Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Detailed Action
2. This second Non-Final Office Action is responsive to Applicants’ arguments as received 9/10/25. Claims 1-18 remain pending, of which claim 1 is independent.
Claim Rejections - 35 USC § 103
3. 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.
4. 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.
5. 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.
6. Claims 1-13 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2023/0044102 (“Anderson”, previously-cited by the Examiner but only now relied upon) in view of Non-Patent Literature “Quantum computing with neutral atoms” (“Henriet”, previously-cited and relied-upon).
Regarding claim 1, ANDERSON teaches A method (the programmatic flow of FIG. 7 to refine trust scores for models in an ensemble so as to optimize the model weights, as performed using a framework as shown per FIG. 9) including the following steps:
setting, by one or more processing devices (FIG. 8’s element 810, as would be situated in the classical computer shown in FIG. 9), a plurality of classifiers for classification of data in two or more classes (the models in the ensemble are classifiers, per [0029], [0037], and [0046] for example, which may be directed to multiclass classification as mentioned per [0037]), each of the two or more classes being associated to a numeric value (classifier output, i.e., a classification result / class as identified, can assume a number as indicated per y as discussed with respect to [0087]-[0088] and [0075] for example), and each classifier of the plurality of classifiers being associated to one or more weighting factors (classifiers are weighted, as discussed per [0091]-[0093]);
defining, by one or more processing devices, a cost function and optimizing, by one or more processing devices and a quantum circuit, the one or more weighting factors associated to each classifier of the plurality of classifiers by minimizing the defined cost function ... ([0098]: “loss-based error function” for use in training the ensemble model and each model therein, which the Examiner equates with the recited “cost function”, and where the errors for each individual model is used to determine relative weights of the models in the ensemble, where the determination of the weights as mentioned is solvable on a quantum computing platform as shown per FIG. 7 and as discussed per [0098] (i.e., using the equivalent of the recited “quantum circuit”), and where the Examiner understands that a minimization is sought for the error function / loss as discussed per [0013] and [0087], such that optimal weights are determinable through the training and the use of error/loss considerations (see, e.g., [0091]-[0097])).
Subject to the defined cost function as minimized, Applicants’ claim further requires additional limitations also taught by Anderson for digitally storing at least a result of the cost function as last computed (the step in FIG. 7 to “calculate error for model fi(x) over training/testing data set”, which is understood to be performed iteratively by a classical computer and hence subject to digital management of the calculation including for example storing the result value at least temporarily) and setting, by one or more processing devices, a boosted classifier based on the optimized weighting factors (Anderson’s [0071]-[0083] generally discussing the particular approach is a boosting approach (specifically: [0071], [0073], [0075])).
Applicants’ claim further clarifies the minimizing of the defined cost function to entail further limitations ... as follows (which the Examiner believes Anderson does not sufficiently):
radiating a vacuum chamber comprising an ensemble of neutral atoms with a laser so as to trap atoms of the ensemble of neutral atoms in an array of optical tweezers, thereby providing a quantum register, and each optical tweezer comprising a single neutral atom;
digitally configuring at least one laser parameter for implementing one or more unitary operations, wherein the one or more unitary operations are dependent at least upon the at least one laser parameter;
radiating the ensemble of atoms with laser light so as to excite at least some atoms of the quantum register, with a laser being operated in accordance with the at least one laser parameter to implement one or more unitary operations in the quantum circuit;
reading the quantum register with optical means, thereby obtaining a string of bits based on an amount of light produced by the atoms, thus mapping binary values of the weighting factors of the classifiers to a state of qubits in the quantum register;
using the string of bits to compute the cost function;
updating, for reducing the cost function, the at least one laser parameter of the laser by an optimization algorithm until the weighting factors are optimized.
Anderson clearly teaches the use of a quantum computing platform to facilitate optimization of model weighting to train the larger ensemble. See, e.g., FIG. 7 and its related discussion beginning at [0098]. That said, Anderson does not go into the technical detail of how the quantum computing platform functions, e.g. as further claimed and as reproduced just above. Rather, the Examiner relies upon HENRIET to teach what Anderson otherwise lacks, see e.g., portions of Henriet as detailed just below:
Regarding the further limitation for “radiating a vacuum chamber comprising an ensemble of neutral atoms with a laser so as to trap atoms of the ensemble of neutral atoms in an array of optical tweezers, thereby providing a quantum register, and each optical tweezer comprising a single neutral atom”, see Henriet: page 6, section 2.1.1 teaching quantum register preparation involving the forming of a dilute atomic vapor is formed inside an ultra-high vacuum system where neural atoms are prepared, and where a first laser system and a second laser system are used to isolate atoms inside the ensemble, such that optical tweezers are used to essentially do the isolating, where the isolating per tweezer is “at most one single atom at a time.”
