources in the communication network;

train, using the second access feedback and the historic information associated with the authentication operations, the machine learning model to predict the user device actions and the operations in the communication network;

monitor, using the machine learning algorithm in accordance with the trained machine learning model, operations triggered in the communication network after receiving the second access feedback for a second period of time;

determine, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of tracked activities and second metadata triggered after receiving the second access feedback;

generate, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of predicted activities based on the second metadata;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second plurality of tracked activities matches the second plurality of predicted activities within a second predefined adverse impact threshold;

in response to determining that the second plurality of tracked activities matches the second plurality of predicted activities within the second predefined adverse impact threshold, generate, using the machine learning algorithm in accordance with the trained machine learning model, a second adverse impact assessment granting preliminary access between the second user device and the one or more network resources;

create a second quantum access command for the second user device, the second quantum access command comprising one or more second keys to access the one or more network resources; and

provide the second quantum access command to the second user device.

19 . The non-transitory computer-readable medium of claim 15 , wherein, when executed by the processor, the instructions further cause the processor to:

receive, during a second authentication operation, second access feedback from a second user device requesting to access the one or more network resources in the communication network;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second user device is associated with historical data in the communication network, the historical data indicating whether the second user device is previously associated with the communication network;

in response to determining that the second user device is associated with the historical data, train, using the second access feedback and the historic information associated with the authentication operations, the machine learning model to predict the user device actions and the operations in the communication network;

monitor, using the machine learning algorithm in accordance with the trained machine learning model, the second access feedback for a second period of time;

determine, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of tracked activities and second metadata triggered after receiving the second access feedback;

generate, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of predicted activities based on the second metadata and the historical data;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second plurality of tracked activities matches the second plurality of predicted activities within a second predefined adverse impact threshold;

in response to determining that the second plurality of tracked activities matches the second plurality of predicted activities within the second predefined adverse impact threshold, generate, using the machine learning algorithm in accordance with the trained machine learning model, a second adverse impact assessment granting preliminary access between the second user device and the one or more network resources; and

create a second quantum access command for the second user device, the second quantum access command comprising one or more second keys to access the one or more network resources; and

provide the second quantum access command to the second user device.

20 . The non-transitory computer-readable medium of claim 15 , wherein:

receive, during a second authentication operation, second access feedback from a second user device requesting to access the one or more network resources in the communication network;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second user device is associated with historical data in the communication network, the historical data indicating whether the second user device is previously associated with the communication network;

in response to determining that the second user device is not associated with the historical data, train, using the second access feedback and the historic information associated with the authentication operations, the machine learning model to predict the user device actions and the operations in the communication network;

monitor, using the machine learning algorithm in accordance with the trained machine learning model, the second access feedback for a second period of time;

determine, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of tracked activities and second metadata triggered after receiving the second access feedback;

generate, using the machine learning algorithm in accordance with the trained machine learning model, a second plurality of predicted activities based on the second metadata;

determine, using the machine learning algorithm in accordance with the trained machine learning model, whether the second plurality of tracked activities matches the second plurality of predicted activities within a second predefined adverse impact threshold;

in response to determining that the second plurality of tracked activities matches the second plurality of predicted activities within the second predefined adverse impact threshold, generate, using the machine learning algorithm in accordance with the trained machine learning model, a second adverse impact assessment granting preliminary access between the second user device and the one or more network resources; and

create a second quantum access command for the second user device, the second quantum access command comprising one or more second keys to access the one or more network resources;

provide the second quantum access command to the second user device; and

store the second plurality of predicted activities as historical data associated with the second user device.