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 .
Status of Claims
This Office Action is in response to the communication filed on 12 Oct 2023.
Claims 1-20 are being considered on the merits.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04 Jun 2020 has been considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, initialed and dated copies of Applicant's IDS form 1499 is attached to the instant Office action.
Drawings
The drawings filed on 12 Oct 2023 are accepted.
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.
Claims 1-2, 4-5, 10, 12-13, 15-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Sadeghi, et. al. (US 2024/0248947 A1; hereinafter, “Sadeghi”) in view of Alcazar, et. al (US 2024/0248947 A1; hereinafter, “Alcazar”) and further in view of Brown (WO 2024/102180 A2; hereinafter, “Brown”)
Claim 1:
A method of performing computation in a hybrid quantum-classical computing system comprising a classical computer and a quantum processor, comprising: (Brown, para. 0031: “According to still another aspect, a hybrid quantum-classical computing system is provided. In an example embodiment, the hybrid quantum-classical computing system comprises at least one classical computing engine, one or more real time engines, and a quantum processor”)
A hybrid quantum-classical computing system, comprising: (Brown, para. 0031: “According to still another aspect, a hybrid quantum-classical computing system is provided. In an example embodiment, the hybrid quantum-classical computing system comprises at least one classical computing engine, one or more real time engines, and a quantum processor”)
selecting, by a classical computer, samples of a set of variables (Sadeghi, para. 0019: “The analog computer, as a provider of samples, is an example of a sample generator. The analog computer can be operated to provide samples from a selected probability distribution, the probability distribution assigning a respective probability of being sampled to each data point in the population”) and a target joint distribution of the set of variables; (Sadeghi, para. 0021: “New points that are accepted are ones that make for a probabilistic convergence to the target distribution. Convergence is guaranteed if the proposal and acceptance criteria satisfy detailed balance conditions, and the proposal satisfies the ergodicity requirement.”)
selecting, by the classical computer, a set of variational parameters to construct a parametrized quantum circuit; (Alcazar, para. 0081: “A given variational quantum circuit may be parameterized in a suitable device-specific manner. More generally, the quantum gates making up a quantum circuit may have an associated plurality of tuning parameters. For example, in embodiments based on optical switching, tuning parameters may correspond to the angles of individual optical elements.”)
executing iterations, each iteration comprising: setting, by a system controller, a quantum processor in an initial state, (Alcazar, para. 0086: “Quantum annealing starts with the classical computer 254 generating an initial Hamiltonian 260 and a final Hamiltonian 262 based on a computational problem 258 to be solved, and providing the initial Hamiltonian 260, the final Hamiltonian 262 and an annealing schedule 270 as input to the quantum computer 252. The quantum computer 252 prepares a well-known initial state 266 (FIG. 2B, operation 264), such as a quantum-mechanical superposition of all possible states (candidate states) with equal weights, based on the initial Hamiltonian 260.”)
wherein the quantum processor comprises a plurality of trapped ions (Brown, para. 0088: “The illustrated example hybrid quantum-classical computing environment 100 comprises a quantum object confinement apparatus 70 (e.g., an ion trap, surface trap, Paul trap, and/or the like), in accordance with an example embodiment”), each of which has two frequency-separated states defining a qubit; (Brown, para. 00141: “For example, the one or more RTEs 310 may cause the quantum processor to perform one or more reading and/or measurement operations to determine, measure, and/or read the quantum state of one or more qubits.”)
applying, by the system controller, the parametrized quantum circuit to the quantum processor based on the set of the variational parameters (Alcazar, para. 0081, above), to transform the quantum processor from the initial state to a trial state; (Alcazar, para. 0086: “The quantum computer 252 starts in the initial state 266, and evolves its state according to the annealing schedule 270 following the time-dependent Schrodinger equation, a natural quantum-mechanical evolution of physical systems (FIG. 2B, operation 268).”)
