DETAILED ACTION
This action is in response to the amendments filed on June 18th, 2026. A summary of this action:
Claims 1-3, 5-11, 13-16 have been presented for examination.
Claims 1-3, 5-11, 13-16 are objected to because of informalities
Claims 1-3, 5-11, 13-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mathematical concept without significantly more.
Claim(s) 1, 3, 5-9, 11, 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over BMW, “SENSOR POSITIONS OPTIMIZATION - BMW Quantum Computing Challenge”, July 13th 2021, URL: cs795(dot)cs(dot)odu(dot)edu/papers/210713_UC4_Sensors(dot)pdf in view of Uotila, Valter et al.. New Angles from Right Range: Optimizing Car Sensor Positioning with D-Wave Hybrid Quantum Computers. Dec. 2nd, 2021. GitHub Repo. URL: github(dot)com/valterUo/Quantum-computing-drafts/blob/main/sensor_bmw/main_3D_connecting_Dwave_Leap(dot)ipynb in further view of Quinones, Miguel Paredes, and Catarina Junqueira. "Modeling linear inequality constraints in quadratic binary optimization for variational quantum eigensolver." arXiv preprint arXiv:2007.13245 (2020).
Claim(s) 2 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over BMW, “SENSOR POSITIONS OPTIMIZATION - BMW Quantum Computing Challenge”, July 13th 2021, URL: cs795(dot)cs(dot)odu(dot)edu/papers/210713_UC4_Sensors(dot)pdf in view of Uotila, Valter et al.. New Angles from Right Range: Optimizing Car Sensor Positioning with D-Wave Hybrid Quantum Computers. Dec. 2nd, 2021. GitHub Repo. URL: github(dot)com/valterUo/Quantum-computing-drafts/blob/main/sensor_bmw/main_3D_connecting_Dwave_Leap(dot)ipynb in further view of Quinones, Miguel Paredes, and Catarina Junqueira. "Modeling linear inequality constraints in quadratic binary optimization for variational quantum eigensolver." arXiv preprint arXiv:2007.13245 (2020), wherein a term has its meaning explained/a showing that a characteristic not disclosed is inherent (MPEP § 2131.01) by Hughes, C., Isaacson, J., Perry, A., Sun, R.F., Turner, J. (2021). What Is a Qubit?. In: Quantum Computing for the Quantum Curious. Springer, Cham as well as National Academy of Engineering 2019. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2018 Symposium. Washington, DC: The National Academies Press.
This action is Final
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 Arguments/Amendments
Regarding the objections and § 112 rejections
Withdrawn in view of amendment. New objections as necessitated by amendment.
Regarding the § 101 Rejection
Maintained, updated as necessitated by amendment.
With respect to remarks at prong 1, see ¶ 72. The value of the result of a math calculation in textual form is a math concept, i.e. a math calculation, and its result of the calculation. Furthermore, it does not improve the quantum computer, rather it merely uses it as a tool to do a math concept. In addition, it does not recite any actual step of “physically configuring” the sensors – rather, and per the specification, it’s a math concept. See rejection to clarify.
With respect to prong 2, limitation (f), per the rejection and above, is a math calculation in textual form. Limitation (d) is mere instructions to use a computer and a quantum computer to do the abstract idea – neither the claims nor the specification convey that this invention is a new novel quantum computer, i.e. the quantum computer aspect is not inventive, but generic, e.g. there is no structure associated with it, and see ¶ 19: “According to one embodiment herein, the method is executed through a quantum computer. The quantum computer perform calculations based on the probability of an object's state before it is measured, instead of just 1 s or Os.” – a generically described “quantum computer” to do math calculations. Its mere instructions to invoke generic computer technology as a tool to perform an abstract idea. See ¶ 64 to further clarify.
Also, a claim to a complex math concept is still directed to a math concept.
Regarding the § 102/103 Rejection
Maintained, updated as necessitated by amendment.
