DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/26/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Prior Art Rejection Arguments
Applicant asserts:
Applicant argues, on page 5-7, that the prior art does not teach the amended claims
Examiner response:
Examiner respectfully disagrees. Applicant' s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1, 5, 9, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Woerner, Stefan et al; WO 2020064329 A1 (hereinafter “Woerner”) in view of Sima E. Borujeni et al; “Quantum circuit representation of Bayesian networks” filed Apr 12, 2021 (hereinafter “Borujeni”)
Regarding claim 1, Woerner teaches A method for establishing a circuit configuration for a data set to carry out a quantum computation on a quantum processor, the method comprising: (Woerner Page 22 Line 5; "Operation 804 depicts determining, by a classical computing device of the computing system ( e.g., by computing system 1000), parameters for the quantum computing circuit. These parameters can include, for example, a Y-rotation with a known angle for a qubit, and can be determined based on a value for an objective function that is determined by the quantum computing circuit in operation 802." Examiner notes that determining parameters for the quantum computing circuit is establishing a circuit configuration for a data set/a value for an objective function to carry out a quantum computation on a quantum processor/quantum computing circuit);
ingesting data values of the data set; (Woerner Page 25 Line 30; "Computing system 1000 comprises input 1002… Input 1002 can be an input of a stochastic control problem to be solved" Examiner notes that computing system ingests data values (input 1002) of dataset (stochastic control problem));
fitting the data values to a function f(i) wherein the function f(i) is a stochastic function; (Woerner Page 6 Line 18; "An exponentially-growing policy-scenario tree of a stochastic control problem can be efficiently represented through the quantum state of a logarithmically-smaller set of qubits (i.e., a two-state quantum-mechanical system), which can be prepared by a parameterized quantum computing circuit. The corresponding value of the considered objective function can be estimated by applying quantum amplitude estimation, and classical optimization techniques can be used to determine the optimal parameters." Woerner Page 26 Line 1; "and output 1016 can be an objective value or objective function that corresponds to the stochastic control problem." Examiner notes that the function (output 1016) is fitted to the data values; the objective function is referring to the stochastic control problem therefore it is a stochastic function);
and – converting, by encoding, the data values to qubits (qo-q3) to be processed by the quantum computer. (Woerner Page 9 Line 8; " Quantum computing circuit 100 can be utilized to represent decisions of a stochastic control problem over multiple time steps, as well as uncertainty, using qubits. Here, the first two qubits encode a decision (in an example that utilizes a binary tree), the second two qubits encode uncertainty, and the following two qubits encode a decision at a second time stamp." Examiner notes that data values/decisions are encoded into 4 qubits; 2 qubits for each decision; to be processed by the quantum computer/quantum computing circuit).
- wherein the data values are encoded by angle encoding of the qubits (o-3), (Woerner Page 9 Line 8; "Here, the first two qubits encode a decision (in an example that utilizes a binary tree), the second two qubits encode uncertainty, and the following two qubits encode a decision at a second time stamp." Woerner Page 10 Line 16; “the decision is used to encode {0, 1, 2, 3}. Do 150 includes Ro,o 102, and R1,o 104, which are each depicted as a controlled Y-rotation gate, 20 with rotation angle thetaij.” Examiner notes that data values/decisions are encoded into 4 qubits; 2 qubits for each decision; Y-axis rotation is used in angle encoding);
and wherein the angle encoding are applied to the ancilla qubit (Examiner refers to previous mapping to show that angle encoding (Y-axis rotation) is applied to ancilla qubits to encode decisions)
Woerner does not teach- the method further comprising integrating by applying the qubits to an ancillary qubit (q4),
[and wherein the angle encoding are applied to the ancilla qubit] and depend on corresponding probability values assigned to the qubits (q0-q3) in previous probability distribution integration.