Regarding the further limitation for “digitally configuring at least one laser parameter for implementing one or more unitary operations, wherein the one or more unitary operations are dependent at least upon the at least one laser parameter”, see Henriet: page 10 discussing “... In the Pasqal device the laser is an optical laser field driving Raman transitions through an intermediate atomic state. The atom-laser interaction is characterized by the Rabi frequency ... the detuning ... and their relative phase ... Hence, any single-qubit gate can be implemented by tuning the pulse duration, the laser intensity, the detuning, and the phase of the laser ...”, and where pages 9 and 11 both discuss the use of these features in relation to performing unitary transformations, and Box 3 on pages 18-19 mentions the hybrid aspect that involves classical computing/processing apart from quantum processing as solely used to prepare the quantum state, and where the looping until stopping criterion is fulfilled or optimization converges is understood to be driven via the classical element and not the quantum element and that the quantum element is merely used for the function portion of the register and preparing and manipulating the register based on parameterization more associated with the classical element.
Regarding the further limitation for “radiating the ensemble of atoms with laser light so as to excite at least some atoms of the quantum register, with a laser being operated in accordance with the at least one laser parameter to implement one or more unitary operations in the quantum circuit”, see Henriet: pages 5-6, section 2.1, discussing register preparation, quantum processing, and register readout, where the parameterization involved in these aspects is associated with parameterization as discussed per page 10 (e.g., “The atom-laser interaction is characterized by the Rabi frequency ... the detuning ... and their relative phase ... Hence, any single-qubit gate can be implemented by tuning the pulse duration, the laser intensity, the detuning, and the phase of the laser.”).
Regarding the further limitation for “reading the quantum register with optical means, thereby obtaining a string of bits based on an amount of light produced by the atoms, thus mapping binary values of the weighting factors of the classifiers to a state of qubits in the quantum register”, see Henriet: section 2.1.2 discussing register readout, which involves fluorescence image capture of the atomic register, which the Examiner believes is incorporable into Anderson’s framework for quantum machine learning using a quantum ensemble of classifiers, where as discussed Anderson’s ensemble is defined by per-classifier weight which is determinable through a training / cost minimization process. See, e.g., Henriet’s discussion of machine learning tasks, and specifically classification as an example quantum application, as found on page 25 under the title “Machine Learning tasks.”
Regarding the further limitation for “using the string of bits to compute the cost function”, see Henriet and Anderson as discussed just above, where the quantum computing results, e.g. the register readout, drives the result-based processing of minimizing/reducing cost per Anderson’s classifier for example.
Regarding the further limitation for “updating, for reducing the cost function, the at least one laser parameter of the laser by an optimization algorithm until the weighting factors are optimized”, see Henriet: page 10 discussing “... In the Pasqal device the laser is an optical laser field driving Raman transitions through an intermediate atomic state. The atom-laser interaction is characterized by the Rabi frequency ... the detuning ... and their relative phase ... Hence, any single-qubit gate can be implemented by tuning the pulse duration, the laser intensity, the detuning, and the phase of the laser ...”
Anderson and Henriet both relate to machine learning tasks based on a quantum computing paradigm, and are amenable to informing how quantum computing might be used to perform classification. Hence, they are similarly directed and therefore analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement machine-learning and specifically classification training aspects using a quantum computing aspect/framework, as both Anderson and Henriet generally contemplate, and all the ways it can be parameterized and tuned per Henriet specifically, with a reasonable expectation of success, such as to realize the sought advantages expressed in Anderson’s [0030] and [0041] for example in situations such as those discussed per Anderson’s [0038].