With respect to the amendments and supporting remarks, see rejection below for how it is rejected. Also, remarks are not addressing the rejection – see Quinones, which teaches a variational quantum eigensolver algorithm, which is an example of a VQA and VQIA. Note § 3.1 ¶ 1: “The VQE is a hybrid algorithm that has a quantum part [the VQA] and a classical part [the VQIA] [16]. It is a type of near-term algorithm that uses noisy quantum computers to calculate expectation values of a minimum energy state . Originally, the VQE was used in quantum chemistry to approximate the minimum []state of energy of a quantum system represented by a Hamiltonian using the Variational theorem.” - see remained of § 3.1 for further details on this.
Claim Objections
Claims 1-3, 5-11, 13-16 are objected to because of the following informalities:
Claim 3: “when a value of the critical index wk is one, the point k is to cover” – Examiner suggests amending to “to be covered” or the like, i.e. to convey that point k is to be covered for when w = 1 = it must be/”required” to be covered by the sensor. Parallel claim objected to for similar reason.
Claim 8: “wherein after each iteration, better variational state is obtained, that provides lower energy and better plurality of sensor configuration.” – Examiner suggests a more clear phrasing, e.g. “where after each iteration, a better variational state is obtained as compared to a prior iteration, that provides a lower energy and better plurality of sensor configurations as the prior iteration” See ¶ 76 to clarify
The claims have numerous issues with antecedent basis. The Examiner suggests amending the claims such that the first recitation of each distinct element uses articles such as “a”/”an”, later recitations referring back to the same distinct element uses articles such as “the”/”said”, to use disambiguating modifiers (e.g., first, second, etc.) when there are multiple distinct elements with the same base term, and that the use of modifiers for each distinct element is kept consistent. Below is a non-exhaustive list of examples of these issues:
Claim 1, last limitation: “a specific sensor…of the sensor” – however, there is “a sensor” previously recited as well that “the sensor” may refer to. Given the context of the claim, the Examiner infers this is intended to refer back to the “specific sensor”.
Claim 3: “a value” is repeated twice, and given the context of the claim the Examiner suggests “a kth value of the critical index wk”, and refer back to “the kth value” – to ensure clarity expressly in the claim of which value it is referring to (the value of the weight at point k), or a similar such limitation
Claim 3: point k was previously recited in claim 1, but it does not refer back to it
Claim 5: “a specific position and in a specific orientation” is previously recited in claim 1 but it does not refer back to it, furthermore it is linked to the “plurality of sensor configurations” in claim 1, not the “plurality of sensors” – given the context of the claim, the Examiner infers claim 5 is intended to refer back to further limiting the elements in limitation (c) of claim 1, not the plurality of sensor configurations
Claim 7 recites “a minimum energy state of a Hamiltonian” – claim 1 already recites “a lowest energy state of a Hamiltonian”, which is the same element, i.e. the lowest/minimum energy state – Examiner suggests in view of ¶ 24 amending these to be the same – see ¶ 71 as well
Similar objections for parallel claims
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 5-11, 13-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mathematical concept without significantly more.
Step 1
Claim 1 is directed towards the statutory category of a process.
Claim 9 is directed towards the statutory category of an apparatus.
Claims 9, and the dependents thereof, are rejected under a similar rationale as representative claim 1, and the dependents thereof.
Step 2A – Prong 1
The claims recite an abstract idea of a mathematical concept. See MPEP § 2106.04(a)(2).
The mathematical concept recited in claim 1 is:
Limitation (c) is math calculations in textual form.
Limitation (f) is math calculations in textual form, but do it on a computer. See ¶ 62.
Under the broadest reasonable interpretation, the claim recites a mathematical concept – the above limitations are steps in a mathematical concept such as mathematical relationships, mathematical formulas or equations, and mathematical calculations. If a claim, under its broadest reasonable interpretation, is directed towards a mathematical concept, then it falls within the Mathematical Concepts grouping of abstract ideas. In addition, as per MPEP § 2106.04(a)(2): “It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)”
See MPEP § 2106.04(a)(2).
As such, the claims recite a mathematical concept.
Step 2A, prong 2
The claimed invention does not recite any additional elements that integrate the judicial exception into a practical application. Refer to MPEP §2106.04(d).
