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 .
Response to Amendment
This Office Action is in response to the amendment filed on 6/29/26. The applicant’s remarks and amendments to the claims were considered and results as
follow: THIS ACTION IS MADE FINAL.
3. Claims 1, 10, 11, 12, 13, 14, 15, 16 and 17 have been amended. Claim 3 has been cancelled. Claim 18 has been added. As a result, claims 1-2 and 4-18 now pending in this office action.
Claim Rejections 35 U.S.C. §103
4. 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.
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 following is a quotation of 35 U.S.C. 103 which forms the basis for all
obviousness rejections set forth in this Office action:
Claims 1-2, 5-6 and 10-18 are rejected under 35 U.S.C. 103 as being unpatentable over TOMARU (US 2021/0064994 A1) in view of Benjamin et al. (US 2022/0156629 A1).
Regarding claim 1, TOMARU teaches an information processing device, comprising at least one processor carrying out:
a first acquisition process to acquire input data, (See TOMARU Abstract a reservoir arithmetic device including an input unit);
a generation process to generate, from the input data, reservoir input data, (See TOMARU paragraph [0042], The reservoir arithmetic device 100 includes a reservoir 20, an input unit 10),
a second acquisition process to acquire an output result of the quantum reservoir, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), to which the reservoir input data has been input, ((See TOMARU Abstract a reservoir arithmetic device including an input unit); and
an output data generation process to generate, by an output data generator, output data with reference to the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs calculation in response to an input value input through the input unit by utilizing the dynamics of the nodes and outputs a result).
TOMARU does not explicitly disclose which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs, wherein the reservoir input data, includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result.
However, Benjamin teaches which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs, (See Benjamin; paragraph [0136], The charge reservoirs 250 acts as a source and drain of electrons. When the energy levels of the quantum dots and the reservoirs are tuned to the right level), wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code), and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result, (See Benjamin; paragraph [0155], the mediator dots 230, which will result in faulty exchange gates. However, each mediator dot 230 can be coupled/attached to a charge reservoir (which may be the same charge reservoir as initially used to populate the quantum dot array)…Since the electrons in the mediator dots 230 are outside of the computational subspace…such a coupling to a charge reservoir is highly unlikely to introduce any further errors).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs, wherein the reservoir input data, includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result. of Benjamin; in order to protect quantum information from errors due to decoherence and other sources of noise.
Regarding claim 2, TOMARU taught the information processing device as set forth of claim 1, as described above. TOMARU further teaches wherein
:the generation process divides the input data into a plurality of time slices, and generates the reservoir input data with use of a representative value of the input data in each of the plurality of time slices, (See TOMARU paragraph [0095], a machine learning model illustrated in FIG. 10, multilayered nodes for deep learning are provided in the portion corresponding to the reservoir 20 in FIG. 1. As described above, the feature of the present invention lies in that the measurement outcome z.sub.i(t.sub.k′) at the node i at the time point t.sub.k′(k≠k′) also contributes to the output y(t.sub.k) at the time point t.sub.k.).
Regarding claim 5, TOMARU taught the information processing device as set forth of claim 1, as described above. TOMARU further teaches wherein: the quantum reservoir has two or more layers of sub- reservoirs, (See TOMARU paragraph [0002], the output layer, meaning that the versatility of the calculation unit is not required in the first place. Thus, while deep learning requires a calculator with relatively high versatility as a precondition, reservoir computing does not require the versatility of the calculation unit as a precondition. In view of this, active researches for using physical systems such as a spin system and a quantum system in reservoir computing have recently been conducted).
Regarding claim 6, TOMARU taught the information processing device as set forth of claim 1, as described above. TOMARU further teaches wherein :the quantum reservoir is a nonlinear physical system that utilizes dynamics of a qubit, (See TOMARU paragraph [0003], a quantum reservoir includes multiple virtual nodes that hold signals from the respective qubits at each time points dividing a certain time interval into multiple intervals, a weight for determining a linear weight in a linear coupling between the multiple virtual nodes is determined, and an output signal is read that is obtained from quantum superimposition of the states at the virtual node and is linearly combined using the linear weight determined.”).
