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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-12, 22-24, 26-27 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Choukroun et al. (Deep Quantum Error Correction).
Choukroun discloses:
1. A method for detecting errors in a computation performed by a quantum computer comprising a plurality of data qubits, the method comprising:
obtaining error correction data for each of a plurality of time steps during the computation, the error correction data for each time step comprising a respective feature for each of a plurality of stabilizer qubits that each correspond to a respective subset of the data qubits for the time step; (p 5, 4.3. Noisy Syndrome Measurements: each syndrome measurement is repeated T times. This gives the decoder input an additional time dimension; p 3, 3.2. Quantum Error Correction Code: stabilizer measurements and are performed via additional qubits (ancilla bits). The result of all of the stabilizer measurements on a given state is called the syndrome; fig 3, 5)
initializing a decoder state that represents information about the plurality of stabilizer qubits, wherein the decoder state comprises a vector representation for each of the plurality of stabilizer qubits; and (p 3: QECC teaches stabilizer qubits; fig 1(b); p 4: "decoder that is parameterized by a vector of weights"; p 5: 4.4. Architecture and Training: decoder; fig 2: Quantum Error Correction Code Transformer (QECCT) architecture (QECCT))
for each of a plurality of updating time steps, wherein each updating time step corresponds to one or more of the time steps:
generating an intermediate representation comprising, for each of the stabilizer qubits, one or more embeddings representing the respective feature for the stabilizer qubit at the one or more time steps corresponding to the updating time step; and (p 5: 4.4. Architecture and Training: The initial encoding is defined as a d dimensional one-hot encoding of the n+ns input elements where n is the number of physical qubits and ns the length of the syndrome. The network gw is defined as a shallow network with two fully connected layers of hidden dimensions equal to 5ns and with a GELU non-linearity; fig 2)
processing a time step input for the updating time step through a Transformer neural network to update the decoder state for the updating time step, wherein the time step input comprises (i) the intermediate representation for the updating time step that comprises the one or more embeddings representing the respective feature for each of the stabilizer qubits and (ii) the decoder state for a preceding updating time step that comprises the vector representations for each of the stabilizer qubits after being updated at the preceding updating time step; and (p 3: QECC teaches stabilizer qubits; p 4: "decoder that is parameterized by a vector of weights"; p 5: 4.3 In the presence of measurement errors, each syndrome measurement is repeated T times. This gives the decoder input an additional time dimension; p 5: 4.4. Architecture and Training: embedding; fig 2: decoding, QECCT)
generating a prediction of whether an error occurred in the computation, comprising:
generating, from the decoder state for the last updating time step of the plurality of updating time steps, a respective input corresponding to each of one or more prediction neural networks; and (p 5, 4.3. Noisy Syndrome Measurements: each syndrome measurement is repeated T times. This gives the decoder input an additional time dimension; fig 2: decoding, output, equation 11, Quantum Error Correction Code Transformer architecture; p 5: 4.4. Architecture and Training)
processing each respective input using the corresponding prediction neural network to generate the prediction. (p 5: 4.4. Architecture and Training: The output is obtained via two fully connected layers. The first layer reduces the element-wise embedding to a one-dimensional n + ns vector and the second to an n dimensional vector representing the soft decoded noise, trained)
2. The method of claim 1, wherein the decoder state represents information about the plurality of data qubits. (p 5: 4.4. Architecture and Training; fig 2)
3. The method of claim 1, wherein the respective feature for each of the stabilizer qubits is an analog measurement of the corresponding subset of data qubits at the time step. (p 3: ancilla bits)
5. The method of claim 1, wherein the respective feature for each of the stabilizer qubits comprises posterior probabilities of a stabilizer measurement given analog measurements of the corresponding subset of data qubits at the time step. (p 3: ancilla bits)
6. The method of claim 1, wherein the respective feature for each of the stabilizer qubits comprises a time series of analog measurements of the corresponding subset of data qubits for a period of time ending at the time step. (p 3: ancilla bits)
7. The method of claim 1, wherein the error correction data for each time step comprises stabilizer events for one or more of the stabilizer qubits at the time step. (p 3, 3.2. Quantum Error Correction Code: syndrome)
8. The method of claim 1, wherein generating an intermediate representation comprising, for each of the stabilizer qubits, one or more embeddings representing the respective feature for the stabilizer qubit at the one or more time steps corresponding to the updating time step comprises:
generating respective embeddings for the stabilizer qubits for the updating time step; obtaining a respective positional embedding for each stabilizer qubit characterizing a position of the stabilizer qubit within the quantum computer; and processing the respective embeddings for the stabilizer qubits and the respective positional embeddings for the stabilizer qubits using an encoding neural network to generate the intermediate representation for the updating time step. (p 4: 3.3. Error Correction Code Transformer: positional embedding; p 5: 4.4. Architecture and Training: The network gw is defined as a shallow network with two fully connected layers of hidden dimensions equal to 5ns and with a GELU non-linearity)
9. The method of claim 1, wherein generating an intermediate representation comprising, for each of the stabilizer qubits, one or more embeddings representing the respective feature for the stabilizer qubit at the one or more time steps corresponding to the updating time step comprises:
