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 pending in the application under prosecution and have been examined.
The specification has not been checked to the extent necessary to determine the presence of all possible minor errors.
The specification should be amended to reflect the status of all related application, whether patented or abandoned. Therefore, applications noted by their serial number and/or attorney docket number should be updated with correct serial number and patent number if patented.
The first instance of all acronyms or abbreviation should be spelled out for clarity, whether or not considered well known in the art.
In the response to this Office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application.
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
37 C.F.R. § 1.83(a) requires the Drawings to illustrate or show all claimed features.
Applicant must clearly point out the patentable novelty that they think the claims present, in view of the state of the art disclosed by the references cited or the objections made, and must also explain how the amendments avoid the references or objections. See 37 C.F.R. § 1.111(c).
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.
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 are rejected under 35 U.S.C. 103 as being unpatentable over US 20260220516 A1 (MAKSYMENKO et al) in view of US 20230177377 A1 (OH et al).
With respect to claims 1, 8, and 15, MAKSYMENKO hybrid quantum machine learning system, comprising: a classical computing subsystem (quantum computing algorithm to generate classical data values), configured to: execute a quantum circuit to determine a number of measurements based on a plurality of qubits and a plurality of parameters output from the secondary last feed forward layer (executing original quantum circuit to obtain a first set of measurement outputs quantum circuit pre-processing module configured to perform pre-processing operations on received parameterized quantum circuit data and output measurement values), wherein the classical computing subsystem is further configured to: use a cross-entropy loss function to determine the difference between a probability distribution for a number of variables and a true value for each of the number of variables (generating an array of classical random data values from a selected probability distribution, the array of classical random data values comprising a plurality of vectors) [Par. 0079-0082] (performing, using a cross entropy loss function, a similarity-based objective function calculation on data obtained from the first and second sets of measurement outputs, the similarity-based objective function calculation is performed to determine the divergence with the output results used to reconstruct the measurements of a large-scale quantum circuit) [Par. 0033-0034; Par. 0079-0082], wherein the probability distribution is based on the determined number of measurements (performing a similarity-based objective function calculation on data obtained based on the number of measurement outputs and computational steps) [Par. 0032-0033; Par. 0071-0073]; and update one or more weights associated with the number of variables in the pre- trained neural network through an optimizer (adjusting one or more of the parameters of the compressed PQC based on a result of the similarity-based objective function calculation) [Par. 0019-0020; Par. 0070-0073].
MAKSYMENKO hybrid quantum machine learning system, comprising quantum computing algorithm to generate classical random data values of a quantum computing circuit decomposed into slices, with each slice requiring only a fraction of the total number of qubits of that circuit, each quantum circuit slice may be represented as a parameterized quantum circuit (PQC) [Par. 0033-0034]. MAKSYMENKO fails to specifically teach instruction to remove a last feed forward layer from a pre-trained neural network and introduce a secondary last feed forward layer. However OH teaches deep learning model using a Rectified Linear Unit (ReLU) as an activation function of each hidden node, with output layer using a softmax function such that output shows a probability distribution, a loss function optimizes a weight and bias of a neural network using categorical cross entropy, method of reducing quantum readout errors applying certain single qubit rotation to a qubit using a quantum circuit: acquiring a measurement result of a readout object by performing quantum readout; inputting the acquired measurement result of the readout object into a neural network previously constructed in relation to errors, which are generated in quantum readout, by means of the quantum computer [Par. 0051-0054; Par. 0012-0015; Par. 0084-0085].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to combine the quantum computing algorithm that generates classical data values, taught by MAKSYMENKO with the apparatus performing correction of OH in order to infer an ideal measurement result corresponding to the measurement result of the readout object using the neural network by means of the quantum computer, where the inferring results of measurements fed to a classical decoding algorithm whose goal is to determine the most likely errors afflicting the data qubits, improving the performance of error correcting codes and fault-tolerant quantum computing architectures, as taught by OH [Par. 0054].
With respect to claims 2, 9, and 16, MAKSYMENKO and OH, combined, teach hybrid quantum machine learning system, wherein the secondary last feed forward layer is configured to output a number of parameters less than a number of parameters from the last feed forward layer (generating a compressed parameterized quantum circuit (PQC) starting from the initial state and using the first set of measurement outputs) [MAKSYMENKO’s Abstract; Par. 0019-0020].
With respect to claims 3, 10, and 17, MAKSYMENKO and OH, combined, teach hybrid quantum machine learning system, wherein the quantum circuit receives the plurality of qubits and the plurality of parameters to determine the number of measurements (parameters used in similarity-based objective function calculation for the measurement outputs of parameterized quantum circuit and input quantum circuit) [MAKSYMENKO’s Par. 0098-0102].
With respect to claims 4, 11, and 18, MAKSYMENKO and OH, combined, teach hybrid quantum machine learning system, wherein the probability distribution is produced by using a softmax activation function on the determined number of measurements (linear model to achieve output with a probability distribution using a softmax function in the final hierarchy of a neural network) [OH’s Par. 0035-0037; Par. 0052-0054].
With respect to claims 5, 12, and 19 MAKSYMENKO and OH, combined, teach hybrid quantum machine learning system, wherein the optimizer is configured to update the one or more weights in the secondary last feed forward layer and in the quantum circuit (deep learning model composed of an input layer showing a probability of measuring a computational base in actual measurement, and an output layer showing a probability of measuring computational base state in an ideal case) [OH’s Par. 0052-054].