Regarding claim 10, an information processing device, comprising at least one processor carrying out:
a first acquisition process to acquire training data, (See TOMARU Abstract a reservoir arithmetic device including an input unit); including input data and label data, (See TOMARU paragraph [0045], input as training data. Here, the subscript v indicates that x.sub.v and y.sub.v.sup.0 are vectors, and components of these are represented as x.sub.v=(x.sub.1, x.sub.2, . . . , x.sub.nx).sup.T);
a generation process to generate, from the input data, reservoir input data (See TOMARU paragraph [0042], The reservoir arithmetic device 100 includes a reservoir 20, an input unit 10),
a second acquisition process to acquire an output result of the quantum reservoir, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), to which the reservoir input data has been input, ((See TOMARU Abstract a reservoir arithmetic device including an input unit); and
a training process to train, with use of the label data, (See TOMARU paragraph [0047], the first training data x.sub.v input to the node 201 time-evolves), an output data generator which generates output data with reference to the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs calculation in response to an input value input through the input unit by utilizing the dynamics of the nodes and outputs a result).
TOMARU does not explicitly disclose including input data and label data; reservoir input data which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result.
However, Benjamin teaches disclose which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs, (See Benjamin; paragraph [0136], The charge reservoirs 250 acts as a source and drain of electrons. When the energy levels of the quantum dots and the reservoirs are tuned to the right level), and wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code), and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result, (See Benjamin; paragraph [0155], the mediator dots 230, which will result in faulty exchange gates. However, each mediator dot 230 can be coupled/attached to a charge reservoir (which may be the same charge reservoir as initially used to populate the quantum dot array)…Since the electrons in the mediator dots 230 are outside of the computational subspace…such a coupling to a charge reservoir is highly unlikely to introduce any further errors).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result of Benjamin in order to protect quantum information from errors due to decoherence and other sources of noise.
Regarding claim 11, TOMARU teaches an information processing device, comprising:
at least one processor carrying out, a first acquisition process to acquire reservoir input data, (See TOMARU Abstract a reservoir arithmetic device including an input unit);
a second acquisition process to acquire an output result of the quantum reservoir, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), to which the reservoir input data has been input, ((See TOMARU Abstract a reservoir arithmetic device including an input unit); and
an output process to output the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs…and outputs a result).
TOMARU does not explicitly disclose a quantum reservoir having a plurality of layers of sub- reservoirs; and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated Quantum computations on each sub-reservoir to output the output result.
However, Benjamin teaches a quantum reservoir having a plurality of layers of sub- reservoirs, (See Benjamin; paragraph [0136], The charge reservoirs 250 acts as a source and drain of electrons. When the energy levels of the quantum dots and the reservoirs are tuned to the right level); and wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, , (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code), and the quantum reservoir is configured to execute the designated Quantum computations on each sub-reservoir to output the output result, (See Benjamin; paragraph [0155], the mediator dots 230, which will result in faulty exchange gates. However, each mediator dot 230 can be coupled/attached to a charge reservoir (which may be the same charge reservoir as initially used to populate the quantum dot array)…Since the electrons in the mediator dots 230 are outside of the computational subspace…such a coupling to a charge reservoir is highly unlikely to introduce any further errors).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify a quantum reservoir having a plurality of layers of sub- reservoirs; and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated Quantum computations on each sub-reservoir to output the output result of Benjamin in order to protect quantum information from errors due to decoherence and other sources of noise.
Regarding claim 12, TOMARU teaches an information processing system comprising a first information processing device and a second information processing device, (See TOMARU paragraph [0003], a quantum information processing system where reservoir computing is applied to a quantum system);
the first information processing device including at least a first processor carrying out, (See TOMARU paragraph [0034], the object for such processing is variously provided. In some cases, a program is executed mainly with a processor);
a first acquisition process to acquire input data, (See TOMARU Abstract a reservoir arithmetic device including an input unit);
a generation process to generate, from the input data, (See TOMARU paragraph [0042], The reservoir arithmetic device 100 includes a reservoir 20, an input unit 10),
a second acquisition process to acquire an output result of the quantum reservoir to which the reservoir input data has been input, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), and
an output data generation process to generate output data with reference to the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs calculation in response to an input value input through the input unit by utilizing the dynamics of the nodes and outputs a result). and
the second information processing device including, (See TOMARU paragraph [0034], the object for such processing is variously provided. In some cases, a program is executed mainly with a processor); and
a third acquisition process to acquire reservoir input data which has been generated by the generation process of the first information processing device, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100),
a fourth acquisition process to acquire an output result of the quantum reservoir to which the reservoir input data has been input, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), and
an output process to output the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs calculation in response to an input value input through the input unit by utilizing the dynamics of the nodes and outputs a result).