measuring, by the system controller, an amplitude of the trial state (Alcazar, para. 0009: “Embodiments of the present invention implement a generative model based on Matrix Product States (MPS) to learn target distributions. MPS is a type of Tensor Network (TN) where the tensors are arranged in a one-dimensional geometry. Despise its simple structure, MPS can represent a large number of quantum states extremely well. Once the MPS form of the wavefunction W is chosen, learning can be achieved by adjusting parameters of the wavefunction such that the distribution represented by Born's rule is as close as possible to the data distribution” Examiner notes Alcazar teaches Born’s rule which includes measurement of a probability amplitude), to generate a trial joint distribution of the set of variables; and (Sadeghi, para. 0021: “New points that are accepted are ones that make for a probabilistic convergence to the target distribution. Convergence is guaranteed if the proposal and acceptance criteria satisfy detailed balance conditions, and the proposal satisfies the ergodicity requirement.” Examiner notes it is unclear how the act of measuring results in generation of a distribution—Sadeghi teaches necessarily measuring a proposal to assess convergence on a target distribution)
replacing, by the classical computer, the set of the variational parameters (Alcazar, para. 0081, above) with another set of variational parameters, if a difference between the generated trial joint distribution of the set of variables and an adaptive target joint distribution based on the target joint distribution and a mixing coefficient is more than a predetermined value; and (Sadeghi, para. 0030: “updated value for the ith variable based on the objective energy bias and the constraint energy bias, the updated value replacing the current value, incrementing a progress parameter, evaluating a termination criteria”)
outputting the set of the variational parameters, (Sadeghi, para. 0030: “and outputting a solution comprising the current values for the set of variables.”)
wherein the adaptive target joint distribution is a mixture of a uniform joint distribution of the set of variables with the target joint distribution, (Alcazar, para. 0042 and 0047: “Build an initial dataset, d0, with uniformity probability distribution” “Compute the cost for these two new bit strings, and recalculate the associated Boltzmann distribution. The new elements may be inside or outside of the current dataset, and a new normalization factor is required for the Boltzmann distribution.”)
the mixing coefficient is decreased in each iteration. (Sadeghi, para. 0025 and 0038: “In order to allow some flexibility in solving while also ensuring that constraints are satisfied, a penalty value may be assigned to the constraints, allowing the weight that the constraints are given to be varied, such that the constraint may be violated in some circumstances (such as at the start of a simulated annealing). When adding constraints to an objective function, a penalty value for the constraint may need to be selected without any guidance as to an appropriate penalty value in the given problem” “the method comprising receiving a sample from an optimization, determining an energy value for one or more constraint functions, evaluating feasibility of the sample, if the sample is not feasible, increasing a penalty value, if the sample is feasible, decreasing a penalty value, and returning a penalty value to an optimization algorithm.” Examiner notes Sadeghi teaches a penalty value i.e. mixing coefficient which decreases when a sample is feasible where Alcazar, para. 0086 teaches iterative computing with biases i.e. such as a penalty value).
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Alcazar into Sadeghi. Sadeghi teaches Systems and methods for optimization algorithms, updating samples, and penalizing constraint violations; Alcazar teaches a system and method for a quantum-enhanced optimizer (QEO) using quantum generative models to achieve lower minimum cost functions than classical or other known optimizers. One of ordinary skill would have been motivated to combine the teachings of Alcazar into Sadeghi in order find an optimization strategy that can work on arbitrary objective functions, bypassing the translation and overhead limitations (Alcazar, para. 0003).
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Brown into Sadeghi as modified. Brown teaches hybrid quantum-classical computing environments and methods. One of ordinary skill would have been motivated to combine the teachings of Brown into Sadeghi as modified in order to enable a quantum computer to operate more efficiently, correct/mitigate errors, and/or the like (Brown, para. 0083).
Claims 2, 10, and 16:
wherein the parametrized quantum circuit comprises single qubit rotation gates and two-qubit rotation gates. (Brown, para. 0077 and 0078: “For example, a gate may be configured to be performed for an angle a based on a function result b that takes the classical call response (or content thereof) as input such that if a = vi or is in a range vo < a < vi, the gate is performed for a first angle (e.g., to accomplish a qubit rotation of a first angle) and if a = V2 or is in a range vi < a < V2, the gate is performed for a second angle (e.g., to accomplish a qubit rotation of a second angle), where the first angle and the second angle are different from one another.” “In another example, parameterized single and/or multiple (e.g., two) qubit gates are implemented by expanding Rzz(9) gates in terms of ZZM;I Rz(0) ZZMax, and general single qubit gates are implemented by expanding the gates in terms of their respective Euler decompositions with Hadamard gates inserted to provide the respective Euler decompositions in terms of (only) Rz(9)”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Brown into Sadeghi, as modified, as set forth above with respect to claim 1.
