DETAILED ACTION Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Examiner Remarks The execution of the quantum subcircuits that resulted from the cutting of the quantum circuit (as indicated in claim 9) appears to help provide an indication of use of what could be an improvement, but the improvement is not apparent from the current claims. The claims do not give an indication on how the probability for determining cutting works. As a result, the claims do not give any indication of an improvement that could be created from cutting the quantum circuit, as there is no indication the cutting is being performed in any particular way. Meaning, under BRI, the claimed system/method appears to just cut a quantum circuit with no particular rhyme or reason. If the probability recited in claim 1 was a measure of something instead of any probability, then claim 1 might indicate the cuts related to the probability cause something, such as the improvements mentioned in the specification. Without an indication on what the probability measures, the cuts caused by the probability have no indication to cause a form of improvement, such as the probability could be a measure of the worst place to cut or the probability could be the measure that the cut creates a particular graph shape . Claim 6 may note a maximum probability, but without an indication of what the probability is the BRI of claim 6 does not appear to give any indication as to how the cutting works. Adding elements to the claim to help indicate how the cutting works could help provide the required details in the claims to indicate the improvements noted in the specification. Claim 2 may indicate that data from previous cut graphs is used, but claim 2 also does not indicate as how the cuts were performed, thus any probability is still available under BRI. Information Disclosure Statement The information disclosure statement filed 08/11/2023 fails to comply with 37 CFR 1.98(a)(1), which requires the following: (1) a list of all patents, publications, applications, or other information submitted for consideration by the Office; (2) U.S. patents and U.S. patent application publications listed in a section separately from citations of other documents; (3) the application number of the application in which the information disclosure statement is being submitted on each page of the list; (4) a column that provides a blank space next to each document to be considered, for the examiner’s initials; and (5) a heading that clearly indicates that the list is an information disclosure statement. The information disclosure statement has been placed in the application file, but the information referred to therein has not been considered. The reason for the failure to comply is reasoning “(1) a list of all patents, publications, applications, or other information submitted for consideration by the Office”, as an NPL submitted on 08/11/2023 titled “Unsupervised Learning for Real-Time Detection of Events of Far Edge Mobile Device Trajectories” is not listed in an IDS. The IDS itself is considered, but the information referred to therein by the unlisted NPL has not been considered. If the NPL is an existing patent application or publication, then the NPL can instead listed as such on the IDS. Specification The disclosure is objected to because of the following informalities: paragraph 17 notes the word include twice in a typo in “The classical computing resources 120 (e.g., servers, nodes, containers, clusters, virtual machines) may include include processors…”. Appropriate correction is required. Claim Objections Claim 6 and 16 are objected to because of the following informalities: the claims recite “cutting locations” instead of “cutting points” (as other claims such as claim 1 indicate the term is cutting points). Appropriate correction is required. Claim Rejections - 35 USC § 112 Regarding 112(b): The following is a quotation of 35 U.S.C. 112(b): (b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 8 and 18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. In regard to Claim 8: Claim 8 recites the limitation " wherein a probability suggests a cutting point when a probability for the corresponding vector layer is above a threshold probability ". There is insufficient antecedent basis for this limitation in the claim. Claim 1 recites “ wherein each of the vector layers having a probability higher than a threshold probability is identified as a cutting point ”. Both claims recite “ a probability ” (“ a probability for the corresponding vector layer” and “ vector layers having a probability” ) and “ a threshold probability ”. One of ordinary skill in the art cannot determine whether the recitations in claim 8 is intended to refer to the same elements as in claim 1 or if the elements of claim 8 are separate. Appropriate correction is required. Can alter claim 8 to refer to the elements as “the probability” and “the threshold probability” if the claim intended to invoke the elements from claim 1. In regard to claim 18: Claim 18 is rejected for the same reason as claim 8 as claim 18 is analogous to claim 8. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed towards an abstract idea without significantly more. In regards to Claim 1: Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter? Yes, the claim is directed towards a method, so a process. Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 1 recites the following abstract ideas: wherein each of the vector layers having a probability higher than a threshold probability is identified as a cutting point This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as judgement. Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 1 recites the following additional elements: receiving a quantum circuit at an orchestration engine This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). wherein the orchestration is configured to orchestrate execution of the quantum circuit At a high level of generality, this is an activity of using an orchestration engine as an “apply it” use (see MPEP 2106.05(f)). generating a graph that corresponds to the quantum circuit, wherein the graph includes graph layers At a high level of generality, this is an activity of using quantum circuit as an “apply it” use (see MPEP 2106.05(f)). generating a vector layer for each of the graph layers At a high level of generality, this is an activity of using graph layers as an “apply it” use (see MPEP 2106.05(f)). and providing the vector layers as input to a recurrent model, wherein the recurrent model is configured to generate a probability for each of the vector layers This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). and performing a cutting operation to cut the quantum circuit at one or more of the cutting points identified by the recurrent model, wherein each of the cutting points corresponds to a graph layer of the graph At a high level of generality, this is an activity of using a recurrent model as an “apply it” use (see MPEP 2106.05(f)). No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 1 recites the following additional elements: receiving a quantum circuit at an orchestration engine This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). wherein the orchestration is configured to orchestrate execution of the quantum circuit At a high level of generality, this is an activity of using an orchestration engine as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, an orchestration engine appears to be an implementation of the abstract idea on a computer, so merely using a computer as a tool to perform the abstract idea. The interpretation of the orchestration engine being a computer, such as a classical processor, is supported by [0017]: “The orchestration of the quantum job 108 may be performed by an orchestration engine 110 that includes or has access to execution resources 140, which includes classical computing resources 120 and quantum computing resources 134. The classical computing resources 120 (e.g., servers, nodes, containers, clusters, virtual machines) may include include processors, memory, and the like. As previously stated, some aspects of the quantum job 108 may be executed in classical computing systems 120 and other aspects may be executed in quantum computing resources 134 (simulated or real)”. generating a graph that corresponds to the quantum circuit, wherein the graph includes graph layers At a high level of generality, this is an activity of using quantum circuit as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “generating a graph” using a quantum circuit does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. generating a vector layer for each of the graph layers At a high level of generality, this is an activity of using graph layers as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “generating a vector layer” using graph layers does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. and providing the vector layers as input to a recurrent model, wherein the recurrent model is configured to generate a probability for each of the vector layers This limitation is directed towards the insignificant extra solution activity of mere data gathering (see MPEP § 2106.05(g)). This is a well understood, routine, conventional activity of transmitting data (see MPEP 2106.05(d) example i in computer functions). and performing a cutting operation to cut the quantum circuit at one or more of the cutting points identified by the recurrent model, wherein each of the cutting points corresponds to a graph layer of the graph At a high level of generality, this is an activity of using a recurrent model as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “performing a cutting operation” using a recurrent model does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. In regards to Claim 2: Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 2 recites the following additional elements: training the model using a dataset that includes a plurality of graphs corresponding to previously cut quantum circuits and successful cutting locations of those graphs At a high level of generality, this is an activity of using a dataset as an “apply it” use (see MPEP 2106.05(f)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 2 recites