However, Borujeni does teach - the method further comprising integrating by applying the qubits to an ancillary qubit (q4), (Borujeni Page 17 Paragraph 2; “Let n represent the number of parent nodes for a child node, Vi. The conditional probabilities of Vi can be stated using CnRY gate where the n control qubits are the n qubits corresponding to the n parent nodes and the target qubit represents the child node. For the child node C in Figure 4, n=2, and therefore, we used a CCRY or C2RY gate to show the conditional probabilities of C in Figure 5. CnRY is not an elementary gate and will need to be decomposed into elementary gates. One of the techniques to build the CnRY is the use of additional “dummy” qubits known as ancilla qubits” Examiner notes that the qubits (control qubits and target qubits) are applied to an ancillary qubit (techniques to build the CnRY is the use of additional “dummy” qubits known as ancilla qubits”))
[and wherein the angle encoding are applied to the ancilla qubit] and depend on corresponding probability values assigned to the qubits (q0-q3) in previous probability distribution integration. (Borujeni Page 20 Paragraph 1; “Our approach is to decompose the probability distribution defined over mi qubits into a combinations of marginal and conditional distributions that can be implemented using single-qubit and controlled-rotations.” Examiner refers to previous mapping and cited paragraph to show that ancilla qubit depend on probability values (ancilla qubits are used to build CNRY to show conditional probabilities) assigned to the qubits (control qubits and target qubits) in previous probability distributions integration (probability distribution defined over mi qubits))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Woerner and Borujeni. Woerner teaches techniques for facilitating utilizing a quantum computing circuit in con/junction with a stochastic control problem and a classical computing device that determines parameters for the quantum computing circuit. Borujeni teaches a method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. One of ordinary skill would have motivation to combine Woerner and Borujeni to improve on computational of quantum computing applications “quantum computing applications man aging to exploit the advantages of amplitude amplification have shown significant computational benefits when compared against their classical counterparts.” (Borujeni Abstract).
Regarding claim 5, Woerner teaches The method of claim 1, wherein the converting of the data values comprising applying Y-rotations to one or more qubits to represent the data values. (Woerner Page 9 Line 16; "Y-rotation can be applied to qubits (to give the qubits a rotation angle thetai.sub.j) so that the qubits can be prepared to have an even probability between 0, 1, 2, and 3, or so that there is a specific probability of those various values occurring." Examiner notes that Y-rotations are applied to represent the various values/data values as probabilities)
Regarding claim 9, Woerner teaches A system for establishing a circuit configuration for a data set to carry out a quantum computation on a quantum processor, the system comprising: (Woerner Page 22 Line 5; "Operation 804 depicts determining, by a classical computing device of the computing system ( e.g., by computing system 1000), parameters for the quantum computing circuit. These parameters can include, for example, a Y-rotation with a known angle for a qubit, and can be determined based on a value for an objective function that is determined by the quantum computing circuit in operation 802." Examiner notes that determining parameters for the quantum computing circuit is establishing a circuit configuration for a data set/a value for an objective function to carry out a quantum computation on a quantum processor/quantum computing circuit);
a plurality of input/output devices for reading data values of the data set; (Woerner Page 28 Line 16; " A user enters commands or information into the computer 1112 through input device(s) 1136... Output device(s) 1140 use some of the same type of ports as input device(s) 1136. Thus, for example, a USB port can be used to provide 25 input to computer 1112, and to output information from computer 1112 to an output device 1140.");
a central processing unit for fitting the data values to a function f(i) and encoding the data values to qubits; (Woerner Page 22 Line 20; "a computing device ( or system) ( e.g., computing system 1000) is provided comprising one or more processors and one or more memories that stores executable instructions that, when executed by the one or more processors, can facilitate performance of the operations as described herein" Woerner Page 6 Line 18; "An exponentially-growing policy-scenario tree of a stochastic control problem can be efficiently represented through the quantum state of a logarithmically-smaller set of qubits (i.e., a two-state quantum-mechanical system), which can be prepared by a parameterized quantum computing circuit. The corresponding value of the considered objective function can be estimated by applying quantum amplitude estimation, and classical optimization techniques can be used to determine the optimal parameters." Woerner Page 9 Line 8; "Here, the first two qubits encode a decision (in an example that utilizes a binary tree), the second two qubits encode uncertainty, and the following two qubits encode a decision at a second time stamp." Examiner notes that determining the optimal parameters of objective function is fitting the data values/corresponding value to a function f(i)/objective function; data values/decisions are encoded into 4 qubits; 2 qubits for each decision; note: this limitation appears to be an intended use and does not hold any patentable weight. For compact prosecution, the limitation has been addressed by the art);