Regarding claim 2, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising solving, by the one or more processing devices setting the boosted classifier, a problem requiring classification of datapoints in a dataset in the two or more classes using the boosted classifier, the problem defining either a configuration or operation of an apparatus or system, or behaviour of a process (a classifier per Anderson, e.g., as defined via the taught quantum-based training approach detailed above in relation to claim 1 for example, where the classifier may be generally used to classify data, per [0029], [0037], and [0046] for example (e.g., multiclass classification per [0037] for example), and is amenable to implementations such as those discussed in [0037] for example (e.g., image classification), and where the classification is understood to be boosted – see e.g., Anderson’s [0071]-[0083] generally discussing the particular approach is a boosting approach (and more specifically: [0071], [0073], [0075])). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 3, Anderson in view of Henriet teaches the method of claim 2, as discussed above. The aforementioned references further teach the additional limitations further comprising determining based on the solution to the problem, by one or more processing devices, at least one of the following:
whether a potential anomaly exists in the operation of the apparatus or the system, or in the behaviour of the process; and
a configuration of the apparatus or the system intended to improve the operation and/or solve the potential anomaly thereof, or a configuration of any apparatus or system in the process intended to improve the behaviour and/or solve the potential anomaly of the process (see the Examiner’s rationale provided per claim 2, such that a classifier per Anderson as improved by way of training via quantum computing in a manner more granularly taught by Henriet especially in view of Henriet’s discussion of machine learning tasks on its page 25).
The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 4, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising wherein the stage of updating, for reducing the cost function, the at least one laser parameter of the laser by an optimization algorithm until the weighting factors are optimized, includes the following steps:
i) digitally reconfiguring the at least one laser parameter for reducing the cost function; and
ii) radiating the ensemble of atoms with laser so as to excite at least some atoms of the quantum register, with the laser being operated in accordance with the at least one laser parameter as last reconfigured; and
iii) reading the quantum register with optical means after the last irradiation of the ensemble of atoms, and digitally defining the string of bits based on the quantum register as last read; and
iv) using the string of bits as last defined, digitally calculating a result of the cost function; and
v) digitally processing the result of the cost function as last calculated, and digitally providing a convergence factor based on both said result and the result as last stored; and
vi) if the convergence factor does not fulfil a predetermined criterion, radiating the ensemble of atoms with laser so as to reinitialize a state of the qubits in the quantum register and repeating steps i) to v); and
vii) if the convergence factor fulfills the predetermined criterion, digitally setting the boosted classifier with the values of the last modified weighting factors.
Respectfully, the limitations here are a sort of reiteration of the features and limitations as already discussed above in relation to claim 1. Moreover, the limitations of the instant claim are merely an iterative framework of the same features/limitations which are arranged in a manner to iterate until cost/error is reduced and/or convergence is realized, as is widely known and understood in the state of the art pertaining to machine learning models and their training. For example, please see Henriet’s pages 18-19, Box 3 and Figure B3.1, which provide articulation to some of these same principles discussed here by the Examiner as relevant to Applicants’ claim and as taught by Henriet. To the extent that iterative training/tuning is performed by Henriet, the Examiner believes it is reasonable to understand that to encompass the tuning by way of modifying laser parameters, see e.g., Henriet: pages 5-6, section 2.1, discussing register preparation, quantum processing, and register readout, where the parameterization involved in these aspects is associated with parameterization as discussed per page 10 (e.g., “The atom-laser interaction is characterized by the Rabi frequency ... the detuning ... and their relative phase ... Hence, any single-qubit gate can be implemented by tuning the pulse duration, the laser intensity, the detuning, and the phase of the laser.”