Claims 4, 12, and 18:
The method of claim 1, wherein the set of variational parameters is initially selected randomly. (Sadeghi, para. 0098: “The sample values may be a predetermined starting value based on known properties of the problem, may be provided by another routine, may be randomly selected, or may be provided using other techniques as are known in the art.”)
Claims 5, 13, and 19:
The method of claim 1 , wherein the mixing coefficient is in the initial iteration is between 0.5 and 1. (Sadeghi, para. 0011: “where HE is the evolution Hamiltonian, HP is the problem Hamiltonian, HD is the delocalization Hamiltonian, and A(t), B(t) are coefficients that can control the rate of evolution, and typically lie in the range [0,1].” Examiner notes Sadeghi teaches a mixing coefficient between 0 and 1 which includes between 0.5 and 1).
Claim 15:
A hybrid quantum-classical computing system comprising non-volatile memory having a number of instructions stored therein which, when executed by one or more processors, causes the hybrid quantum-classical computing system to perform operations comprising: (Sadeghi, para. 0029: “According to an aspect, there is provided a system for updating a sample in an optimization algorithm, the system comprising at least one non-transitory processor-readable medium that stores at least one of processor executable instructions and data and at least one processor communicatively coupled to the least one non-transitory processor-readable medium, which, in response to execution of the at least one of processor executable instructions and data performs a method as described herein”)
selecting, by a classical computer, samples of a set of variables (Sadeghi, para. 0019: “The analog computer, as a provider of samples, is an example of a sample generator. The analog computer can be operated to provide samples from a selected probability distribution, the probability distribution assigning a respective probability of being sampled to each data point in the population”) and a target joint distribution of the set of variables; (Sadeghi, para. 0021: “New points that are accepted are ones that make for a probabilistic convergence to the target distribution. Convergence is guaranteed if the proposal and acceptance criteria satisfy detailed balance conditions, and the proposal satisfies the ergodicity requirement.”)
selecting, by the classical computer, a set of variational parameters to construct a parametrized quantum circuit; (Alcazar, para. 0081: “A given variational quantum circuit may be parameterized in a suitable device-specific manner. More generally, the quantum gates making up a quantum circuit may have an associated plurality of tuning parameters. For example, in embodiments based on optical switching, tuning parameters may correspond to the angles of individual optical elements.”)
executing iterations, each iteration comprising: setting, by a system controller, a quantum processor in an initial state, (Alcazar, para. 0086: “Quantum annealing starts with the classical computer 254 generating an initial Hamiltonian 260 and a final Hamiltonian 262 based on a computational problem 258 to be solved, and providing the initial Hamiltonian 260, the final Hamiltonian 262 and an annealing schedule 270 as input to the quantum computer 252. The quantum computer 252 prepares a well-known initial state 266 (FIG. 2B, operation 264), such as a quantum-mechanical superposition of all possible states (candidate states) with equal weights, based on the initial Hamiltonian 260.”)
wherein the quantum processor comprises a plurality of trapped ions (Brown, para. 0088: “The illustrated example hybrid quantum-classical computing environment 100 comprises a quantum object confinement apparatus 70 (e.g., an ion trap, surface trap, Paul trap, and/or the like), in accordance with an example embodiment”), each of which has two frequency-separated states defining a qubit; (Brown, para. 00141: “For example, the one or more RTEs 310 may cause the quantum processor to perform one or more reading and/or measurement operations to determine, measure, and/or read the quantum state of one or more qubits.”)
applying, by the system controller, the parametrized quantum circuit to the quantum processor based on the set of the variational parameters (Alcazar, para. 0081, above), to transform the quantum processor from the initial state to a trial state; (Alcazar, para. 0086: “The quantum computer 252 starts in the initial state 266, and evolves its state according to the annealing schedule 270 following the time-dependent Schrodinger equation, a natural quantum-mechanical evolution of physical systems (FIG. 2B, operation 268).”)