The following limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f), including the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”:
The system and processor of claim 9 are considered as mere instructions to use a computer and generic computer components to perform an abstract idea. To clarify, see MPEP § 2106.04(a)(2)(III)(C) for the shift register of Gottschalk, see MPEP § 2106.05(f) for TLI communications, and see the instant specification, e.g. ¶ 74: “In the variational quantum algorithm (VQA), the energy expectation value of Hamiltonian is obtained on a quantum computer by computing the exact gradient, Similarly, in the variational quantum-inspired algorithm (VQIA), the energy expectation value of Hamiltonian is obtained on a classical computer”
The following limitations are adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g):
Limitations (a-b) are mere data gathering
Limitations (d-e), given their generality, are mere instructions to “apply it” with generic commonplace algorithms to achieve a desired result with no restriction on how this is done and mere instructions to invoke generic computer components as a tool to perform the abstract idea, as well as an insignificant computer implementation. To clarify, these do not recite any steps of the algorithms at all. These are also considered as generally linking to a particular technological environment for similar reasons.
A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. See MPEP § 2106.04(d).
MPEP 2106.04(II)(A)(2) “…Instead, under Prong Two, a claim that recites a judicial exception is not directed to that judicial exception, if the claim as a whole integrates the recited judicial exception into a practical application of that exception. Prong Two thus distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception…Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."). For a claim reciting a judicial exception to be eligible, the additional elements (if any) in the claim must "transform the nature of the claim" into a patent-eligible application of the judicial exception, Alice Corp., 573 U.S. at 217, 110 USPQ2d at 1981, either at Prong Two or in Step 2B” and MPEP § 2106(I): “Mayo, 566 U.S. at 80, 84, 101 USPQ2dat 1969, 1971 (noting that the Court in Diamond v. Diehr found “the overall process patent eligible because of the way the additional steps of the process integrated the equation into the process as a whole,”” – and see MPEP § 2106.05(e).
To further clarify, MPEP § 2106.04(II)(A)(1): “Alice Corp., 573 U.S. at 216, 110 USPQ2d at 1980 (citing Mayo, 566 US at 71, 101 USPQ2d at 1965). Yet, the Court has explained that ‘‘[a]t some level, all inventions embody, use, reflect, rest upon, or apply laws of nature, natural phenomena, or abstract ideas,’’ and has cautioned ‘‘to tread carefully in construing this exclusionary principle lest it swallow all of patent law” See also Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335, 118 USPQ2d 1684, 1688 (Fed. Cir. 2016) ("The ‘directed to’ inquiry, therefore, cannot simply ask whether the claims involve a patent-ineligible concept, because essentially every routinely patent-eligible claim involving physical products and actions involves a law of nature and/or natural phenomenon").”
As a point of clarity, RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." discussed in MPEP § 2106.04(II)(A)(2) as well as MPEP § 2106.04(I): “Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a new abstract idea is still an abstract idea") (emphasis in original).
The claimed invention does not recite any additional elements that integrate the judicial exception into a practical application. Refer to MPEP §2106.04(d).
Step 2B
The claimed invention does not recite any additional elements/limitations that amount to significantly more.
The following limitations are merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f), including the “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more”:
The system and processor of claim 9 are considered as mere instructions to use a computer and generic computer components to perform an abstract idea. To clarify, see MPEP § 2106.04(a)(2)(III)(C) for the shift register of Gottschalk, see MPEP § 2106.05(f) for TLI communications, and see the instant specification, e.g. ¶ 74: “In the variational quantum algorithm (VQA), the energy expectation value of Hamiltonian is obtained on a quantum computer by computing the exact gradient, Similarly, in the variational quantum-inspired algorithm (VQIA), the energy expectation value of Hamiltonian is obtained on a classical computer”
The following limitations are adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g):
Limitations (a-b) are mere data gathering
Limitations (d-e), given their generality, are mere instructions to “apply it” with generic commonplace algorithms to achieve a desired result with no restriction on how this is done and mere instructions to invoke generic computer components as a tool to perform the abstract idea, as well as an insignificant computer implementation. To clarify, these do not recite any steps of the algorithms at all. These are also considered as generally linking to a particular technological environment for similar reasons.
