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 . Claims 1-20 are presented in the case.
Priority
Applicant's claim for the benefit of a prior-filed Provisional application 63/497,631 filed on 04/21/2023 is acknowledged.
Information Disclosure Statement
The information disclosure statements submitted on 11/14/2025 and 06/23/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Allowable Subject Matter
Claims 4 and 6 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 112
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 5, 7-10, 13-14 are 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.
Regarding claim 5, it recites the phrase “a quantum private multiplication” which appears to relate to “a quantum private multiplicative weights” as described in the specification. Is this a typo or is this math?
Regarding claims 7-10, it recites “a method of efficient updates”. The term “efficient” is a term of degree. Is there a certain memory, gate or error threshold?
Regarding claim 14, the phrase “the loss function” does not have an antecedent basis.
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 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 of this title, 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.
Claims 1, 12-14, 16-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Neven et al. (US 20200169396 A1) hereinafter Neven in view of Daraeizadeh et al. (US 20220199888 A1) hereinafter Daraeizadeh and Kerenidis et al. (US 20220391705 A1) hereinafter Kerenidis.
As to independent claim 1, Neven teaches a method for training a quantum machine learning model that is characterized by a set of model parameters, the method comprising:[training a quantum neural network (model) with states and operations ¶¶29-31]
accessing a set of qubits, wherein a set of initial quantum states of the set of qubits encodes a set of training data for the quantum machine learning model; [qubits are encoded for input (training example) ¶29 "for each training example: preparing, in an initial quantum state, multiple qubits of an input quantum neural network layer in an initial quantum state to encode the machine learning task input;"]
determining a set of final quantum states of the set of qubits based on a quantum logic circuit (QLC) operating on the set of qubits, [evolved qubit state (final) with logic gates, operation ¶¶11-12 and 18 "evolved state of the target qubit"…"each quantum neural network layer comprises multiple quantum logic gates that operate on the multiple qubits"] wherein the QLC includes a set of quantum logic gates and each quantum logic gate of the set of quantum logic gates performs a quantum operation on one or more qubits of the set of qubits and the quantum operation performed by a quantum logic gate of the set of quantum logic gates is characterized by a model parameter of the set of model parameters; [gates and gate parameters ¶¶29-31]
determining a value for each model parameter of the set of model parameters based on a gradient that is based on the updated offline model. [uses gradient decent values ¶¶31-35 " performing gradient descent to determine adjusted values of the gate parameters"]
Neven does not specifically teach initializing an offline model that corresponds to the QLC, updating the offline model based on the set of final quantum states of the set of qubits;
However, Daraeizadeh teaches initializing an offline model that corresponds to the QLC, updating the offline model based on the set of final quantum states of the set of qubits; [error uses to update simulated model on classic computer (offline) ¶¶85-86 "Using the simulated model 1010 and considering the error covariance between the actual measurements y from the sensors and the estimated measurements outputs 9 from the ML-based estimator 1030, new control parameters 1001 are predicted to minimize the cost function (i.e. maximize process fidelity)"], [system with model is calibrated offline and uses gates ¶94 " the quantum system is calibrated offline and the effect of cross-capacitance between all gates calculated and encoded in a crosstalk-matrix to generate virtual quantum operations which compensate for the crosstalk"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the modeling disclosed by Neven by incorporating the initializing an offline model that corresponds to the QLC, updating the offline model based on the set of final quantum states of the set of qubits disclosed by Daraeizadeh because both techniques address the same field of quantum computing and by incorporating Daraeizadeh into Neven enable models to better hit their desired results and thresholds for accurate models [Daraeizadeh ¶19, ¶50].
Neven and Daraeizadeh do not specifically teach wherein the offline model is characterized by the set of model parameters and is operable to predict an evolved set of quantum states of the set of qubits at each quantum logic gate of the set of quantum logic gates;
However, Kerenidis teaches wherein the offline model is characterized by the set of model parameters and is operable to predict an evolved set of quantum states of the set of qubits at each quantum logic gate of the set of quantum logic gates; [classic (offline) model with gates that calculate resulting evolved state ¶103-104, "quantum circuits can be simulated classically on a classical computing system" ¶122 " Applying a timestep includes applying the matrix w.sup.λ, which is made of all the BS gates aligned vertically at this timestep (w.sup.λ is the unitary in the unary basis). When a timestep is applied, the resulting state is a vector in the unary basis named inner layer and noted by ζ.sup.λ. This evolution can be written as ζ.sup.λ+1=w.sup.λ.Math.ζ.sup.λ"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the modeling disclosed by Neven and Daraeizadeh by incorporating the wherein the offline model is characterized by the set of model parameters and is operable to predict an evolved set of quantum states of the set of qubits at each quantum logic gate of the set of quantum logic gates disclosed by Kerenidis because both techniques address the same field of quantum computing and by incorporating Kerenidis into Neven and Daraeizadeh provide a more efficient and accurate training of quantum networks [Kerenidis ¶4].