the following additional elements: training the model using a dataset that includes a plurality of graphs corresponding to previously cut quantum circuits and successful cutting locations of those graphs At a high level of generality, this is an activity of using a dataset as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “training the model” using a dataset does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. In regards to Claim 3: Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 3 recites the following additional elements: wherein the graph comprises a directed acyclic graph At a high level of generality, this is a continuation of an activity of generating a graph as an “apply it” use (see MPEP 2106.05(f)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 3 recites the following additional elements: wherein the graph comprises a directed acyclic graph At a high level of generality, this is a continuation of an activity of generating a graph as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a graph being a directed acyclic graph does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. In regards to Claim 4: Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 4 recites the following additional elements: generating each of the vector layers using a subgraph of the graph, wherein the subgraph includes k consecutive layers of the graph At a high level of generality, this is a continuation of an activity of using generating vector layers as an “apply it” use (see MPEP 2106.05(f)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 4 recites the following additional elements: generating each of the vector layers using a subgraph of the graph, wherein the subgraph includes k consecutive layers of the graph At a high level of generality, this is a continuation of an activity of using generating vector layers as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “generating each of the vector layers” where there are k consecutive layers does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. In regards to Claim 5: Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 5 recites the following additional elements: wherein the k consecutive layers includes at least one layer before a current layer of the graph and at least one layer after the current layer At a high level of generality, this is a continuation of an activity of using generating vector layers as an “apply it” use (see MPEP 2106.05(f)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 5 recites the following additional elements: wherein the k consecutive layers includes at least one layer before a current layer of the graph and at least one layer after the current layer At a high level of generality, this is a continuation of an activity of using generating vector layers as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a description of k consecutive layers from claim 4 does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. In regards to Claim 6: Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 6 recites the following additional elements: performing the cutting operation based on specific probabilities, wherein the specific probabilities include probabilities that are close to maximums and minimums, wherein the cutting locations correspond to the layers associated with probabilities that are close to maximums At a high level of generality, this is an activity of using probabilities as an “apply it” use (see MPEP 2106.05(f)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 6 recites the following additional elements: performing the cutting operation based on specific probabilities, wherein the specific probabilities include probabilities that are close to maximums and minimums, wherein the cutting locations correspond to the layers associated with probabilities that are close to maximums At a high level of generality, this is an activity of using probabilities as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “cutting” using probabilities does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. In regards to Claim 7: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 7 recites the following abstract ideas: determining how many subcircuits to generate based on the cutting points This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation. In regards to Claim 8: Step 1: Is the claim directed towards a process, machine, manufacture, or composition of matter? Yes, the claim is directed towards a method, so a process. Step 2A Prong 8: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 8 recites the following abstract ideas: a probability suggests a cutting point when the probability for the corresponding vector layer is above a threshold probability This limitation is directed towards the continuation of the abstract ideas of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3) from claim 1. In regards to Claim 9: Step 2A Prong 2: Does the claim recite additional elements that integrate the exception into a practical application of the exception? No, the application does not recite any additional elements that would integrate the abstract idea into a practical application. Claim 9 recites the following additional elements: executing quantum subcircuits resulting from cutting the quantum circuit At a high level of generality, this is an activity of using quantum subcircuits as an “apply it” use (see MPEP 2106.05(f)). Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? No, the claim as a whole does not amount to significantly more than the judicial exception. All elements of the claim, viewed individually or wholistically, do not provide an inventive concept or otherwise significantly more than the abstract idea itself. Claim 9 recites the following additional elements: executing quantum subcircuits resulting from cutting the quantum circuit At a high level of generality, this is an activity of using quantum subcircuits as an “apply it” use (see MPEP 2106.05(f)). At said high level of generality, a generic recitation of “executing quantum subcircuits” does not incorporate the abstract idea into a practical invention and is seen as a variation of the phrase “apply it”. In regards to Claim 10: Step 2A Prong 1: Does the claim recite a law of nature, a natural phenomenon, or an abstract idea? Yes, the claim does recite a(n) abstract idea. Claim 10 recites the following abstract ideas: determining whether the quantum circuit can be cut based on the probabilities prior to performing the cutting operation This limitation is directed towards the abstract idea of a mental process, or a concept performed in the human mind, including observation, evaluation, judgement or opinion (see MPEP 2106.04(a)(2) subsection 3). Here the limitation is seen as evaluation and judgement. In regards to Claim 11: Claim 11 is analogous to claim 1 in regards to 101. In regards to Claim 12: Claim 12 is analogous to claim 2 in regards to 101. In regards to Claim 13: Claim 13 is analogous to claim 3 in regards to 101. In regards to Claim 14: Claim 14 is analogous to claim 4 in regards to 101. In regards to Claim 15: Claim 15 is analogous to claim 5 in regards to 101. In regards to Claim 16: Claim 16 is analogous to claim 6 in regards to 101. In regards to Claim 17: Claim 17 is analogous to claim 7 in regards to 101. In regards to Claim 18: Claim 18 is analogous to claim 8 in regards to 101. In regards to Claim 19: Claim 19 is analogous to claim 9 in regards to 101. In regards to Claim 20: Claim 20 is analogous to claim 10 in regards to 101. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Claims 1 -20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al (“CutQC: using small Quantum computers for large Quantum circuit evaluations”) , referred to as Tang in this document , and further in combination with Gu et al (“A Deep Learning Algorithm for the Max-Cut Problem Based on Pointer Network Structure with Supervised Learning and Reinforcement Learning Strategies”), referred to as Gu in this document, and further in combinations with Kukar et al (“Reliable Classifications with Machine Learning”), referred to as Kukar in this document . Regarding Claim 1: Tang teaches: A method comprising: receiving a quantum circuit at an orchestration engine, wherein the orchestration is configured to orchestrate execution of the quantum circuit; [Tang 4 Framework Overview page 5]: “Figure 5 summarizes the key components of CutQC. Our framework is built on top of IBM’s Qiskit [2] package in order to use IBM’s quantum devices, but we note that the hybrid approach works with any gate-based quantum computing platforms [wherein the orchestration is configured to orchestrate execution of the quantum circuit; where this part indicates the quantum components ] . Given a quantum circuit specified as an input [A method comprising: receiving a quantum circuit at an orchestration engine,] , the first step is to decide where to make cuts.” Further support for computer hardware in [Tang Abstract page 1]: “This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers [ where the teaching of classical computers is seen as teaching elements such as a processor, memory, and computer readable medium ] and quantum computers to enable evaluation of quantum circuits that cannot be run on classical or quantum computers alone.” generating a graph that corresponds to the quantum circuit, wherein the graph includes graph layers; [Tang 4.1 MIP Cut Searcher page 6]: “A quantum circuit can be modeled as a directed acyclic graph [generating a graph that corresponds to the quantum circuit, wherein the graph includes graph layers;] G.” generating a vector layer for each of the graph layers; [Tang 2 Background page 3]: “State vector [generating a vector layer for each of the graph layers where Tang is noting that aspects of a quantum circuit can be represented as vectors and earlier taught that a quantum circuit could be represented as an acyclic graph ] simulation (Figure 2a) is typically an idealized noise less simulation of a quantum circuit. All quantum operations are represented as unitary matrices.” Support for vector layer indicating that the graph can be represented as vectors is from [Current Application 0033]: “To generate the vector representation of a layer (represented as vector representations