Furthermore, such algorithms are WURC for solving optimization problems with a quantum computer, wherein these conventionally use hybrid computing arrangements with a classical computer and a quantum computer (to clarify, these are generally called hybrid quantum-classical algorithms, or variations thereof, e.g. a hybrid quantum algorithm) – see:
Choi, Jaeho, Seunghyeok Oh, and Joongheon Kim. "The useful quantum computing techniques for artificial intelligence engineers." 2020 international conference on information networking (ICOIN). IEEE, 2020. Abstract and § I, then see §§ II-IV
Gacon, Julien, Christa Zoufal, and Stefan Woerner. "Quantum-enhanced simulation-based optimization." 2020 IEEE International conference on quantum computing and engineering (QCE). IEEE, 2020. § I: “We would like to point out that other quantum algorithms have already been investigated in the context of optimization. Common examples are the Variational Quantum Eigensolver (VQE) or Quantum Approximate Optimization Algorithm (QAOA) [10]–[13]. These methods are applicable to Quadratic Unconstrained Binary Optimization (QUBO), where the problem can be mapped to an Ising Hamiltonian.”
Harwood, Stuart, et al. "Formulating and solving routing problems on quantum computers." IEEE transactions on quantum engineering 2 (2021): 1-17. § I ¶¶ 3-4
Quinones, Miguel Paredes, and Catarina Junqueira. "Modeling linear inequality constraints in quadratic binary optimization for variational quantum eigensolver." arXiv preprint arXiv:2007.13245 (2020). §§ 2 and 3.1
Sim, Sukin. Algorithm development for near-term quantum computers. Diss. Harvard University, 2021. Abstract ¶¶ 1-2 and see § 1.2 incl.: “…In fact, since the introductions of VQE and QAOA, the variational algorithm framework has been widely applied for various computational tasks, such as quantum simulation, combinatorial optimization, factoring, circuit compilation, generative modeling, data classification, and more5,20,70,95,187,198,207. For a more complete list of existing variational algorithms, we refer the readers to the tables in Ref. 20. In the following subsection, we describe the framework shared by all variational quantum algorithms in order to motivate the ways in which we can build upon and improve current algorithms...” – see remaining parts of § 1 and its subsections to further clarify. Also see § 1.2.1.7: “In variational algorithms, an objective function value is computed using a hybrid scheme”, see § 2.1 ¶¶ 1-3 including: “A particular class of algorithms that maximizes the use of such pre-threshold hardware is the hybrid quantum-classical (HQC) algorithm, which strategically divides computational tasks between quantum and classical resources. A prime example of a HQC algorithm is the variational quantum eigensolver (VQE), used to compute the ground states of molecular systems…” -
Tang, Hao, et al. "Teaching quantum information technologies and a practical module for online and offline undergraduate students." arXiv preprint arXiv:2112.06548 (2021). Abstract, then see page 3, col. 2, ¶ 2. See fig. 1 as well.
Ushijima-Mwesigwa, Hayato, et al. "Multilevel combinatorial optimization across quantum architectures." ACM Transactions on Quantum Computing 2.1 (2021): 1-29. §§ 2-2.3
de Zoete, J. "A practical quantum algo-rithm for solving structural optimization problems: a proof-of-concept!." Aerospace Engineering, Delft University of Technology, Delft, Netherlands. §§ 2.4-2.5
In addition, the above insignificant extra-solution activities are also considered as well-understood, routine, and conventional activities, as discussed in MPEP § 2106.05(d):
Limitations (a-b) are WURC in view of MPEP § 2106.05(d)(II); for additional evidence see the below relied upon BMW reference in the § 102 rejection, along with the Uotila reference (e.g. “We took the values for these parameters from the data set criticallity_grid which BMW provided.” in the global parameters section) and other references on this BMW quantum computing challenge, which shows that these datasets were provided by BMW in a quantum computing challenge to multiple parties, i.e. merely claiming retrieving the data in data sets provided by a third-party (BMW) is not an inventive concept. Also, see MPEP § 2106.05(a)(I): “Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:… vii. Providing historical usage information to users while they are inputting data, in order to improve the quality and organization of information added to a database, because "an improvement to the information stored by a database is not equivalent to an improvement in the database’s functionality," BSG Tech LLC v. Buyseasons, Inc., 899 F.3d 1281, 1287-88, 127 USPQ2d 1688, 1693-94 (Fed. Cir. 2018); and”
As such, the claims are directed to a mathematical concept without significantly more.