measuring, by the system controller, an amplitude of the trial state (Alcazar, para. 0009: “Embodiments of the present invention implement a generative model based on Matrix Product States (MPS) to learn target distributions. MPS is a type of Tensor Network (TN) where the tensors are arranged in a one-dimensional geometry. Despise its simple structure, MPS can represent a large number of quantum states extremely well. Once the MPS form of the wavefunction W is chosen, learning can be achieved by adjusting parameters of the wavefunction such that the distribution represented by Born's rule is as close as possible to the data distribution” Examiner notes Alcazar teaches Born’s rule which includes measurement of a probability amplitude), to generate a trial joint distribution of the set of variables; and (Sadeghi, para. 0021: “New points that are accepted are ones that make for a probabilistic convergence to the target distribution. Convergence is guaranteed if the proposal and acceptance criteria satisfy detailed balance conditions, and the proposal satisfies the ergodicity requirement.” Examiner notes it is unclear how the act of measuring results in generation of a distribution—Sadeghi teaches necessarily measuring a proposal to assess convergence on a target distribution)
replacing, by the classical computer, the set of the variational parameters (Alcazar, para. 0081, above) with another set of variational parameters, if a difference between the generated trial joint distribution of the set of variables and an adaptive target joint distribution based on the target joint distribution and a mixing coefficient is more than a predetermined value; and (Sadeghi, para. 0030: “updated value for the ith variable based on the objective energy bias and the constraint energy bias, the updated value replacing the current value, incrementing a progress parameter, evaluating a termination criteria”)
outputting the set of the variational parameters, (Sadeghi, para. 0030: “and outputting a solution comprising the current values for the set of variables.”)
wherein the adaptive target joint distribution is a mixture of a uniform joint distribution of the set of variables with the target joint distribution, (Alcazar, para. 0042 and 0047: “Build an initial dataset, d0, with uniformity probability distribution” “Compute the cost for these two new bit strings, and recalculate the associated Boltzmann distribution. The new elements may be inside or outside of the current dataset, and a new normalization factor is required for the Boltzmann distribution.”) and
the mixing coefficient is decreased in each iteration. (Sadeghi, para. 0025 and 0038: “In order to allow some flexibility in solving while also ensuring that constraints are satisfied, a penalty value may be assigned to the constraints, allowing the weight that the constraints are given to be varied, such that the constraint may be violated in some circumstances (such as at the start of a simulated annealing). When adding constraints to an objective function, a penalty value for the constraint may need to be selected without any guidance as to an appropriate penalty value in the given problem” “the method comprising receiving a sample from an optimization, determining an energy value for one or more constraint functions, evaluating feasibility of the sample, if the sample is not feasible, increasing a penalty value, if the sample is feasible, decreasing a penalty value, and returning a penalty value to an optimization algorithm.” Examiner notes Sadeghi teaches a penalty value i.e. mixing coefficient which decreases when a sample is feasible where Alcazar, para. 0086 teaches iterative computing with biases i.e. such as a penalty value).
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Alcazar into Sadeghi, as modified, as set forth above with respect to claim 1.
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Brown into Sadeghi, as modified, as set forth above with respect to claim 1.
Claims 3, 7, 11, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sadeghi, in view of Alcazar, in view of Brown and further in view of Paul V. Klimov et al. (“Quantum entanglement at ambient conditions in a macroscopic solid-state spin ensemble.” Sci. Adv.1,e1501015(2015).DOI:10.1126/sciadv.1501015; hereinafter, “Klimov”)
Claim 3, 11, and 17:
wherein in the initial state, each register comprising a plurality of qubits and representing one of the set of variables is in a maximally entangled state. (Klimov, abstract: “We optically initialize 103 identical registers in a 40-μm3 volume
0.95
-
0.07
+
0.05
(with fidelity) and deterministically prepare them into the maximally entangled Bell states (with 0.88 ± 0.07 fidelity).”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Klimov into Sadeghi as modified. Klimov teaches entanglement for quantum computers, quantum-communication networks, and high-precision sensors. One of ordinary skill would have been motivated to combine the teachings of Klimov into Sadeghi in order to develop quantum algorithms for quantum technologies (Klimov, abstract).
Claim 7:
A hybrid quantum-classical computing system, comprising: a quantum processor comprising a plurality of trapped ions, each of the trapped ions having two hyperfine states defining a qubit; (Klimov, fig. 1: “A hybrid two-qubit register comprising a PL6 color-center defect’s intrinsic electron spin and a nearby 29Sinuclear spin. The PL6 defect, whose physical structure is unknown, is depicted as a pyramid to indicate its known C3v symmetry. (B) The hybrid system forms an atom-like state with an optical, fine, and hyperfine structure.”)