The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 5, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising wherein the stage of setting a plurality of classifiers for classification of data in two or more classes, comprises training, by one or more processing devices, the plurality of classifiers by inputting a first dataset to the plurality of classifiers (“training data set” as mentioned with respect to Anderson’s FIG. 7, [0036]-[0038] and [0062] for example, where the classification is understood to be inclusive of multiclass classification as mentioned per [0037] for example) and reducing a second cost function associated with the plurality of classifiers, the plurality of classifiers classifying each datapoint of the first dataset in two or more classes (Anderson’s [0011] sum of loss-based penalty functions, e.g. understood to encompass a loss for all the classifiers in the ensemble). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 6, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising wherein the defined cost function at least comprises an error function with the error of A relative to B, where:
A is F({right arrow over (xj)})=≡Σiαifi({right arrow over (xj)}), where {right arrow over (xj)} is a j-th datapoint of a first dataset (201), fi({right arrow over (xj)}) is a classification of the j-th datapoint by i-th classifier of the plurality of classifiers, and αi is one or more weighting factors of the one or more weighting factors associated to the i-th classifier; and B is an actual class of the j-th datapoint; and the error function being for all datapoints of the first dataset or a subset of the first dataset (the error equation provided in Anderson’s [0009], [0075], and [0083]). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 7, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising wherein the stage of mapping the binary values of the weighting factors of the classifiers to the state of the qubits in the quantum register is done using a fluorescence image of the atoms (Henriet; pages 6-7, section 2.1.1, teaching “... To detect which of the tweezers are filled, the atoms are imaged by collecting their fluorescence onto a sensitive camera ...”). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 8, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising wherein the computation of the cost function is done digitally (Henriet: on page 19, Figure B3.1’s caption: “... These algorithms are composed of both a quantum and a classical processor that exchange information within a feedback loop. The quantum processor is used to prepare and measure a n-qubit parameterized quantum state. The outcome of the measurement is then used as the objective function in a standard classical optimization procedure, that updates the parameter for the next iteration. ...”). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 9, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising wherein the stage of radiating with laser causes an evolution of the state of the qubits, the evolution depending on a time-dependent Hamiltonian having the following formula:
H(t) – (hΩ(t) x (∑j=1,N σj,x)) – (hΔ(t) x (∑j=1,N nj)) + (∑i=1,N (∑N,j=1, j≠1 C6 / ri,j 6 x ni x nj))
where: h is Planck's constant divided by 2π; Ω is a Rabi frequency of the laser radiating the ensemble of atoms; Δ, which is greater than or equal to zero, is a detuning between the laser radiating the ensemble of atoms and atomic frequencies of the atoms in the vacuum chamber; N is an amount of atoms within the ensemble of atoms; C6 is an interaction strength of Van der Waals long-range interactions between atoms; rij is a physical distance between atoms i and j; σj x=|0 1|+|1 0|; ni=|1 1|; nj=|1 1|; and |0 and |1 are respective electronic levels for quantum states of an atom and respectively correspond to an atomic ground state and a Rydberg state (see Henriet’s equation 1 as found on page 14, where the equation is the same or substantially similar to what is recited here).
The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 10, Anderson in view of Henriet teaches the method of claim 9, as discussed above. The aforementioned references further teach the additional limitations further comprising wherein the laser implements the following unitary operation on the ensemble of atoms by evolving a time T: U(T)=𝒯 exp (-i/h x ∫0,T H(t) x dt) and where: 𝒯 is a time-ordering operator; h is Planck's constant divided by 2π (Henriet’s section 2.2.2 beginning on its page 14). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 11, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising
wherein the at least one laser parameter includes a Rabi frequency of the laser radiating the ensemble of atoms, a detuning between the laser radiating the ensemble of atoms and atomic frequencies of the atoms, and a gate time T of the laser radiating the ensemble of atoms (Henriet: page 10 discussing “... In the Pasqal device the laser is an optical laser field driving Raman transitions through an intermediate atomic state. The atom-laser interaction is characterized by the Rabi frequency ... the detuning ... and their relative phase ... Hence, any single-qubit gate can be implemented by tuning the pulse duration, the laser intensity, the detuning, and the phase of the laser ...”, where the Examiner reasons that the phase and pulse duration elements as taught are equivalent to the recited “gate time” and that a Rabi frequency and detuning aspects are explicitly and plainly taught per this citation). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 12, Anderson in view of Henriet teaches the method of claim 11, as discussed above. The aforementioned references further teach the additional limitations further comprising wherein in each step of radiating the ensemble of atoms with laser, the Rabi frequency and the detuning are kept constant (per the same cited portion of Henriet as found on its page 10, “Hence, any single-qubit gate can be implemented by tuning the pulse duration, the laser intensity, the detuning and the phase of the laser. These parameters are controlled using direct digital synthetizers (DDS) that drive acousto- and electro-optic modulators (AOM and EOM) placed on the laser beams.”, where the Examiner reasons that this teaches a capability to tune each and/or all of the parameters to a user’s discretion and that per a user’s intention, some of the parameters may be kept fixed/constant, and that tuning permutations/variations to that end are obvious to try). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 13, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising wherein the neutral atoms are rubidium atoms or ytterbium atoms (Henriet: page 5, section 2, first paragraph teaching “The technology at the heart of Pasqal is based on configurable arrays of single neutral atoms. The array can be seen as a register, where each single atom plays the role of a qubit. At Pasqal we use rubidium atoms, a very common species in atomic physics that bene ts from well-established technological solutions, especially in terms of lasers. More precisely, two electronic levels of the rubidium atoms are chosen to be the two qubit states, which we refer to as 0 and 1 ...”). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 16, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising a data processing device or system comprising means for carrying out the digital steps of the method of claim 1 (Henriet: page 4, Figure 1, showing the quantum stack including applications, software, and hardware, where some of these features are well-understood to use or be implemented in executable code/instructions as recited). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 17, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising a controlling device or system comprising: a vacuum chamber (Henriet: page 6, section 2.1.1, first paragraph, teaching “... As a starting point, a dilute atomic vapor is formed inside an ultra-high vacuum system operated at room temperature ...”), at least two lasers (Henriet: page 6, section 2.1.1, first paragraph, teaching first and second laser systems), optical means (Henriet: pages 6-7, section 2.1.1, third and final paragraph, teaching “... To detect which of the tweezers are filled, the atoms are imaged by collecting their fluorescence onto a sensitive camera ...”) and means adapted to execute the steps of the method of claim 1 (Henriet: page 6, Figure 3, showing hardware components, and see also page 4, Figure 1, showing the quantum stack which is inclusive of hardware elements for example). The motivation for combining the references is as discussed above in relation to claim 1.