a quantum processor for processing the qubits; (Woerner Page 25 Line 19; "Figure 10 illustrates a block diagram of an example, non-limiting quantum computing system for implementing stochastic control in accordance with one or more embodiments described herein… In some examples, computing system 1000 can be used to implement aspects of quantum computing circuit 100, quantum computing circuit 200, quantum computing circuit 300, quantum computing circuit 400, or quantum computing circuit 500." Examiner notes that quantum computing system indicates a quantum processor is present for processing the qubits);
and a read-in/read out element for passing the data values for processing to the quantum processor and reading out a result. (Woerner Page 15 Line 30; "Computing system 1000 comprises input 1002, stochastic-control control logic 1004, driver 1006a, driver 1006b, driver 1006c, qubit 1008a, qubit 1008b, coupler 1010, sensor 1012a, sensor 1012b, stochastic-control readout control logic 1014, and output 1016." Examiner notes that input 1002 and output 1016 are read-in/read out element for passing the data values for processing to the quantum processor and reading out a result)
wherein the system is adapted to encode the data values by angle encoding of the qubits (o-g3), (Woerner Page 9 Line 8; "Here, the first two qubits encode a decision (in an example that utilizes a binary tree), the second two qubits encode uncertainty, and the following two qubits encode a decision at a second time stamp." Woerner Page 10 Line 16; “the decision is used to encode {0, 1, 2, 3}. Do 150 includes Ro,o 102, and R1,o 104, which are each depicted as a controlled Y-rotation gate, 20 with rotation angle thetaij.” Examiner notes that data values/decisions are encoded into 4 qubits; 2 qubits for each decision; Y-axis rotation is used in angle encoding)
and wherein the angle encoding is applied to the ancilla qubit and (Examiner refers to previous mapping to show that angle encoding (Y-axis rotation) is applied to ancilla qubits to encode decisions)
Woerner does not teach and to probability distribution by applying the qubits to an ancillary qubit (q4),
depend on corresponding probability values assigned to the qubits (q0-q3) in previous probability distribution integration.
However, Borujeni does teach and to probability distribution by applying the qubits to an ancillary qubit (q4), (Borujeni Page 17 Paragraph 2; “Let n represent the number of parent nodes for a child node, Vi. The conditional probabilities of Vi can be stated using CnRY gate where the n control qubits are the n qubits corresponding to the n parent nodes and the target qubit represents the child node. For the child node C in Figure 4, n=2, and therefore, we used a CCRY or C2RY gate to show the conditional probabilities of C in Figure 5. CnRY is not an elementary gate and will need to be decomposed into elementary gates. One of the techniques to build the CnRY is the use of additional “dummy” qubits known as ancilla qubits” Examiner notes that the qubits (control qubits and target qubits) are applied to an ancillary qubit (techniques to build the CnRY is the use of additional “dummy” qubits known as ancilla qubits”))
depend on corresponding probability values assigned to the qubits (q0-q3) in previous probability distribution integration. (Borujeni Page 20 Paragraph 1; “Our approach is to decompose the probability distribution defined over mi qubits into a combinations of marginal and conditional distributions that can be implemented using single-qubit and controlled-rotations.” Examiner refers to previous mapping and cited paragraph to show that ancilla qubit depend on probability values (ancilla qubits are used to build CNRY to show conditional probabilities) assigned to the qubits (control qubits and target qubits) in previous probability distributions integration (probability distribution defined over mi qubits))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Woerner and Borujeni. Woerner teaches techniques for facilitating utilizing a quantum computing circuit in con/junction with a stochastic control problem and a classical computing device that determines parameters for the quantum computing circuit. Borujeni teaches a method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. One of ordinary skill would have motivation to combine Woerner and Borujeni to improve on computational of quantum computing applications “quantum computing applications man aging to exploit the advantages of amplitude amplification have shown significant computational benefits when compared against their classical counterparts.” (Borujeni Abstract).