one or more lasers configured to emit a laser beam, which is provided to trapped ions in the quantum processor; (Brown, para. 0033: “In an example embodiment, the controller further comprises or is in communication with one or more voltage source drivers and one or more laser drivers and controlling operation of the one or more components of the quantum processor comprises controlling operation of the one or more voltage source drivers and controlling operation of the one or more laser drivers.”)
a classical computer configured to: select samples of a set of variables (Sadeghi, para. 0019: “The analog computer, as a provider of samples, is an example of a sample generator. The analog computer can be operated to provide samples from a selected probability distribution, the probability distribution assigning a respective probability of being sampled to each data point in the population”) and a target joint distribution of the set of variables; (Sadeghi, para. 0021: “New points that are accepted are ones that make for a probabilistic convergence to the target distribution. Convergence is guaranteed if the proposal and acceptance criteria satisfy detailed balance conditions, and the proposal satisfies the ergodicity requirement.”)
select a set of variational parameters to construct a parametrized quantum circuit; (Alcazar, para. 0081: “A given variational quantum circuit may be parameterized in a suitable device-specific manner. More generally, the quantum gates making up a quantum circuit may have an associated plurality of tuning parameters. For example, in embodiments based on optical switching, tuning parameters may correspond to the angles of individual optical elements.”)
execute iterations, each iteration comprising: instructing a system controller to set the quantum processor in an initial state; (Alcazar, para. 0086: “Quantum annealing starts with the classical computer 254 generating an initial Hamiltonian 260 and a final Hamiltonian 262 based on a computational problem 258 to be solved, and providing the initial Hamiltonian 260, the final Hamiltonian 262 and an annealing schedule 270 as input to the quantum computer 252. The quantum computer 252 prepares a well-known initial state 266 (FIG. 2B, operation 264), such as a quantum-mechanical superposition of all possible states (candidate states) with equal weights, based on the initial Hamiltonian 260.”)
instructing the system controller to apply the parametrized quantum circuit to the quantum processor based on the set of the variational parameters (Alcazar, para. 0081, above), to transform the quantum processor from the initial state to a trial state; (Alcazar, para. 0086: “The quantum computer 252 starts in the initial state 266, and evolves its state according to the annealing schedule 270 following the time-dependent Schrodinger equation, a natural quantum-mechanical evolution of physical systems (FIG. 2B, operation 268).”)
instructing the system controller to measure an amplitude of the trial state (Alcazar, para. 0009: “Embodiments of the present invention implement a generative model based on Matrix Product States (MPS) to learn target distributions. MPS is a type of Tensor Network (TN) where the tensors are arranged in a one-dimensional geometry. Despise its simple structure, MPS can represent a large number of quantum states extremely well. Once the MPS form of the wavefunction W is chosen, learning can be achieved by adjusting parameters of the wavefunction such that the distribution represented by Born's rule is as close as possible to the data distribution” Examiner notes Alcazar teaches Born’s rule which includes measurement of a probability amplitude), to generate a trial joint distribution of the set of variables; and (Sadeghi, para. 0021: “New points that are accepted are ones that make for a probabilistic convergence to the target distribution. Convergence is guaranteed if the proposal and acceptance criteria satisfy detailed balance conditions, and the proposal satisfies the ergodicity requirement.” Examiner notes it is unclear how the act of measuring results in generation of a distribution—Sadeghi teaches necessarily measuring a proposal to assess convergence on a target distribution)
replacing the set of the variational parameters (Alcazar, para. 0081, above) with another set of variational parameters, if a difference between the generated trial joint distribution of the set of variables and an adaptive target joint distribution based on the target joint distribution and a mixing coefficient is more than a predetermined value; and
output the set of the variational parameters, (Sadeghi, para. 0030: “updated value for the ith variable based on the objective energy bias and the constraint energy bias, the updated value replacing the current value, incrementing a progress parameter, evaluating a termination criteria”)
wherein the adaptive target joint distribution is a mixture of a uniform joint distribution of the set of variables with the target joint distribution (Alcazar, para. 0042 and 0047: “Build an initial dataset, d0, with uniformity probability distribution” “Compute the cost for these two new bit strings, and recalculate the associated Boltzmann distribution. The new elements may be inside or outside of the current dataset, and a new normalization factor is required for the Boltzmann distribution.”) , and the mixing coefficient is decreased in each iteration. (Sadeghi, para. 0025 and 0038: “In order to allow some flexibility in solving while also ensuring that constraints are satisfied, a penalty value may be assigned to the constraints, allowing the weight that the constraints are given to be varied, such that the constraint may be violated in some circumstances (such as at the start of a simulated annealing). When adding constraints to an objective function, a penalty value for the constraint may need to be selected without any guidance as to an appropriate penalty value in the given problem” “the method comprising receiving a sample from an optimization, determining an energy value for one or more constraint functions, evaluating feasibility of the sample, if the sample is not feasible, increasing a penalty value, if the sample is feasible, decreasing a penalty value, and returning a penalty value to an optimization algorithm.” Examiner notes Sadeghi teaches a penalty value i.e. mixing coefficient which decreases when a sample is feasible where Alcazar, para. 0086 teaches iterative computing with biases i.e. such as a penalty value).
