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 .
DETAILED ACTION
This action is responsive to the response to restriction/election filed on 2/18/2026. Applicant elected claims 2-11.
Claim Objections
Claim 9 is objected to because of the following informalities: “a quantum processor, the quantum processor being a gate-based quantum processor implementing an extreme learning machine ELM having an input layer” is missing a semicolon. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: “multiplication of the inverse matrix by the true labels vector” where “true labels vector” has the wrong antecedent.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2-11 are rejected under 35 U.S.C. 103 as being unpatentable over “Quantum reservoir computing: a reservoir approach toward quantum machine learning on near-term quantum devices”, Fujii et al, 2020 in view of “Tutorial: Photonic Neural Networks in Delay Systems”, Brunner et al, 2021.
Referring to claim 2, Fujii discloses the method for training a quantum-based extreme learning machine using a quantum processor implementing a quantum substrate and a set of training data, wherein the training data comprises input features vectors with a plurality of N parameters and true labels vector, (page 2 of Fujii, “quantum state is represented as a normalized vector on a complex vector space.”) the method comprising:
- uploading the training data to the quantum processor; (page 2 of Fujii, “The former is a frame work to use quantum reservoir for nontemporal tasks, that is, the input is fed into a quantum system, and generalization or classification tasks are performed by a linear regression on a quantum enhanced feature space. In the latter, the parameters of the quantum system is further fine-tuned via the gradient descent by measuring an analytically obtained gradient, just like the back propagation for feed forward neural networks.”)
- passing a plurality of subsets of the input features vector from the training data through the quantum substrate to obtain a plurality of output vectors of expectation values; (page 7 of Fujii, linear regression is taken by using the classical feature vector exacted from the quantum feature space)
- concatenation of the plurality of output vectors of expectation values to construct a matrix; (page 5 of Fujii, “improve a representation power of the model, we can concatenate the linear transformation and the activation function”)
- computation of an inverse matrix from the matrix; and - multiplication of the inverse matrix by the true labels vector to obtain a vector of optimal weights P. (page 5 of Fujii, “Finally the output is extracted by an output weight Wout (1 ×N dimensional matrix): Woutσ(Winx). (45) The parameters in Win and Wout are trained such that the error between the output and teacher data becomes minimum. While this optimization problem is highly nonlinear, a gradient based optimization, so-called back propagation, can be employed.”)
Fujii does not specifically disclose “encoding the uploaded training data”.
However, Brunner discloses encoding the uploaded training data (page 3 of Brunner, Temporal input-masking needs to randomly map the input information onto the delay Reservoir’s temporal dimensions according to Win. This is generally achieved by a temporal encoding technique called time multiplexing, with time multiplexing according to a simple input masking sequence visualized in the left panel of Fig. 5.)
Fujii and Brunner are analogous art because both references concern managing ELM qubit output values. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujii’s ELM with ANN with substrates that encodes data as taught by Brunner. The motivation for doing so would have been more efficiently process ELM gate information.
Referring to claim 3, Fujii in view of Brunner disclose the method of claim 2, wherein the inverse matrix is a Moore-Penrose pseudo inverse matrix. (page 5 of Fujii, superimposing can be using Moore-Penrose Pseudo inverse matrix)
Referring to claim 4, Fujii in view of Brunner disclose the method of claim 2, wherein the encoding is one of basis encoding, amplitude encoding, angle encoding, qsample encoding and Hamiltonian encoding. (page 6 of Brunner, This is generally achieved by a temporal encoding technique called time multiplexing)
Referring to claim 5, Fujii in view of Brunner disclose the method of claim 2, wherein the quantum substrate comprises n qubits and wherein n<N. (page 6 of Fujii, quantum qubits, each qubits has multiple gates, which means that parameters are greater than number of qubits)
Referring to claim 6, Fujii in view of Brunner disclose the method of claim 2, wherein each output vector of expectation values is a row of the matrix. (page 2 of Fujii, output vector of expectation value is row of the matrix)
Referring to claim 7, Fujii in view of Brunner disclose the method of claim 2, further comprising normalizing values of the training data. (page 2 of Fujii, complex probability amplitudes have to be normalized)
Referring to claim 8, Fujii in view of Brunner disclose the method of claim 2, further comprising redundantly encoding values of the training data. (page 6 of Brunner, This process is repeated for each n, and as such is the multiplication by Win and time multiplexing according to δτ. A delay-node’s input typically consists of consecutive and non-overlapping sequences of length τm.)