Regarding claim 18, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references further teach the additional limitations further comprising a computer program product comprising computer program instructions/code to cause a data processing device or system or a controlling device or system to execute the steps of the method of claim 1 (Henriet: page 4, Figure 1, showing the quantum stack including applications, software, and hardware, where some of these features are well-understood to use or be implemented in executable code/instructions as recited). The motivation for combining the references is as discussed above in relation to claim 1.
7. Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Anderson in view of Henriet and further in view of Non-Patent Literature “QBoost: Large Scale Classifier Training with Adiabatic Quantum Optimization” (“Neven”).
Regarding claim 14, Anderson in view of Henriet teaches the method of claim 1, as discussed above. The aforementioned references do not teach the additional limitation wherein the defined cost function comprises a square loss part and a regularization part, the regularization part including a L0-norm. Rather, the Examiner relies upon NEVEN to teach what Anderson etc. otherwise lack, see e.g., Neven’s discussion of l0 norm regularization to compute loss as part of a machine learning training/learning task, per section 2 beginning on page 334.
Anderson and Henriet both relate to machine learning tasks based on a quantum computing paradigm, and are amenable to informing how quantum computing might be used to perform classification. Neven is similarly directed, and therefore analogous. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Neven’s l0-norm regularization with Anderson’s modified framework, with a reasonable expectation of success, such as to realize the good generalization benefits including fast execution during performance as Neven mentions in the paragraph beginning at the bottom of Neven’s page 334.
Regarding claim 15, Anderson in view of Henriet and further in view of Neven teaches the method of claim 14, as discussed above. The aforementioned references teach the additional limitations wherein the defined cost function is: ∑s,S (1/N x (∑I,N (wi x hi (xs))) - ys)2 + λ ||w||0
where: w is a set of the one or more weighting factors associated to each classifier of the plurality of classifiers; S is a dataset; N is a quantity of classifiers within the plurality of classifiers; xs is s-th datapoint from the dataset S; hi(xs) is a classification of the s-th datapoint xs provided by i-th classifier hi from the plurality of classifiers; wi is one or more weighting factors from the set of weighting factors w and associated to the i-th classifier hi; ys is a correct classification of the s-th datapoint xs; ∥w∥0 is an L0-norm of the set of weighting factors w; λ is a real number (see claim 1’s discussion of the cost functions as taught per Anderson and/or Henriet, and where the exact arrangement of the same/similar variables in the equation is rendered obvious because it would be obvious to try to arrange the variables/values in a way that is essentially a design choice). The motivation for combining the references is as discussed above in relation to claim 14.
Response to Arguments
8. Applicants’ arguments, received 9/10/25, with respect to the prior art rejections of the pending claims have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, new grounds of rejection are made in view of the newly-relied upon Anderson reference.
Conclusion
9. The prior art made of record and not relied upon is considered pertinent to Applicants’ disclosure:
U.S. Patent Application Publication No. 2019/0164034 Gambetta: [0001], [0003]-[0005], [0020], [0023]-[0024], and [0028].
Non-Patent Literature “Training Ensembles of Quantum Binary Neural Networks”
Non-Patent Literature “Quantum ensembles of quantum classifiers”
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHOURJO DASGUPTA whose telephone number is (571)272-7207. The examiner can normally be reached M-F 8am-5pm CST.
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/SHOURJO DASGUPTA/Primary Examiner, Art Unit 2144
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