generating respective embeddings for the stabilizer qubits for each time step corresponding to the updating time step; obtaining a respective positional embedding for each stabilizer qubit characterizing a position of the stabilizer qubit within the quantum computer; and processing the respective embeddings for the stabilizer qubits and the respective positional embeddings for the stabilizer qubits using an encoding neural network to generate the intermediate representation for the updating time step. (p 4: 3.3. Error Correction Code Transformer: positional embedding; p 5: 4.4. Architecture and Training: The network gw is defined as a shallow network with two fully connected layers of hidden dimensions equal to 5ns and with a GELU non-linearity)
10. The method of claim 1, wherein the one or more prediction neural networks comprise only one prediction neural network, and wherein processing each respective input using the corresponding prediction neural network to generate the prediction comprises processing the respective input using the prediction neural network to generate a score that indicates whether an error occurred. (p 5: 4.4. Architecture and Training: The output is obtained via two fully connected layers. The first layer reduces the element-wise embedding to a one-dimensional n + ns vector and the second to an n dimensional vector representing the soft decoded noise, trained)
11. The method of claim 1, wherein one or more initial updating time steps correspond to a respective plurality of time steps and a last updating time step corresponds only to a last time step of the plurality of time steps, and wherein the one or more prediction neural networks comprise only one prediction neural network, and wherein processing each respective input using the corresponding prediction neural network to generate the prediction comprises processing the respective input using the prediction neural network to generate a score that indicates whether an error occurred. (p 5: 4.4. Architecture and Training: The output is obtained via two fully connected layers. The first layer reduces the element-wise embedding to a one-dimensional n + ns vector and the second to an n dimensional vector representing the soft decoded noise, trained)
12. The method of claim 1, wherein the Transformer neural network comprises one or more Transformer layers, and wherein each Transformer layer comprises a self-attention layer and a feed-forward layer. (fig 2; p 5: 4.4. Architecture and Training: The decoder is defined as a concatenation of N decoding layers composed of self-attention and feed-forward layers)
22. The method of claim 1, wherein the prediction of whether an error occurred in the computation comprises a respective prediction for each of a plurality of logical observables. (p 4, 3.2. Quantum Error Correction Code: we are interested in the logical qubits, predicting the code up to the logical operators mapping L)
23. The method of claim 22, wherein the one or more prediction neural networks comprise a plurality of prediction neural networks, and wherein processing each respective input using the corresponding prediction neural network to generate the prediction comprises processing each respective input using the corresponding prediction neural network to generate a respective score that indicates whether an error for a corresponding logical observable of a plurality of logical observables occurred. (p 5: 4.4. Architecture and Training; fig 2)
24. The method of claim 22, wherein the one or more prediction neural networks comprise only one prediction neural network, and wherein each of the respective inputs is a transposed version of another respective input, wherein processing each respective input using the corresponding prediction neural network to generate the prediction comprises processing each respective input using the prediction neural network to generate a respective score that indicates whether an error for a corresponding logical observable for the respective input of a plurality of logical observables occurred. (p 5: 4.4. Architecture and Training; fig 2)
Claim(s) 26 is/are rejected as being the system implemented by the method of claim(s) 1, and is/are rejected on the same grounds.
Claim(s) 27 is/are rejected as being the media implemented by the method of claim(s) 1, and is/are rejected on the same grounds.
Allowable Subject Matter
Claim(s) 4, 13-21, 25 is/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.
Response to Remarks
DP rejection is withdrawn, since claims are narrower than claims in 18237204.
Applicant's Remarks have been fully considered but they are not persuasive.
Regarding the prior art rejection under 35 USC 102, the Remarks state, “However, Choukroun makes no mention of "generating, from the decoder state for the last updating time step of the plurality of updating time steps, a respective input corresponding to each of one or more prediction neural networks" because there is no "decoder state for the last updating time step." Thus, Choukroun describes "pooling over the time dimension" to generate the resulting "element-wise embedding" that is provided to a fully connected layer...” However, the examiner respectfully disagrees. Choukroun discloses, on p 5, 4.3. Noisy Syndrome Measurements, each syndrome measurement is repeated T times. This gives the decoder input an additional time dimension. Choukroun further discloses in fig 2: decoding and Quantum Error Correction Code Transformer architecture.
The Remarks state, “Furthermore, the Office Action appears to allege that a "vector representing the soft decoded noise" corresponds with "decoder state" as recited in the claim (See Office Action at p. 4 and 5). The "vector representing the soft decoded noise" cannot be mapped to both the "decoder state" and "a prediction of whether an error occurred in the computation." Such a mapping contradicts "generating, from the decoder state for the last updating time step of the plurality of updating time steps, a respective input corresponding to each of one or more prediction neural networks; and processing each respective input using the corresponding prediction neural network to generate the prediction," as recited in amended claim 1.” The OA has been updated. “The decoder state comprises a vector representation for each of the plurality of stabilizer qubits” is mapped to fig 1(b), p 4: "decoder that is parameterized by a vector of weights…"
Conclusion
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/KATHERINE LIN/ Primary Examiner, Art Unit 2113