Regarding the dependent claims
Claim 2 is generally linking to the technological environment of quantum computers as well as part of the mere instructions to do this abstract idea on a computer, followed by math calculations in textual form
Claim 3 is rejected under a similar rationale as the mere data gathering of the independents
Claim 5 – more mere data gathering that is WURC in view of the above evidence
Claim 6 – further clarifying on the math concept
Claim 7 – further adding to the math concept - see ¶¶ 35-36, 62-63, 72, i.e. math calculations in textual form with the math equation of the Hamiltonian in textual form
Claim 8 – merely adding a step of mere data gathering to specify how many repetitive calculations are to be done, also WURC – MPEP § 2106.05(d)(II): “ii. Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.");” to the abstract idea of the math concept (see specification citations above for claim 1 and 7, and see ¶ 74)
Remaining dependents rejected under similar rationales as their representative claims above
As such, the claims are directed towards a mathematical concept without significantly more.
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.
Claim(s) 1, 3, 5-9, 11, 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over BMW, “SENSOR POSITIONS OPTIMIZATION - BMW Quantum Computing Challenge”, July 13th 2021, URL: cs795(dot)cs(dot)odu(dot)edu/papers/210713_UC4_Sensors(dot)pdf in view of Uotila, Valter et al.. New Angles from Right Range: Optimizing Car Sensor Positioning with D-Wave Hybrid Quantum Computers. Dec. 2nd, 2021. GitHub Repo. URL: github(dot)com/valterUo/Quantum-computing-drafts/blob/main/sensor_bmw/main_3D_connecting_Dwave_Leap(dot)ipynb in further view of Quinones, Miguel Paredes, and Catarina Junqueira. "Modeling linear inequality constraints in quadratic binary optimization for variational quantum eigensolver." arXiv preprint arXiv:2007.13245 (2020).
To clarify on the date of the BMW reference, note the date in the URL: “210713”, i.e. July 13th, 2021.
Should further evidence of this being otherwise accessible to the public before the instant effective filing date, see MPEP § 2121.01: “The statutory phrase "printed publication" has been interpreted to mean that before the critical date the reference must have been sufficiently accessible to the public interested in the art; dissemination and public accessibility are the keys to the legal determination whether a prior art reference was "published." Constant v. Advanced Micro-Devices, Inc., 848 F.2d 1560, 1568, 7 USPQ2d 1057, 1062 (Fed. Cir. 1988).”, so see:
BMW. Welcome to the BMW Quantum Computing Challenge! - Supplierthon series. Accessed via the WayBack machine, archive date Nov. 18th, 2021. URL: crowd-innovation(dot)bmwgroup(dot)com/servlet/hype/IMT?documentTableId=7025714350976893092&userAction=Browse&templateName=&documentId=740c3d47812c470b208a495d85f18206 - Section “Who can join”: “Participation is open to anyone who has interest to apply his or her knowledge and skills on quantum algorithms to real-world problems, and is part of a legal entity. This could be, for instance, a research group, a start-up or an innovative company. A background in quantum computing is very beneficial, but not a strict requirement. Find more details on eligibility in Terms and Conditions” and section “How to participate”: “There are a few steps for you to participate. First register to crowd-innovation.bmwgroup.com. Then choose a use case you want to explore and get working! You can submit your idea for the first round until the 24th of September 2021.”
Schuetz, Martin et al. Exploring industrial use cases in the BMW Group Quantum Computing Challenge. July 2021. Amazon Blog. URL: aws(dot)amazon(dot)com/blogs/quantum-computing/exploring-industrial-use-cases-in-the-bmw-quantum-computing-challenge/ - “Today, the BMW Group launched a global open innovation challenge focused on discovering potential quantum computing solutions for real-world use cases: The BMW Group Quantum Computing Challenge…The BMW Group Quantum Computing Challenge is open to participants from research groups and companies worldwide. You can register now, and prepare to submit your entries before the September 24 deadline…We worked closely with BMW to define appropriate use cases for the competition, from the many computational challenges that exist inside complex automotive engineering, manufacturing, and logistics domains…Based on these, the team developed the following four challenges that have the potential to drive real-world innovation for BMW…Use Case: Vehicle Sensor Placement…The challenge is to find an optimal configuration of sensors for a given vehicle such that the vehicle can reliably detect obstacles indifferent driving scenarios – using quantum computing or nature-inspired optimization approaches…To learn more and to register for the challenge, visit the BMW Group’s Open Innovation website . Each use case is distinct and requires unique approaches, so we provide more detailed information on submission requirements foreach of them.”