or vector layers) 412 and 414, a graph embedding engine 410 is configured to transform a subgraph of DAG layers into a numerical vectorial representation or into a vector layer.” and performing a cutting operation to cut the quantum circuit at one or more of the cutting points identified by the recurrent model , wherein each of the cutting points corresponds to a graph layer of the graph [Tang 3.3 Circuit Cutting: Challenges page 5]: “The first challenge is to find cut locations. While quantum circuits can always be split into smaller ones, finding optimal cut locations is crucial in order to minimize the classical postprocessing overhead. In general, large quantum circuits may require more than one cut [and performing a cutting operation to cut the quantum circuit at one or more of the cutting points identified, wherein each of the cutting points corresponds to a graph layer of the graph where the parts of the graph being cut are elements of a graph and thus considered akin to a graph layer ] in order to be separated into subcircuits.” Tang does not explicitly teach: and providing the vector layers as input to a recurrent model, wherein the recurrent model is configured to generate a probability for each of the vector layers, by the recurrent model Tang does not explicitly note a recurrent model for use on graphs, but the premise of recursive aspects is noted in [Tang 4.3 Dynamic Definition Algorithm 1] wherein each of the vector layers having a probability higher than a threshold probability is identified as a cutting point; Tang notes the related information of automatic calculation of optimal cutting points [Tang 4 Framework Overview page 5]: “Figure 5 summarizes the key components of CutQC. Our framework is built on top of IBM’s Qiskit [2] package in order to use IBM’s quantum devices, but we note that the hybrid approach works with any gate-based quantum computing platforms. Given a quantum circuit specified as an input, the first step is to decide where to make cuts. We propose the first automatic scheme that uses mixed integer programming to find optimal cuts for arbitrary quantum circuits. The backend for the MIP cut searcher is implemented in the Gurobi solver [18]. Small quantum devices then evaluate the different combinations of the subcircuits. Eventually, a parallel 𝐶 implementation of the reconstructor postprocesses the subcircuit outputs and reproduces the original full circuit outputs from the Kronecker products.” Gu teaches: and providing the vector layers as input to a recurrent model, wherein the recurrent model is configured to generate a probability for each of the vector layers, by the recurrent model [ Gu 3 Models page 4]: “It is difficult for traditional neural networks to classify subsequent events by using previous event information. However, an RNN [and providing the vector layers as input to a recurrent model, wherein the recurrent model is configured to generate a probability for each of the vector layers,][ by the recurrent model] can continuously operate information in a cyclic manner to ensure that the information persists, thereby effectively processing time-series data of any length.” Support for the model being able to utilize probability is given in [Gu 4.1.1 page 9]: “In the solution phase, we select the one with the highest probability in the output probability distribution and set it to 1, and the rest of the positions to 0. According to the result of Opredict, the pointer network selects the variables x1 and x3 with a value of 1, and the remaining variables have a value of 0—which is consistent with the result selected by Olabel” One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine Tang and G u. Tang and G u are in the same field of endeavor of graph partitioning or cutting graphs. One of ordinary skill in the art would have been motivated to combine Tang and G u in order to take advantage of machine learning, such as recurrent models and supervised learning, to help create an answer to a problem where exact solutions at large scale are difficult ([Gu Abstract page 1]: “The Max-cut problem is a well-known combinatorial optimization problem, which has many real-world applications. However, the problem has been proven to be non-deterministic polynomial-hard (NP-hard), which means that exact solution algorithms are not suitable for large-scale situations, as it is too time-consuming to obtain a solution. Therefore, designing heuristic algorithms is a promising but challenging direction to effectively solve large-scale Max-cut problems. For this reason, we propose a unique method which combines a pointer network and two deep learning strategies (supervised learning and reinforcement learning) in this paper, in order to address this challenge.”). Recurrent models are noted to be useful for data of any length ([Gu 3 Models page 4]: “It is difficult for traditional neural networks to classify subsequent events by using previous event information. However, an RNN can continuously operate information in a cyclic manner to ensure that the information persists, thereby effectively processing time-series data of any length.”) Kukar