As to dependent claim 12, the rejection of claim 1 is incorporated, Neven, Daraeizadeh and Kerenidis further teach wherein the offline model is a classical model. [Kerenidis simulated classically ¶103-104]
As to dependent claim 13, the rejection of claim 1 is incorporated, Neven, Daraeizadeh and Kerenidis further teach wherein each atom of the set of training data includes a ground-truth label. [Neven known classification (labels) ¶29]
As to dependent claim 14, the rejection of claim 13 is incorporated, Neven, Daraeizadeh and Kerenidis further teach wherein an evaluation of the loss function is based on the ground-truth label for at least a subset of the set of training data. [Neven loss function and known classifications ¶31, ¶35]
As to dependent claim 16, the rejection of claim 1 is incorporated, Neven, Daraeizadeh and Kerenidis further teach deploying the quantum machine learning model for a task based on the determined value for each model parameter. [Neven perform tasks at runtime and provides solutions ¶52, ¶64-69, ¶71-73]
As to dependent claim 17, the rejection of claim 16 is incorporated, Neven, Daraeizadeh and Kerenidis further teach wherein the task includes at least one of an image classification task, a textual sentiment task, an analysis of particle scattering data, a classification of quantum sensor data, a quantum state discrimination task, a prediction task, or a determination of time-time correlation functions. [Kerenidis Image classification (MNIST) ¶131-132], [Neven infer (prediction) ¶44]
As to independent claim 18, Neven teaches a quantum computing system (QCS), comprising: [quantum neural network architecture ¶51-53]
a set of qubits [qubits ¶54]
a quantum logic circuit (QLC); [logic ¶56]
one or more processor devices; [processor ¶59]
one or more memory devices, the one or more memory devices storing computer-readable instructions that when executed by the one or more processor devices cause the one or more processor devices to perform operations for training a quantum machine learning model that is characterized by a set of model parameters, the operations comprising: [programs, memory ¶75 and trained QNN (model) ¶50]
accessing a set of qubits, wherein a set of initial quantum states of the set of qubits encodes a set of training data for the quantum machine learning model; [qubits are encoded for input (training example) ¶29 "for each training example: preparing, in an initial quantum state, multiple qubits of an input quantum neural network layer in an initial quantum state to encode the machine learning task input;"]
determining a set of final quantum states of the set of qubits based on a quantum logic circuit (QLC) operating on the set of qubits, [evolved qubit state (final) with logic gates, operation ¶¶11-12 and 18 "evolved state of the target qubit"…"each quantum neural network layer comprises multiple quantum logic gates that operate on the multiple qubits"] wherein the QLC includes a set of quantum logic gates and each quantum logic gate of the set of quantum logic gates performs a quantum operation on one or more qubits of the set of qubits and the quantum operation performed by a quantum logic gate of the set of quantum logic gates is characterized by a model parameter of the set of model parameters; [gates and gate parameters ¶¶29-31]
determining a value for each model parameter of the set of model parameters based on a gradient that is based on the updated offline model. [uses gradient decent values ¶¶31-35 " performing gradient descent to determine adjusted values of the gate parameters"]
Neven does not specifically teach initializing an offline model that corresponds to the QLC, updating the offline model based on the set of final quantum states of the set of qubits;
However, Daraeizadeh teaches initializing an offline model that corresponds to the QLC, updating the offline model based on the set of final quantum states of the set of qubits; [error uses to update simulated model on classic computer (offline) ¶¶85-86 "Using the simulated model 1010 and considering the error covariance between the actual measurements y from the sensors and the estimated measurements outputs 9 from the ML-based estimator 1030, new control parameters 1001 are predicted to minimize the cost function (i.e. maximize process fidelity)"], [system with model is calibrated offline and uses gates ¶94 " the quantum system is calibrated offline and the effect of cross-capacitance between all gates calculated and encoded in a crosstalk-matrix to generate virtual quantum operations which compensate for the crosstalk"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the modeling disclosed by Neven by incorporating the initializing an offline model that corresponds to the QLC, updating the offline model based on the set of final quantum states of the set of qubits disclosed by Daraeizadeh because both techniques address the same field of quantum computing and by incorporating Daraeizadeh into Neven enable models to better hit their desired results and thresholds for accurate models [Daraeizadeh ¶19, ¶50].