TOMARU does not explicitly disclose reservoir input data which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result.
However, Benjamin teaches reservoir input data which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs, (See Benjamin; paragraph [0136], The charge reservoirs 250 acts as a source and drain of electrons. When the energy levels of the quantum dots and the reservoirs are tuned to the right level), and wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code), and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result, (See Benjamin; paragraph [0155], the mediator dots 230, which will result in faulty exchange gates. However, each mediator dot 230 can be coupled/attached to a charge reservoir (which may be the same charge reservoir as initially used to populate the quantum dot array)…Since the electrons in the mediator dots 230 are outside of the computational subspace…such a coupling to a charge reservoir is highly unlikely to introduce any further errors).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify reservoir input data which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result of Benjamin in order to protect quantum information from errors due to decoherence and other sources of noise.
Regarding claim 13, TOMARU teaches
An information processing method, comprising, (See TOMARU paragraph [0003], quantum information processing system):
a first acquisition step of acquiring input data, (See TOMARU Abstract a reservoir arithmetic device including an input unit);
a generation step of generating, from the input data, reservoir input data which is to be input, (See TOMARU Abstract a reservoir arithmetic device including an input unit);
a second acquisition step of acquiring an output result of the quantum reservoir, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), to which the reservoir input data has been input, ((See TOMARU Abstract a reservoir arithmetic device including an input unit); and
an output data generation step of generating output data with reference to the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs calculation in response to an input value input through the input unit by utilizing the dynamics of the nodes and outputs a result).
TOMARU does not explicitly disclose to a quantum reservoir having a plurality of layers of sub-reservoirs, and wherein the reservoir input data, includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir, is configured to execute the designated quantum computations on each sub-reservoir to output the output result.
However, Benjamin teaches to a quantum reservoir having a plurality of layers of sub-reservoirs, (See Benjamin; paragraph [0136], The charge reservoirs 250 acts as a source and drain of electrons. When the energy levels of the quantum dots and the reservoirs are tuned to the right level), wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), and wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code), and the quantum reservoir, is configured to execute the designated quantum computations on each sub-reservoir to output the output result, (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code).. It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result of Benjamin in order to protect quantum information from errors due to decoherence and other sources of noise.
Regarding claim 14, TOMARU teaches an information processing method, comprising:
a first acquisition step of acquiring training data including input data and label data, (See TOMARU Abstract a reservoir arithmetic device including an input unit);
a generation step of generating, from the input data, reservoir input data, (See TOMARU paragraph [0042], The reservoir arithmetic device 100 includes a reservoir 20, an input unit 10);
a second acquisition step of acquiring an output result of the quantum reservoir, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), to which the reservoir input data has been input, ((See TOMARU Abstract a reservoir arithmetic device including an input unit); and
a training step of training, with use of the label data, (See TOMARU Abstract a reservoir arithmetic device including an input unit); an output data generator which generates output data with reference to the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs calculation in response to an input value input through the input unit by utilizing the dynamics of the nodes and outputs a result).
TOMARU does not explicitly disclose which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result.
However, Benjamin teaches which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs, (See Benjamin; paragraph [0136], The charge reservoirs 250 acts as a source and drain of electrons. When the energy levels of the quantum dots and the reservoirs are tuned to the right level), and wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code), and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result, (See Benjamin; paragraph [0155], the mediator dots 230, which will result in faulty exchange gates. However, each mediator dot 230 can be coupled/attached to a charge reservoir (which may be the same charge reservoir as initially used to populate the quantum dot array)…Since the electrons in the mediator dots 230 are outside of the computational subspace…such a coupling to a charge reservoir is highly unlikely to introduce any further errors).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result of Benjamin in order to protect quantum information from errors due to decoherence and other sources of noise.
Regarding claim 15, TOMARU teaches an information processing method, comprising:
a first acquisition step of acquiring reservoir input data, (See TOMARU Abstract a reservoir arithmetic device including an input unit);
a second acquisition step of acquiring an output result of a quantum reservoir to which the reservoir input data has been input, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), and
an output step of outputting the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs…and outputs a result).