teaches: wherein each of the vector layers having a probability higher than a threshold probability is identified as a cutting point; [Kukar 3.4 Reliable and Unreliable Classifications page 8]: “Since the datasets used for training classifiers vary in their representativeness and noise levels as well as Machine Learning algorithms vary in strength and assumptions of their underlying models, it is hard to obtain absolute thresholds [wherein each of the vector layers having a probability higher than a threshold probability is identified as a cutting point;] for reliable classifications. In our experiments they varied between 0.20 and 0.70 for different domains and Ma chine Learning algorithms.” The aspects of a cutting point come from primary reference Tang and secondary reference Gu being based on cutting or partitioning graphs. Kukar is being used to teach the aspect of have a probability threshold. One of ordinary skill in the art, prior to the effective filing date, would have been motivated to combine modified Tang and Kukar. Modified Tang and Kukar are in the same field of endeavor of machine learning (as modified Tang utilizes the teaches of Gu, which teaches machine learning). One of ordinary skill in the art would have been motived to combine Modified Tang and Kukar in order to utilize the advantages of probability thresholds for situations like classification. The current problem and limitations mirror aspects of classification, such as labeling something as a cutting point or not (supported by [Current Application 0034]: “The cutting points 554 for the DAGs 552 in the training dataset 550 are labeled with a probability of 1 and the other layers are labelled with a probability of 0.”) . Thus the classification with probability can be improved by utilizing the probability or such as a form of measurement of confidence or reliability ([Kukar Introduction page 1]: “There have been numerous attempts to assign probabilities to Machine Learning classifiers’ (decision trees and rules, Bayesian classifiers, neural networks, nearest neighbour classifiers, ...)in order to interpret their decision as a probability distribution over all possible classes. In fact, we can trivially convert every Machine Learning classifier’s output to a probability distribution by assigning the predicted class the probability 1, and 0 to all other possible classes. The posterior probability of the predicted class can be viewed as a classifier’s trust in its prediction (reliability) [3, 19]. However, such estimations may not be good due to the applied algorithm’s language and representational biases.”), where a threshold of reliability enables more reliable classifications ([Kukar 3.4 Reliable and Unreliable Classifications page 8]: “Since the datasets used for training classifiers vary in their representativeness and noise levels as well as Machine Learning algorithms vary in strength and assumptions of their underlying models, it is hard to obtain absolute thresholds for reliable classifications. In our experiments they varied between 0.20 and 0.70 for different domains and Machine Learning algorithms.”). Regarding Claim 2: The method of claim 1 is taught by Tang, Gu, and Kukar Gu teaches : further comprising training the model using a dataset that includes a plurality of graphs corresponding to previously cut quantum circuits and successful cutting locations of those graphs [Gu 4.1 Supervised Learning]: “The goal of supervised learning [further comprising training the model using a dataset that includes a plurality of graphs corresponding to previously cut quantum circuits and successful cutting locations of those graphs as these limitation indicate the use of supervised or labeled data, where Gu et al teaching using supervised learning on graphs. Tang teaches the premise of quantum circuits being able to be graphs and thus teaches the datatype. ] is to learn the relationship between the input x and the output y by modeling 𝑦 = 𝑓 ( 𝑥 ; 𝜃 ) or 𝑝 ( 𝑦 | 𝑥 ; 𝜃 ).” The motivation to combine with Gu is the same motivation to combine with Gu as used in claim 1. Regarding Claim 3: The method of claim 1 is taught by Tang, Gu, and Kukar Tang teaches: wherein the graph comprises a directed acyclic graph [Tang 4.1 MIP Cut Searcher page 6]: “A quantum circuit can be modeled as a directed acyclic graph [wherein the graph comprises a directed acyclic graph] G.” Regarding Claim 4: The method of claim 1 is taught by Tang, Gu, and Kukar Tang teaches: further comprising generating each of the vector layers using a subgraph of the graph, wherein the subgraph includes k consecutive layers of the graph [Tang 3 Circuit Cutting page 4]: “Figure 4 offers an illustrative example, where one cut separates a 5-qubit quantum circuit into 2 subcircuits of 3 qubits each [further comprising generating each of the vector layers using a subgraph of the graph, wherein the subgraph includes k consecutive layers of the graph where the subgraph contains k consecutive layers as the elements are noted to be in a DAG in [Tang 4.1 MIP Cut Searcher page 6], thus the elements can be considered consecutive as the elements are