Goeders, James et al. Winners announced in the BMW Group Quantum Computing Challenge. Dec. 9th, 2021. URL: aws(dot)amazon(dot)com/blogs/quantum-computing/winners-announced-in-the-bmw-group-quantum-computing-challenge/ - “Use Case: Vehicle Sensor Placement: Accenture Modern vehicles come with sensors to help provide safety and convenience to drivers. Vehicles need these sensors to gather data from as large a portion of their surroundings as possible, but each additional sensor adds costs. The goal of this use case was to optimize the positions of sensors to allow for maximum coverage while keeping the required number of sensors as low as possible. The Accenture team provided a holistic workflow for prototyping, from user input all the way to the final result, involving a detailed pipeline for (i) the definition of the input data, (ii) pre-processing steps, (iii) optimization of the underlying MaxCover problem and (iv) visualization of the results with an advanced sensor distribution visualization app. For the actual optimization problem, the Accenture team developed a general framework including four classes of algorithmic approaches. While classical custom algorithms delivered the best results today, the framework from the Accenture team comes with plugins for quantum methods to be elaborated in the future…Submissions were received from more than 70 teams globally, spanning from quantum software startups to enterprise companies. The jury evaluated the submissions for comprehensibility, feasibility, scalability, innovation, and benefit for the BMW Group. From the initial submissions, 15 finalists were selected before the final selection of the four winners.”
AWS. Re:Invent. Nov. 29th-Dec. 3, 2021. URL: d1(dot)awsstatic(dot)com/events/reinvent/2021/Use_case_insights_from_the_BMW_Group_quantum_challenge_QTC304(dot)pdf – see slide 7-8, in particular the “Sensor position optimization” in 8. Slide 9 shows the “Total Submissions” from around the planet, with “20%” being for the “Sensor position optimization”, note slide 11: “Definition of appropriate use cases for the competition, from the many computational challenges within the BMW Group”, then see slides 13-14
BMW. Quantum computing: BMW Group launches “Quantum Computing Challenge” in collaboration with AWS to crowd-source innovation. Press Release. July 13th, 2021. URL: www(dot)press(dot)bmwgroup(dot)com/global/article/detail/T0337884EN/quantum-computing:-bmw-group-launches-%E2%80%9Cquantum-computing-challenge%E2%80%9D-in-collaboration-with-aws-to-crowd-source-innovation?language=en - “Starting today, researchers, start-ups and pioneering companies from the global quantum computing community can propose solutions for specific industrial challenges to the BMW Group Quantum Computing Challenge… Experts from the BMW Group have identified over 50 challenges at various stages of the value chain where quantum computing could provide a potential benefit in the future… The Quantum Computing Challenge will focus on four specific challenges where quantum computing could deliver an advantage over classical computing methods:… Optimisation of sensor positions for automated driving functions… Registration begins today, and the deadline for submissions is 24 September 2021, after which they will be examined and judged by a panel of experts. A final event will take place in December 2021 [see AWS above], where the top entrants will have the opportunity to pitch their solutions to the panel of expert judges…”
To further clarify, see:
Uotila, Valter et al.. New Angles from Right Range: Optimizing Car Sensor Positioning with D-Wave Hybrid Quantum Computers. Dec. 2nd, 2021. GitHub Repo. URL: github(dot)com/valterUo/Quantum-computing-drafts/blob/main/sensor_bmw/main_3D_connecting_Dwave_Leap(dot)ipynb – GitHub repository as a result of this “BMW Quantum Computing Challenge” with code and the like on the sensor placement optimization challenge. Do note in the global parameters: “We took the values for these parameters from the data set criticallity_grid which BMW provided.” – to clarify, see the relied upon BMW reference in the § 103 rejection below, page 6 ¶ 1 last two sentences: “To overcome this numerical challenge, one may map a vehicle's surrounding area onto a grid of 3D points and assign specific importance (critical index) of being observed to each grid point. The values for each point are collected through all scenarios, which allows validating an optimal sensor setup by comparing its resulting field of view.” – i.e. the “criticallity_grid which BMW provided”. Also see in Uotila the section on “Initializing allowed sensor positions on car”, and compare with section “Sensor position” and fig. 3 in the BMW challenge problem statement relied upon below, etc. i.e. Uotila work bears a strong resemblance to a coding implementation to solve BMWs problem statement.