TOMARU does not explicitly disclose which has been generated from time series data and the quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result.
However, Benjamin teaches which has been generated from time series data and the quantum reservoir having a plurality of layers of sub-reservoirs, (See Benjamin; paragraph [0136], The charge reservoirs 250 acts as a source and drain of electrons. When the energy levels of the quantum dots and the reservoirs are tuned to the right level), and wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code), and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result, (See Benjamin; paragraph [0155], the mediator dots 230, which will result in faulty exchange gates. However, each mediator dot 230 can be coupled/attached to a charge reservoir (which may be the same charge reservoir as initially used to populate the quantum dot array)…Since the electrons in the mediator dots 230 are outside of the computational subspace…such a coupling to a charge reservoir is highly unlikely to introduce any further errors).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify which has been generated from time series data and the quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated quantum computations on each sub-reservoir to output the output result of Benjamin in order to protect quantum information from errors due to decoherence and other sources of noise.
Regarding claim 16, TOMARU teaches a non-transitory storage medium storing therein a program for causing a computer to function as an information processing device, the program causing the computer to carry out, (See TOMARU paragraph [0035], a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server may include a processor):
a first acquisition step of acquiring input data, (See TOMARU Abstract a reservoir arithmetic device including an input unit);
a generation step of generating, from the input data, reservoir input data, (See TOMARU paragraph [0042], The reservoir arithmetic device 100 includes a reservoir 20, an input unit 10),
a second acquisition step of acquiring an output result of the quantum reservoir, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), to which the reservoir input data has been input, ((See TOMARU Abstract a reservoir arithmetic device including an input unit); and
an output data generation step of generating output data with reference to the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs calculation in response to an input value input through the input unit by utilizing the dynamics of the nodes and outputs a result).
TOMARU does not explicitly disclose which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated Quantum computations on each sub-reservoir to output the output result.
However, Benjamin teaches which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs, (See Benjamin; paragraph [0136], The charge reservoirs 250 acts as a source and drain of electrons. When the energy levels of the quantum dots and the reservoirs are tuned to the right level), and wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code), and the quantum reservoir is configured to execute the designated Quantum computations on each sub-reservoir to output the output result, (See Benjamin; paragraph [0155], the mediator dots 230, which will result in faulty exchange gates. However, each mediator dot 230 can be coupled/attached to a charge reservoir (which may be the same charge reservoir as initially used to populate the quantum dot array)…Since the electrons in the mediator dots 230 are outside of the computational subspace…such a coupling to a charge reservoir is highly unlikely to introduce any further errors).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify which is to be input to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific quantum computations with respect to qubits constituting each sub-reservoir, and the quantum reservoir is configured to execute the designated Quantum computations on each sub-reservoir to output the output result of Benjamin in order to protect quantum information from errors due to decoherence and other sources of noise.
Regarding claim 17, TOMARU teaches a non-transitory storage medium storing therein a program for causing a computer to function as an information processing device, the program causing the computer to carry out, (See TOMARU paragraph [0035], a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server may include a processor):
a first acquisition step of acquiring training data including input data and label data, (See TOMARU Abstract, See paragraph [0008], the machine learning arithmetic device performs calculation in response to an input value input through the input unit by utilizing the dynamics of the nodes);
a generation step of generating, from the input data, reservoir input data which is to be input, (See TOMARU paragraph [0042], The reservoir arithmetic device 100 includes a reservoir 20, an input unit 10);
a second acquisition step of acquiring an output result of the quantum reservoir, (See TOMARU paragraph [0075], a specific calculation result. It is assumed that a quantum system (Ising model) is used for the reservoir arithmetic device 100), to which the reservoir input data has been input, ((See TOMARU Abstract a reservoir arithmetic device including an input unit); and
a training step of training, with use of the label data, an output data generator which generates output data with reference to the output result, (See TOMARU paragraph [0008], the machine learning arithmetic device performs calculation in response to an input value input through the input unit by utilizing the dynamics of the nodes and outputs a result).
TOMARU does not explicitly disclose to a quantum reservoir having a plurality of layers of sub-reservoirs, and wherein the reservoir input data, includes information that designates one or more specific Quantum computations with respect to qubits constituting each sub-reservoir, and the Quantum reservoir is configured to execute the designated Quantum computations on each sub-reservoir to output the output result.