Neven and Daraeizadeh do not specifically teach wherein the offline model is characterized by the set of model parameters and is operable to predict an evolved set of quantum states of the set of qubits at each quantum logic gate of the set of quantum logic gates;
However, Kerenidis teaches wherein the offline model is characterized by the set of model parameters and is operable to predict an evolved set of quantum states of the set of qubits at each quantum logic gate of the set of quantum logic gates; [classic (offline) model with gates that calculate resulting evolved state ¶103-104, "quantum circuits can be simulated classically on a classical computing system" ¶122 " Applying a timestep includes applying the matrix w.sup.λ, which is made of all the BS gates aligned vertically at this timestep (w.sup.λ is the unitary in the unary basis). When a timestep is applied, the resulting state is a vector in the unary basis named inner layer and noted by ζ.sup.λ. This evolution can be written as ζ.sup.λ+1=w.sup.λ.Math.ζ.sup.λ"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the modeling disclosed by Neven and Daraeizadeh by incorporating the wherein the offline model is characterized by the set of model parameters and is operable to predict an evolved set of quantum states of the set of qubits at each quantum logic gate of the set of quantum logic gates disclosed by Kerenidis because both techniques address the same field of quantum computing and by incorporating Kerenidis into Neven and Daraeizadeh provide a more efficient and accurate training of quantum networks [Kerenidis ¶4].
As to dependent claim 20, the rejection of claim 18 is incorporated, Neven, Daraeizadeh and Kerenidis further teach deploying the quantum machine learning model for a task based on the determined value for each model parameter, [Neven perform tasks at runtime and provides solutions ¶52, ¶64-69, ¶71-73] wherein the task includes at least one of an image classification task, a textual sentiment task, an analysis of particle scattering data, a classification of quantum sensor data, a quantum state discrimination task, a prediction task, or a determination of time-time correlation functions. [Kerenidis Image classification (MNIST) ¶131-132], [Neven infer (prediction) ¶44]
Claims 2-3 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Neven in view of Daraeizadeh and Kerenidis, as applied to the rejection of claim 1 and 18 above, and further in view of Parrish et al. (US 20200320240 A1) hereinafter Parrish.
As to dependent claim 2, the combination of Neven, Daraeizadeh and Kerenidis teach all the limitations of claim 1 that is incorporated.
Neven, Daraeizadeh and Kerenidis further teach wherein updating the offline model comprises: unitarily updating an adjoint state using a subset of the QLC to correspond to a component of the gradient; [Kerenidis reverse order backpropagation (updating) ¶126]
unitarily updating the offline model based on a definition of the QLC; and [Kerenidis updated by same gate ¶120]
Neven, Daraeizadeh and Kerenidis do not specifically teach executing a tomography algorithm based on the updated adjoint state to define an observable; and updating the offline model based on a result of the tomography algorithm.
However, Parrish teaches executing a tomography algorithm based on the updated adjoint state to define an observable; and [executes tomography formulae ¶24-27 and ¶32-35 "adapted to use the parametrization, tomography formulae, and sampling grids unique to the native gate set of the given quantum circuit."]
updating the offline model based on a result of the tomography algorithm. [Parrish gradient obtained through tomography formula ¶65]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Neven, Daraeizadeh and Kerenidis by incorporating the executing a tomography algorithm based on the updated adjoint state to define an observable; and updating the offline model based on a result of the tomography algorithm disclosed by Parrish because all techniques address the same field of quantum computing and by incorporating Parrish into Neven, Daraeizadeh and Kerenidis better maximizes the quality of solutions provided by quantum algorithms [Parrish ¶3].
As to dependent claim 3, the rejection of claim 2 is incorporated, Neven, Daraeizadeh, Kerenidis and Parrish further teach wherein the result of the tomography algorithm includes one or more components of the gradient. [Parrish gradient obtained thru tomography formula ¶65]
As to dependent claim 19, the combination of Neven, Daraeizadeh and Kerenidis teach all the limitations of claim 8 that is incorporated.
Neven, Daraeizadeh and Kerenidis further teach wherein updating the offline model comprises: unitarily updating an adjoint state using a subset of the QLC to correspond to a component of the gradient; [Kerenidis reverse order backpropagation (updating) ¶126]
unitarily updating the offline model based on a definition of the QLC; and [Kerenidis updated by same gate ¶120]
Neven, Daraeizadeh and Kerenidis do not specifically teach executing a tomography algorithm based on the updated adjoint state to define an observable; and updating the offline model based on a result of the tomography algorithm.