With respect to claims 6, 13, and 20, MAKSYMENKO and OH, combined, teach hybrid quantum machine learning system, wherein the optimizer is configured to use cosine learning rate decay and/or sharpness aware minimization, wherein auto augmentation is applied to an input dataset (acquiring a measurement result of a readout object by performing quantum readout by means of a quantum computer; inputting parameter such as a learning rate that acquires measurement result of the readout object into a neural network previously constructed in relation to errors) [OH’s Par. 0053-0054; Par. 0011-0014].
With respect to claims 7 and 14, MAKSYMENKO and OH, combined, teach hybrid quantum machine learning system, further comprising performing a dagger initialization technique to provide an initial value to the one or more weights of the quantum circuit (quantum computation process composed of input state initialization) [OH’s Par. 0027-0029].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Learning hard distributions with quantum-enhanced Variational Autoencoders
Anantha Rao, Dhiraj Madan, Anupama Ray, Dhinakaran Vinayagamurthy, M.S.Santhanam http://creativecommons.org/licenses/by/4.0/, 05/02/2023.
M. Alam and S. Ghosh, "DeepQMLP: A Scalable Quantum-Classical Hybrid Deep Neural Network Architecture for Classification," 2022 35th International Conference on VLSI Design and 2022 21st International Conference on Embedded Systems (VLSID), Bangalore, India, 2022, pp. 275-280.
Elton Yechao Zhu, Sonika Johri, Dave Bacon, Mert Esencan, Jungsang Kim, Mark Muir, Nikhil Murgai, Jason Nguyen, Neal Pisenti, Adam Schouela, Ksenia Sosnova, Ken Wright, “Generative Quantum Learning of Joint Probability Distribution Functions”, https://doi.org/10.48550/arXiv.2109.06315, 13 Sep 2021.
US 20230094389 A1 YOU et al) teaching system comprising: at least one processing device comprising a processor coupled to a memory; the processing device being configured: to configure a machine learning system with a multi-layer network architecture comprising at least one neural network and one or more additional network layers; to train the neural network at least in part utilizing quantum sampling performed by a quantum computing device; to obtain data characterizing a monitored system; to process at least a portion of the obtained data through at least a portion of the multi-layer network architecture of the machine learning system to generate a prediction of at least one characteristic of the monitored system from the obtained data; and to execute at least one automated action relating to the monitored system based at least in part on the generated prediction.
US 20240403619 A1 (SHAIBA NASSAR et al) teaching artificial neural network architecture, comprising: muti layer comprising receptive layer dissected into a predefined amount of spatial (regions) kernels, each spatial kernel encompassing a predefined amount of receptive cells, each receptive cell comprising a predefined amount of receptive nodes.
US 20260023998 A1 (ELFVING et al) teaching training, by the classical computer, a quantum model as a quantum sampler configured to produce samples which are associated with a predetermined target probability distribution and which are exponentially hard to compute classically, the training including classically computing probability amplitudes associated with an execution of a first parameterized quantum circuit that defines a sequence of gate operations for a quantum register, the probability amplitudes being computed classically by simulating the sequence of gate operations on the classical computer.
US 20240095563 A1 (DOU et al) teaching quantum entanglement module is configured to associate quantum state information of different qubits; the quantum convolution kernel module is configured to extract feature information corresponding to the quantum state information; the measuring module is configured to measure a quantum state of a preset qubit and obtain a corresponding amplitude; the computing module is configured to compute a convolution result corresponding to the current group of input data according to the measured quantum state and its amplitude.
US 20240412093 A1 (PAPIC et al) teaching apparatus comprising at least one processor configured to performing a method for obtaining information on one or more error sources affecting dynamics of at least one qubit, the method comprising: receiving at least one characterizing measurement of the at least one qubit configured to act as a sensor for the one or more error sources affecting the dynamics of the at least one qubit; determining, based on the at least one characterizing measurement, at least one characterizing signal, which describes the dynamics of the at least one qubit; providing the at least one characterizing signal as an input to a neural network trained to predict information on the one or more error sources affecting the at least one characterizing signal; and receiving as an output, from the neural network, information on the one or more error sources affecting the at least one characterizing signal.
US 20210342730 A1 (REDMOND et al) teaching quantum optimizer apparatus for accelerating training of a neural network NN, comprising: a quantum system; a first neural network coupled to said quantum system and operative to: compress a plurality of activation and loss function outputs from a classic neural network utilizing an energy based model to generate a reduced number of activation and loss function outputs where state is read out from detectors and weight updates are calculated and fed back to the classic NN.
US 12057859 B1 (CHAMBERLAND et al) suggesting local neural network pre-trained via a supervised learning technique such that a local neural network decoder may be applied for error correction in the presence of circuit-level noise in arbitrarily sized surface codes in a local decoding stage, an intermediate stage may be used to remove vertical pairs of highlighted vertices within the matching graph prior to a global decoding stage.
US 12488273 B2 (Le Van Gong et al) teaching first instance and the one or more second instances of the quantum computer machine learning model each further comprise: an optimization circuit coupled to the parameterized quantum circuit, wherein the optimization circuit is configured to train the quantum computer machine learning model based on measured outputs of the parameterized quantum circuit.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PIERRE MICHEL BATAILLE whose telephone number is (571)272-4178. The examiner can normally be reached Monday - Thursday 7-6 ET.
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/PIERRE MICHEL BATAILLE/Primary Examiner, Art Unit 2138
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