However, Benjamin teaches to a quantum reservoir having a plurality of layers of sub-reservoirs, (See Benjamin; paragraph [0136], The charge reservoirs 250 acts as a source and drain of electrons. When the energy levels of the quantum dots and the reservoirs are tuned to the right level), and wherein the reservoir input data, (See Benjamin; paragraph [0065], The device for quantum information processing further comprises one or more charge reservoirs), includes information that designates one or more specific Quantum computations with respect to qubits constituting each sub-reservoir, (See Benjamin; paragraph [0018], the computation goes on, and eventually corrupt the logical qubits stored in the architecture. General schemes to cope with leakage errors have been proposed with various assumptions on the properties of the leakage errors. To correct the leakage errors, one can detect the leakage errors and replace the leaked qubits. Alternatively, one can apply leakage-reduction protocols to all the qubits, which ideally will restore the leaked qubits without affecting the normal qubits. Using these methods, leakage errors can be transformed into computational errors which can be handled by the quantum error correction code), and the Quantum reservoir is configured to execute the designated Quantum computations on each sub-reservoir to output the output result, (See Benjamin; paragraph [0155], the mediator dots 230, which will result in faulty exchange gates. However, each mediator dot 230 can be coupled/attached to a charge reservoir (which may be the same charge reservoir as initially used to populate the quantum dot array)…Since the electrons in the mediator dots 230 are outside of the computational subspace…such a coupling to a charge reservoir is highly unlikely to introduce any further errors).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify to a quantum reservoir having a plurality of layers of sub-reservoirs and wherein the reservoir input data includes information that designates one or more specific Quantum computations with respect to qubits constituting each sub-reservoir, and the Quantum reservoir is configured to execute the designated Quantum computations on each sub-reservoir to output the output result of Benjamin in order to protect quantum information from errors due to decoherence and other sources of noise.
Regarding claim 18, TOMARU taught the information processing device as set forth of claim 1, as described above.
TOMARU does not explicitly disclose wherein: each sub-reservoir comprises two or more qubits that together define a specific quantum circuit, configured to perform nonlinear transformation of input time series data into higher dimensional space.
However, Benjamin teaches wherein: each sub-reservoir comprises two or more qubits that together define a specific quantum circuit, (See Benjamin paragraph [0101], the processing of quantum information based on qubits, and in particular silicon quantum dot spin qubits in which electron spin states contain information), configured to perform nonlinear transformation of input time series data into higher dimensional space, (See Benjamin paragraph [0151], the computational subspace is the whole spin space of the electrons in the data dots 210 and the ancilla dots 220 in the ground charge configuration…which take the architecture from the charge ground states to a higher energy charge state).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein: each sub-reservoir comprises two or more qubits that together define a specific quantum circuit, configured to perform nonlinear transformation of input time series data into higher dimensional space of Benjamin in order to protect quantum information from errors due to decoherence and other sources of noise.
Claims 4 and 7-9 are rejected under 35 U.S.C. 103 as being unpatentable over TOMARU (US 2021/0064994 A1) in view of Benjamin et al. (US 2022/0156629 A1) and further in view of Ebrahimi Afrouzi (US 2024/0310851 A1).
Regarding claim 4, TOMARU together with Benjamin taught the information processing device as set forth of claim 1, as described above. TOMARU further teaches wherein: the first acquisition process further acquires label data together with the input data, (See TOMARU Abstract a reservoir arithmetic device including an input unit); and
TOMARU together with Benjamin s does not explicitly disclose log the metadata to the unified metadata catalog, the unified metadata catalog logging the metadata as the metadata changes over time.
However, Ebrahimi Afrouzi teaches the at least one processor further carries out a training process to train the output data generator with reference to the label data, (See Ebrahimi Afrouzi paragraph [0394], an object may be labelled automatically by the processor using a classification algorithm or by a user using an application of a communication device (e.g., by choosing from a list of possible labels or creating new labels such as sock, fridge, table, other, etc.)).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify log the metadata to
the unified metadata catalog, the unified metadata catalog logging the metadata as the
metadata changes over time of Ebrahimi Afrouzi for for operating a robot, including: capturing, by at least one image sensor disposed on the robot, images of a workspace.