However, Parrish teaches executing a tomography algorithm based on the updated adjoint state to define an observable; and [executes tomography formulae ¶24-27 and ¶32-35 "adapted to use the parametrization, tomography formulae, and sampling grids unique to the native gate set of the given quantum circuit."]
updating the offline model based on a result of the tomography algorithm. [Parrish gradient obtained thru tomography formula ¶65]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Neven, Daraeizadeh and Kerenidis by incorporating the executing a tomography algorithm based on the updated adjoint state to define an observable; and updating the offline model based on a result of the tomography algorithm disclosed by Parrish because all techniques address the same field of quantum computing and by incorporating Parrish into Neven, Daraeizadeh and Kerenidis better maximizes the quality of solutions provided by quantum algorithms [Parrish ¶3].
Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Neven in view of Daraeizadeh and Kerenidis, as applied to the rejection of claim 1 above, and further in view of STOJEVIC et al. (US 20210398621 A1) hereinafter STOJEVIC.
As to dependent claim 7, the combination of Neven, Daraeizadeh and Kerenidis teach all the limitations of claim 1 that is incorporated.
Neven, Daraeizadeh and Kerenidis do not specifically teach wherein the offline model is an approximate model with a method of efficient updates.
However, STOJEVIC teaches wherein the offline model is an approximate model with a method of efficient updates. [efficient computable states that compute classically ¶390 "computable classically, every contraction in the tensor network has to be storable as an efficiently computable tensor network state"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Neven, Daraeizadeh and Kerenidis by incorporating the wherein the offline model is an approximate model with a method of efficient updates disclosed by STOJEVIC because all techniques address the same field of quantum computing and by incorporating STOJEVIC into Neven, Daraeizadeh and Kerenidis helps achieve the desired accuracy of quantum models in a more efficient way [STOJEVIC ¶8-9].
As to dependent claim 8, the rejection of claim 7 is incorporated, Neven, Daraeizadeh, Kerenidis and STOJEVIC further teach wherein the method of efficient updates includes a product state. [STOJEVIC matrix product states ¶104-108]
As to dependent claim 9, the rejection of claim 7 is incorporated, Neven, Daraeizadeh, Kerenidis and STOJEVIC further teach wherein the method of efficient updates includes a matrix product state. [STOJEVIC matrix product states ¶104-108]
As to dependent claim 10, the rejection of claim 7 is incorporated, Neven, Daraeizadeh, Kerenidis and STOJEVIC further teach wherein the method of efficient updates includes a tensor network state.
[STOJEVIC tensor network state ¶390 "computable classically, every contraction in the tensor network has to be storable as an efficiently computable tensor network state"]
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Neven in view of Daraeizadeh and Kerenidis, as applied to the rejection of claim 1 above, and further in view of Boixo Castrillo et al. (US 20230118636 A1) hereinafter Boixo Castrillo.
As to dependent claim 11, the combination of Neven, Daraeizadeh and Kerenidis teach all the limitations of claim 1 that is incorporated.
Neven, Daraeizadeh and Kerenidis do not specifically teach wherein the offline model is an exact model.
However, Boixo Castrillo teaches wherein the offline model is an exact model. [quantum simulation with exact inference and calculations ¶24 "comprising applying an algorithm for performing exact inference on graphical models."]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Neven, Daraeizadeh and Kerenidis by incorporating the wherein the offline model is an exact model disclosed by Boixo Castrillo because all techniques address the same field of quantum computing and by incorporating Boixo Castrillo into Neven, Daraeizadeh and Kerenidis provide a more efficient use of resources when simulating quantum circuits [Boixo Castrillo ¶39-40].
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Neven in view of Daraeizadeh and Kerenidis, as applied to the rejection of claim 1 above, and further in view of McClean (US 20220358394 A1).
As to dependent claim 15, the combination of Neven, Daraeizadeh and Kerenidis teach all the limitations of claim 1 that is incorporated.
Neven, Daraeizadeh and Kerenidis do not specifically teach wherein the QLC operating on the set of qubits includes the QLC operating on multiple sets of qubits that encode multiple copies of the set of training data.
However, McClean teaches wherein the QLC operating on the set of qubits includes the QLC operating on multiple sets of qubits that encode multiple copies of the set of training data. multiple copies ¶74-75, ¶87 "copies of the quantum state can be generated using quantum sensors"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify data modelling disclosed by Neven, Daraeizadeh and Kerenidis by incorporating the wherein the QLC operating on the set of qubits includes the QLC operating on multiple sets of qubits that encode multiple copies of the set of training data disclosed by McClean because all techniques address the same field of quantum computing and by incorporating McClean into Neven, Daraeizadeh and Kerenidis provides a more energy efficient versions of quantum circuitry and extract accurate information [McClean ¶47, ¶114].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Bocharov et al. (US 20140280427 A1) teaches running an offline mode before a quantum state exists (see ¶68).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST).
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, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388.
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/BEAU D SPRATT/ Primary Examiner, Art Unit 2143