connected ] . Time goes from left to right in quantum circuit diagrams, and each row represents a qubit wire. CutQC performs vertical cuts on qubit wires, in other words, timewise cuts.” Regarding Claim 5: The method of claim 4 is taught by Tang, Gu, and Kukar Tang teaches: wherein the k consecutive layers includes at least one layer before a current layer of the graph and at least one layer after the current layer [Tang 3 Circuit Cutting page 4]: “Figure 4 offers an illustrative example, where one cut separates a 5-qubit quantum circuit into 2 subcircuits of 3 qubits each wherein the k consecutive layers includes at least one layer before a current layer of the graph and at least one layer after the current layer where the layer before and after can be indicated by the circuits containing connected qubits where 3 connected qubits allows for the qubit in the middle to have one in front and another qubit after, thus having layers with a layer before and after ] . Time goes from left to right in quantum circuit diagrams, and each row represents a qubit wire. CutQC performs vertical cuts on qubit wires, in other words, timewise cuts.” Regarding Claim 6: The method of claim 1 is taught by Tang, Gu, and Kukar Tang teaches: further comprising performing the cutting operation [Tang 3.3 Circuit Cutting: Challenges page 5]: “The first challenge is to find cut locations. While quantum circuits can always be split into smaller ones, finding optimal cut locations is crucial in order to minimize the classical postprocessing overhead. In general, large quantum circuits may require more than one cut [further comprising performing the cutting operation] in order to be separated into subcircuits.” Gu teaches: based on specific probabilities, wherein the specific probabilities include probabilities that are close to maximums and minimums, wherein the cutting locations correspond to the layers associated with probabilities that are close to maximums [Gu 4.1.1 page 9]: “In the solution phase, we select the one with the highest probability [based on specific probabilities, wherein the specific probabilities include probabilities that are close to maximums and minimums, wherein the cutting locations correspond to the layers associated with probabilities that are close to maximums where choosing the highest probability is seen as choosing the probability closest to the maximum, thus the teaching of choosing with the close to maximum probabilities using machine learning is taught ] in the output probability distribution and set it to 1, and the rest of the positions to 0. According to the result of Opredict, the pointer network selects the variables x1 and x3 with a value of 1, and the remaining variables have a value of 0—which is consistent with the result selected by Olabel” The motivation to combine with Gu is the same motivation to combine with Gu in claim 1. Kukar also teaches: based on specific probabilities, wherein the specific probabilities include probabilities that are close to maximums and minimums, wherein the cutting locations correspond to the layers associated with probabilities that are close to maximums The combination with Kukar also supports the idea of “ probabilities that are close to maximums ” as the requirement of being above a threshold can indicate choosing probabilities that would be smaller than an amount from the maximum possible probability. ([Kukar 3.4 Reliable and Unreliable Classifications page 8]: “Since the datasets used for training classifiers vary in their representativeness and noise levels as well as Machine Learning algorithms vary in strength and assumptions of their underlying models, it is hard to obtain absolute thresholds [based on specific probabilities, wherein the specific probabilities include probabilities that are close to maximums and minimums, wherein the cutting locations correspond to the layers associated with probabilities that are close to maximums] for reliable classifications. In our experiments they varied between 0.20 and 0.70 for different domains and Ma chine Learning algorithms.”) The motivation to combine with Kukar is the same as the motivation to combine with Kukar in claim 1. Regarding Claim 7: The method of claim 1 is taught by Tang, Gu, and Kukar Tang teaches: further comprising determining how many subcircuits to generate based on the cutting points [Tang 4.1 MIP Cut Searcher page 6]: “Choosing which edges to cut in order to split 𝐺 into subcircuits 𝐶 = 𝑐 1,.. ., 𝑐𝑛𝐶 [further comprising determining how many subcircuits to generate based on the cutting points] can also be thought of as clustering the vertices. The corresponding cuts can then obtained from the vertex clusters.” Regarding Claim 8: The method of claim 1 is taught by Tang, Gu, and Kukar Gu teaches: wherein a probability suggests a cutting point [Gu 4.1.1 page 9]: “In the solution phase, we select the one with the highest probability [wherein a probability suggests a cutting point where indication of being related to cuts is given by the problem discussed in Gu being MaxCut and the premise of cutting is taught by the primary reference Tang in claim 1 rejection ] in the output probability distribution and set it to 1, and the rest of the positions to 0. According to the result of Opredict, the pointer network selects the variables x1 and x3 with a value of 1, and the remaining variables have a value of 0—which is consistent with the result selected by Olabel” The labeling of 0 or 1 for a cut is supported by the specification indicating the training data uses a 0 or 1 labeling ([Current Specification 0034]: “The cutting points 554 for the DAGs 552 in the training dataset 550 are labeled with a probability of 1 and the other layers are labelled with a probability of 0.”) The motivation to combine with Gu is the same motivation to combine with Gu in claim 1, as the probability is seen as an extension of utilizing machine learning. Kukar teaches when the probability for the corresponding vector layer is above a threshold probability [Kukar 3.4 Reliable and Unreliable Classifications page 8]: “Since the datasets used for training classifiers vary in their representativeness and noise levels as well as Machine Learning algorithms vary in strength and assumptions of their underlying models, it is hard to obtain absolute thresholds [when the probability for the corresponding vector layer is above a threshold probability] for reliable classifications. In our experiments they varied between 0.20 and 0.70 for different domains and Ma chine Learning algorithms.” The motivation to combine with Kukar is the same as the motivation to combine with Kukar in claim 1. Regarding Claim 9: The method of claim 1 is taught by Tang, Gu, and Kukar Tang teaches: further comprising executing quantum subcircuits resulting from cutting the quantum circuit [Tang Introduction page 3]: “We develop the first end-to-end hybrid approach that automatically locates efficient cut positions to cut a large quantum circuit into smaller subcircuits that are each independently executed [further comprising executing quantum subcircuits resulting from cutting the quantum circuit] by using quantum devices with fewer qubits.” Regarding Claim 10: The method of claim 1 is taught by Tang, Gu, and Kukar Tang Teaches: further comprising determining whether the quantum circuit can be cut [Tang 3.3 Circuit Cutting: Challenges page 5]: “The first challenge is to find cut locations. While quantum circuits can always be split into smaller ones, finding optimal cut locations [further comprising determining whether the quantum circuit can be cut] is crucial in order to minimize the classical postprocessing overhead. In general, large quantum circuits may require more than one cut in order to be separated into subcircuits.” Gu teaches: based on the probabilities prior to performing the cutting operation [Gu 4.1.1 page 9]: “In the solution phase, we select the one with the highest probability [based on the probabilities prior to performing the cutting operation] in the output probability distribution and set it to 1, and the rest of the positions to 0. According to the result of Opredict, the pointer network selects the variables x1 and x3 with a value of 1, and the remaining variables have a value of 0—which is consistent with the result selected by Olabel” The motivation to combine with Gu is the same motivation to combine with Gu in claim 1. Regarding Claim 11: This claim is analogous to claim 1. Regarding Claim 12: This claim is analogous to claim 2. Regarding Claim 13: This claim is analogous to claim 3. Regarding Claim 14: This claim is analogous to claim 4. Regarding Claim 15: This claim is analogous to claim 5 Regarding Claim 16: This claim is analogous to claim 6 Regarding Claim 17: This claim is analogous to claim 7. Regarding Claim 18: This claim is analogous to claim 8. Regarding Claim 19: This claim is analogous to claim 9 Regarding Claim 20: This claim is analogous to claim 10. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Nazi et al (“A Deep Learning Framework for Graph Partitioning”) is relevant art that discusses the uses of machine learning for partitioning a graph in order to better optimize a graph for use on multiple devices. Merida-Casermeiro et al (“Graph Partitioning via Recurrent Multivalued Neural Networks”) is relevant art that notes the utilizing of recurrent models for partitioning graphs in methods such as MaxCut and MinCut. Merida-Casermeiro et al also notes that such problems are related to or useful for circuit layouts ([Merida-Casermeiro et al Introduction page 1]: “For MaxCut: pattern recognition, clustering, statistical physics and the de sign of communication networks, VLSI circuits and circuit layout”). Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT CHRISTOPHER D DEVORE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (703)756-1234 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday-Friday 7:30 am - 5 pm EST . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Michael J Huntley can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (303) 297-4307 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /C.D.D./ Examiner, Art Unit 2129 MACROBUTTON promptToSign *** /MICHAEL J HUNTLEY/ Supervisory Patent Examiner, Art Unit 2129