Regarding claim 10, Woerner teaches The system of claim 9, wherein the data values are encoded by angle encoding of the qubits (qo-q3). (Woerner Page 9 Line 8; "Here, the first two qubits encode a decision (in an example that utilizes a binary tree), the second two qubits encode uncertainty, and the following two qubits encode a decision at a second time stamp." Woerner Page 10 Line 16; “the decision is used to encode {0, 1, 2, 3}. Do 150 includes Ro,o 102, and R1,o 104, which are each depicted as a controlled Y-rotation gate, 20 with rotation angle thetaij.” Examiner notes that data values/decisions are encoded into 4 qubits; 2 qubits for each decision; Y-axis rotation is used in angle encoding)
Regarding claim 11, Woerner teaches A method for establishing a circuit configuration for a data set to carry out a quantum computation on a quantum processor, the method comprising: - ingesting, by a classical processor, data values of the data set; (Woerner Page 22 Line 20; "a computing device ( or system) ( e.g., computing system 1000) is provided comprising one or more processors and one or more memories that stores executable instructions that, when executed by the one or more processors, can facilitate performance of the operations as described herein" Woerner Page 25 Line 33; "Computing system 1000 comprises input 1002…Input 1002 can be an input of a stochastic control problem to be solved" Examiner notes that data values of the dataset (input of a stochastic control problem) is ingested by a classical processor (computing system))
- fitting, by the classical processor, the data values to a function f(i) wherein the function f(i) is a stochastic function; (Woerner Page 6 Line 18; "An exponentially-growing policy-scenario tree of a stochastic control problem can be efficiently represented through the quantum state of a logarithmically-smaller set of qubits (i.e., a two-state quantum-mechanical system), which can be prepared by a parameterized quantum computing circuit. The corresponding value of the considered objective function can be estimated by applying quantum amplitude estimation, and classical optimization techniques can be used to determine the optimal parameters." Woerner Page 26 Line 1; "and output 1016 can be an objective value or objective function that corresponds to the stochastic control problem." Examiner notes that classical processor (computing system) ingests data values (input 1002) of dataset (stochastic control problem) and the function (output 1016) is fitted to the data values; the objective function is referring to the stochastic control problem therefore it is a stochastic function)
- encoding, by the classical processor, the data values as quantum gates; (Woerner Page 22 Paragraph 5; " Operation 804 depicts determining, by a classical computing device of the computing system ( e.g., by computing system 1000), parameters for the quantum computing circuit. These parameters can include, for example, a Y-rotation with a known angle for a qubit, and can be determined based on a value for an objective function that is determined by the quantum computing circuit in operation 802." Examiner notes that the data values (value for an objective function) is encoded/determined as quantum gates (Y-rotation) by the classical processor (by a classical computing device))
and - converting, by the quantum processor, the data values to qubits (qo-q3) to be processed by the quantum processor, (Woerner Page 9 Line 8; " Quantum computing circuit 100 can be utilized to represent decisions of a stochastic control problem over multiple time steps, as well as uncertainty, using qubits. Here, the first two qubits encode a decision (in an example that utilizes a binary tree), the second two qubits encode uncertainty, and the following two qubits encode a decision at a second time stamp." Examiner notes that data values/decisions are encoded into 4 qubits; 2 qubits for each decision; to be processed by the quantum computer/quantum computing circuit)
said converting comprising applying the quantum gates to the qubits, wherein the data values are encoded by angle encoding of the qubits (q1-q3), (Woerner Page 9 Line 16; "Y-rotation can be applied to qubits (to give the qubits a rotation angle thetai.sub.j) so that the qubits can be prepared to have an even probability between 0, 1, 2, and 3, or so that there is a specific probability of those various values occurring." Woerner Page 10 Line 1; "Quantum computing circuit 100 can consist of an initialization step that applies an initial uncontrolled Y-rotation with angle" Examiner notes that said converting comprises applying the quantum gates (y-rotation) to the qubits by the quantum processor (quantum computing circuit))
and wherein the angle encoding are applied to the ancilla qubit (Examiner refers to previous mapping to show that angle encoding (Y-axis rotation) is applied to ancilla qubits to encode decisions)
Woerner does not teach the method further comprising integrating by applying the qubits to an ancillary qubit (q4),
and depend on corresponding probability values assigned to the qubits (q0-g3) in previous probability distribution integration-However, Borujeni does teach - the method further comprising integrating by applying the qubits to an ancillary qubit (q4), (Borujeni Page 17 Paragraph 2; “Let n represent the number of parent nodes for a child node, Vi. The conditional probabilities of Vi can be stated using CnRY gate where the n control qubits are the n qubits corresponding to the n parent nodes and the target qubit represents the child node. For the child node C in Figure 4, n=2, and therefore, we used a CCRY or C2RY gate to show the conditional probabilities of C in Figure 5. CnRY is not an elementary gate and will need to be decomposed into elementary gates. One of the techniques to build the CnRY is the use of additional “dummy” qubits known as ancilla qubits” Examiner notes that the qubits (control qubits and target qubits) are applied to an ancillary qubit (techniques to build the CnRY is the use of additional “dummy” qubits known as ancilla qubits”))
and depend on corresponding probability values assigned to the qubits (q0-g3) in previous probability distribution integration- (Borujeni Page 20 Paragraph 1; “Our approach is to decompose the probability distribution defined over mi qubits into a combinations of marginal and conditional distributions that can be implemented using single-qubit and controlled-rotations.” Examiner refers to previous mapping and cited paragraph to show that ancilla qubit depend on probability values (ancilla qubits are used to build CNRY to show conditional probabilities) assigned to the qubits (control qubits and target qubits) in previous probability distributions integration (probability distribution defined over mi qubits))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Woerner and Borujeni. Woerner teaches techniques for facilitating utilizing a quantum computing circuit in con/junction with a stochastic control problem and a classical computing device that determines parameters for the quantum computing circuit. Borujeni teaches a method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. One of ordinary skill would have motivation to combine Woerner and Borujeni to improve on computational of quantum computing applications “quantum computing applications man aging to exploit the advantages of amplitude amplification have shown significant computational benefits when compared against their classical counterparts.” (Borujeni Abstract).