Referring to claim 9. (Currently Amended) A computing system for implementing a quantum-based extreme learning machine comprising:
- a quantum processor, the quantum processor being a gate-based quantum processor implementing an extreme learning machine ELM having an input layer - a plurality of input/output devices for inputting training data to the quantum processor and outputting a vector of optimal weights p from the quantum processor; (page 2 of Fujii, “The former is a frame work to use quantum reservoir for nontemporal tasks, that is, the input is fed into a quantum system, and generalization or classification tasks are performed by a linear regression on a quantum enhanced feature space. In the latter, the parameters of the quantum system is further fine-tuned via the gradient descent by measuring an analytically obtained gradient, just like the back propagation for feed forward neural networks.”)
- a gate-based quantum processor implementing an extreme learning machine ELM having an input layer; and, an output layer and a connection layer; (page 5 of Fujii)
- wherein the quantum substrate is adapted for receiving a plurality of subsets of input features vector from the training data and outputting a plurality of output vectors of expectation values;- and (page 7 of Fujii, linear regression is taken by using the classical feature vector exacted from the quantum feature space)
- wherein the connection layer is adapted for concatenation of the plurality of output vectors of expectation values to construct a matrix, computation of an inverse matrix from the matrix; and multiplication of the inverse matrix by the true labels vector to obtain a vector of optimal weights. (page 5 of Fujii, “Finally the output is extracted by an output weight Wout (1 ×N dimensional matrix): Woutσ(Winx). (45) The parameters in Win and Wout are trained such that the error between the output and teacher data becomes minimum. While this optimization problem is highly nonlinear, a gradient based optimization, so-called back propagation, can be employed.”)
Fujii does not specifically disclose “a quantum substrate in the quantum processor with a plurality of noisy quantum gates”.
However, Brunner discloses a quantum substrate in the quantum processor with a plurality of noisy quantum gates (page 1 of Brunner, The Reservoir Computing (RC) concept is of spe cial relevance to the implementation of ANNs in un conventional physical substrates.)
Fujii and Brunner are analogous art because both references concern managing ELM qubit output values. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Fujii’s ELM with ANN with substrates that encodes data as taught by Brunner. The motivation for doing so would have been more efficiently process ELM gate information.
Referring to claim 10, Fujii in view of Brunner disclose the computing system of claim 9, wherein the quantum substrate is a quantum system with a number of qubits. (page 6 of Fujii, quantum qubits)
Referring to claim 11, Fujii in view of Brunner disclose the computing system of claim 9, wherein the noisy quantum gate is a controlled NOT gate. (page 6 of Fujii, CNOT gate)
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
“Effective algorithms of the Moore-Penrose inverse matrices for extreme learning machine”, Lu et al, 2015: Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. After the input weights and the hidden layer biases are chosen randomly, ELM can be simply considered a linear system. However, the learn ing time of ELM is mainly spent on calculating the Moore-Penrose inverse matrices of the hidden layer output matrix. This paper focuses on effective computation of the Moore-Penrose inverse matrices for ELM, several methods are proposed. They are the reduced QR factorization with column Pivoting and Geninv ELM (QRGeninv-ELM), tensor product matrix ELM (TPM ELM). And we compare QRGeninv-ELM, TPM-ELM with the relational algorithm of Moore-Penrose inverse matrices for ELM, the relational algorithms are: Cholesky factorization of singular matrix ELM (Geninv-ELM), QR factorization and Ginv ELM (QRGinv-ELM), the conjugate Gram-Schmidt process ELM (CGS-ELM). The experimental results and the statistical analysis of the experimental results both demonstrate that QRGeninv-ELM, TPM-ELM and Geninv-ELM are faster than other kinds of ELM and can reach comparable generalization performance.
“Extreme Learning Machine With Affine Transformation Inputs in an Activation function”, Cao et al, 2019: The extreme learning machine (ELM) has attracted much attention over the past decade due to its fast learning speed and convincing generalization performance. However, there still remains a practical issue to be approached when applying the ELM: the randomly generated hidden node parameters without tuning can lead to the hidden node outputs being nonuniformly distributed, thus giving rise to poor generalization performance. To address this deficiency, a novel activation function with an affine transformation (AT) on its input is introduced into the ELM, which leads to an improved ELM algorithm that is referred to as an AT-ELM in this paper. The scaling and translation parameters of the AT activation function are computed based on the maximum entropy principle in such a way that the hidden layer outputs approximately obey a uniform distribution. Application of the AT-ELM algorithm in nonlinear function regression shows its robustness to the range scaling of the network inputs. Experiments on nonlinear function regression, real-world data set classification, and benchmark image recognition demonstrate better performance for the AT-ELM compared with the original ELM, the regularized ELM, and the kernel ELM. Recognition results on benchmark image data sets also reveal that the AT-ELM outperforms several other state-of-the-art algorithms in general.
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
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)).
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Conclusion
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/HAIMEI JIANG/ Primary Examiner, Art Unit 2142
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