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Alcazar into Sadeghi as set forth above with respect to claim 1.
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Brown into Sadeghi, as modified, as set forth above with respect to claim 1.
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Klimov into Sadeghi, as modified, as set forth above with respect to claim 3.
Claims 6, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sadeghi, in view of Alcazar, in view of Brown and further in view of Dobsicek, et. al. (“Arbitrary accuracy iterative phase estimation algorithm as a two qubit benchmark”, arXiv:quant-ph/0610214v3 12 Jul 2007; hereinafter “Dobsicek”)
Claims 6, 14, and 20:
The method of claim 1, wherein the mixing coefficient is decreased by between 0.01 and 0.1 in each iteration. (Dobsicek “Considering the dominating effect of dephasing, we find that the number of repetitions grows quickly with the desired number of bits 𝑚, 𝑁𝑘∝𝑒2∣𝛼∣2𝑚𝛤𝜑/𝜆. In Fig. 5 we plot the total number of measurements 𝑁𝑡𝑜𝑡=∑𝑘𝑁𝑘 needed to obtain 2⩽𝑚⩽11bits of the phase 𝛼, with an error probability 𝜀<0.05. For a realistic dephasing rate of 1%–10% percent of the qubit-qubit coupling (0.01<𝛤𝜑/𝜆<0.1), between 5 and 8 binary digits of 𝛼 can be extracted with less than 104 measurements.”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Dobsicek into Sadeghi as modified. Dobsicek teaches use of a phase estimation algorithm (PEA) to characterize (benchmark) qubit circuits. One of ordinary skill would have been motivated to combine the teachings of Dobsicek into Sadeghi in order to implement a benchmarking system where the number of accurate binary digits can be used as a benchmark for multiqubit implementations (Dobsicek, pg. 030306-4).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Sadeghi, in view of Alcazar, in view of Brown, in view of Klimov and further in view of Olmschenk, et. al. (“Quantum Logic Between Distant Trapped Ions”, arXiv:0907.1702v1 [quant-ph] 10 Jul 2009; hereinafter, “Olmschenk”)
Claim 8:
The hybrid quantum-classical computing system of claim 7, wherein each of the trapped ions is 171
Y
b
+
having the 2
S
1
/
2
hyperfine states. (Olmschenk, sec. 3 and table 1: “Finally, the spin-1/2 nucleus of 171Yb+ allows for simple, fast, and efficient preparation and detection of the ground state hyperfine levels.61”
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Olmschenk into Sadeghi as modified. Olmschenk teaches the experimental implementation of a heralded photon-mediated quantum gate between remote ions. One of ordinary skill would have been motivated to combine the teachings of Olmschenk into Sadeghi as modified in order to realize quantum computation through the employment of this gate to perform a teleportation protocol between two ions separated by a distance of about one meter (Olmschenk, abstract).
Claim 9:
The hybrid quantum-classical computing system of claim 7, wherein each of the trapped ions is one selected from Be+, ca+, Sr+, Mg+, Ba+, Zn+, Hg+, Cd+. (Olmschenk, sec. 3 and table 1: “The hydrogen-like ions that have been directly cooled and manipulated for applications in quantum information include Ba+,48,49,50Be+,51Ca+,52,53,54,55,56 Cd+,57Mg+,58,59Sr+,29andYb+.60,61 In Table 1 various properties of some atomic ions are compared”)
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine the teachings of Olmschenk into Sadeghi, as modified, as set forth above with respect to claim 8.
Prior Art
Reagor, et al. (US 11954562 B2) teaches a control system in a quantum computing system assigns subsets of qubit devices in a quantum processor to respective cores
Conclusion
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/STL/Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147
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