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Woerner, Stefan et al; WO 2020064329 A1 (hereinafter “Woerner”) in view of Sima E. Borujeni et al; “Quantum circuit representation of Bayesian networks” filed Apr 12, 2021 (hereinafter “Borujeni”) in view of Dailey; Aaron et al; US 12340110 B1 (hereinafter “Dailey”).
Regarding claim 3, Woerner does not teach The method of claim 1, further comprising cleaning the data values prior to fitting. However, Dailey does teach The method of claim 1, further comprising cleaning the data values prior to fitting. (Dailey Column 42 Line 58; "cleaning and transforming the data in a format convenient for training" Examiner notes that training is fitting)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Woerner, Borujeni, and Dailey. Woerner teaches techniques for facilitating utilizing a quantum computing circuit in con/junction with a stochastic control problem and a classical computing device that determines parameters for the quantum computing circuit. Borujeni teaches a method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. Dailey teaches preprocessing data before applying it for a purpose. One of ordinary skill would have motivation to combine Woerner, Borujeni, and Dailey to preprocess the data values before fitting it to the objective function “Rarely is the ingested data used for only one purpose, and shared storage gives the flexibility to train multiple different models or apply traditional analytics to the data.” (Dailey Colum 43 Line 10).
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Woerner, Stefan et al; WO 2020064329 A1 (hereinafter “Woerner”) in view of Sima E. Borujeni et al; “Quantum circuit representation of Bayesian networks” filed Apr 12, 2021 (hereinafter “Borujeni”) in view of Baughman; Aaron K. et al; US 20230123240 A1 (hereinafter “Baughman”).
Regarding claim 4, Woerner does not teach The method of claim 1, further comprising normalizing the data values. However, Baughman does teach The method of claim 1, further comprising normalizing the data values. (Baughman Line 0040; "This may be accomplished by first pre-processing the data by normalizing each data point to the interval [0, 2π]. ")
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Woerner, Borujeni, and Baughman. Woerner teaches techniques for facilitating utilizing a quantum computing circuit in con/junction with a stochastic control problem and a classical computing device that determines parameters for the quantum computing circuit. Borujeni teaches a method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. Baughman teaches encoding techniques for data clustering. One of ordinary skill would have motivation to combine Woerner, Borujeni, and Baughman to efficiently process and represent data values for a stochastic control problem “This further improves the efficiency with which a computer system encodes, clusters, and structures data.” (Baughman Paragraph 0029).
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Woerner, Stefan et al; WO 2020064329 A1 (hereinafter “Woerner”) in view of Sima E. Borujeni et al; “Quantum circuit representation of Bayesian networks” filed Apr 12, 2021 (hereinafter “Borujeni”) in view of Wiebe; Nathan; US 20170194930 A1 (hereinafter “Wiebe”).
Regarding claim 7, Woerner does not teach The method of claim 1, further comprising reading out a value from the ancillary qubit. However, Wiebe does teach The method of claim 6, further comprising reading out a value from the ancillary qubit. (Wiebe Paragraph 0027; "States of these ancilla qubits are measured to determine method/circuit success." Examiner nots that measuring the ancilla qubit is reading out a value)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Woerner, Borujeni, and Wiebe. Woerner teaches techniques for facilitating utilizing a quantum computing circuit in con/junction with a stochastic control problem and a classical computing device that determines parameters for the quantum computing circuit. Borujeni teaches a method for designing a quantum circuit to represent a generic discrete Bayesian network with nodes that may have two or more states, where nodes with more than two states are mapped to multiple qubits. Wiebe teaches using an ancilla qubit for mathematical analyses. One of ordinary skill would have motivation to combine Woerner, Borujeni, and Weibe to utilize ancilla qubits to help create a best fit quantum circuit “Particular designs can be selected based on a preferred error limit, to reduce a total number of qubits, or based on other considerations.” (Wiebe Paragraph 0048).
Conclusion
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/D.D.T./Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