Regarding claim 7, TOMARU together with Benjamin taught the information processing device as set forth of claim 1, as described above.
TOMARU together with Benjamin does not explicitly disclose wherein :the input data includes time series signals from a sensor that is provided in a robot, the quantum reservoir is a trained quantum reservoir which has been trained using the time series signals as input and which outputs an action of the robot, and the output data generation process generates a control signal which corresponds to an output result, of the quantum reservoir and which is used to control an action of the robot.
However, Ebrahimi Afrouzi teaches wherein :the input data includes time series signals from a sensor that is provided in a robot, (See Ebrahimi Afrouzi paragraph [0238], a robot including communication, mobility, actuation, and processing elements. In some embodiments, the robot may include, but is not limited to include, one or more of a casing, a chassis including a set of wheels, a motor to drive the wheels, a receiver that acquires signals transmitted from, for example, a transmitting beacon, a transmitter for transmitting signals, a processor, a memory storing instructions that when executed by the processor effectuates robotic operations, a controller, a plurality of sensors); the quantum reservoir is a trained quantum reservoir which has been trained using the time series signals as input and which outputs an action of the robot, (See Ebrahimi Afrouzi paragraph [0299], Quantum interpretation of an ANN. Cells of a neural network may be represented by slits or openings through which data may be passed onto a next layer using a governing protocol. In a double slit experiment, the governing rule is particle propagation… With training and back propagation knobs are adjusted such that when a signal is passing through one aperture); and the output data generation process generates a control signal which corresponds to an output result, (See Ebrahimi Afrouzi paragraph [0619], training may result in the neural network providing outputs with high accuracy from basic inputs. As more measured points are captured, increase in efficiency is observed) of the quantum reservoir and which is used to control an action of the robot, (See Ebrahimi Afrouzi paragraph [1181], Each walker may be seen as a simulation of a possible trajectory of a robot. In some embodiments, the processor may use quantum teleportation or population reconfiguration to address a common problem of weight disparity leading to weight collapse. In some embodiments, the processor may control extinction or absorption probabilities of some Markov processes).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to wherein :the input data includes time series signals from a sensor that is provided in a robot, the quantum reservoir is a trained quantum reservoir which has been trained using the time series signals as input and which outputs an action of the robot, and the output data generation process generates a control signal which corresponds to an output result, of the quantum reservoir and which is used to control an action of the robot of Ebrahimi Afrouzi for operating a robot, including: capturing, by at least one image sensor disposed on the robot, images of a workspace.
Regarding claim 8, TOMARU together with Benjamin taught the information processing device as set forth of claim 7, as described above.
TOMARU together with Benjamin does not explicitly disclose wherein: the sensor is a pressure sensor, a vibration sensor, or a camera sensor.
However, Ebrahimi Afrouzi teaches wherein: the sensor is a pressure sensor, a vibration sensor, or a camera sensor, (See Ebrahimi Afrouzi paragraph [0307], data collected from two cameras or one camera and a point measurement device, as opposed to a single camera. In another example, a processor of a robot bundles sensor data with ground truth LIDAR readings, from which a pattern emerges).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify the sensor is a pressure sensor, a vibration sensor, or a camera sensor of Ebrahimi Afrouzi for for operating a robot, including: capturing, by at least one image sensor disposed on the robot, images of a workspace.
Regarding claim 9, TOMARU together with Benjamin taught a robot system, comprising: an information processing device recited of claim 7, as described above. TOMARU further and a robot, (See Ebrahimi Afrouzi paragraph [0006], operating a robot, including: capturing, by at least one image sensor disposed on the robot, images of a workspace).
TOMARU together with Benjamin does not explicitly disclose and a robot.
However, Ebrahimi Afrouzi teaches a robot, (See Ebrahimi Afrouzi paragraph [0006], operating a robot, including: capturing, by at least one image sensor disposed on the robot, images of a workspace).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify and a robot of Ebrahimi Afrouzi for for operating a robot, including: capturing, by at least one image sensor disposed on the robot, images of a workspace.
Response to Arguments
Applicant's arguments with respect to claims 1-2 and 4-20 have been considered but are moot in view of the new ground(s) of rejection.
Conclusions/Points of Contacts
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MULUEMEBET GURMU/Primary Examiner, Art Unit 2163
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