QCI. QCI Qatalyst Selected by BMW Group and Amazon Web Services as a Finalist in the Quantum Computing Challenge. Press Release. Nov. 9th, 2021. URL: globenewswire(dot)com/news-release/2021/11/09/2330437/0/en/QCI-Qatalyst-Selected-by-BMW-Group-and-Amazon-Web-Services-as-a-Finalist-in-the-Quantum-Computing-Challenge(dot)html – “Quantum Computing Inc. (Nasdaq: QUBT), a leader in bridging the power of classical and quantum computing, announced that its Qatalyst ready-to-run quantum software was selected as one of three finalists for the second and final round of the BMW Group and Amazon Web Services (AWS) Quantum Computing Challenge for the Vehicle Sensor Placement use case… The Vehicle Sensor Placement use case challenges participants to find optimal configurations of sensors for a given vehicle so that it can reliably detect obstacles in different driving scenarios – using quantum computing or nature-inspired optimization approaches. The number of sensors per car is expected to increase significantly as autonomous driving becomes more common. Vehicles need sensors to gather data from as large a portion of their surroundings as possible, but each sensor adds additional costs, so optimizing the sensor placement uses genetic algorithms. The goal of the challenge is to use quantum computing techniques to optimize the positions of sensors, enabling maximum coverage while keeping costs to a minimum.”
Pramanik, Sayantan, et al. "Optimization of sensor-placement on vehicles using quantum-classical hybrid methods." 2022 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2022. Abstract: “The paper presents a quantum method to optimize the placement of sensors on the surface of a vehicle. The problem, as posted in the BMW Quantum Computing Challenge 2021, is to arrive at the optimal positions and configurations (type and orientation) of the sensors on the vehicle surface that maximizes coverage of the Region of Interest (RoI), while minimizing the total cost of the selected sensors. The dataset contains approximately 100,000 points in the RoI, with defined measures of criticality (ranging between 0 to 1), distributed over a volume of approximately 40,000 cubic metres around the vehicle. The types of sensors, their coverage parameters and costs are inputs to the problem” – then see § 1, incl.: “The Sensor Placement problem of BMW Quantum Challenge [1], necessitates the coverage of the space surrounding a vehicle, aptly named as the Region of Interest (RoI), through the use of various sensors placed on the vehicle-surface. The sensors have parameters, such as the range which signifies how far the sensor can look and gauge the environment, as well as its horizontal and vertical angular sweep, αH and αV , respectively (as shown Figure 2 of the challenge statement [1])… Further, the sensor can be placed at a particular position and at a given angle with respect to the surface, all of which determine its Field of View (FoV), which typically takes the shape of an elliptical cone [2]. A point in the RoI is said to be covered only if it lies within the FoV of at least one of the selected sensors… From a top-view, 2D visualization of the RoI (see Figure 4 of the challenge document [1]), it is clear that the points in regions pertaining to the four sides - front, back, left and right - of the vehicle are mutually exclusive, with only a minor overlap between the front and the left sides.” – in particular in § 1, note it is describing citations # 1 and 4, and see it’s description of reference # 1 for fig. 2 and 4, wherein the relied upon BMW reference fig. 2 matches the description (including variable names) for fig. 2 and eq. 4 in the relied upon BMW reference; and see BMW fig. 4, in view of the description of Pramanik of fig. 4 as “From a top-view, 2D visualization of the RoI…”
In summary, the relied upon BMW reference was, for a prima facie case in view of the above preponderance of evidence, publicly accessible to at least “the portion of the public concerned with the art would know of the invention. In re Bayer, 568 F.2d 1357, 196 USPQ 670 (CCPA 1978)” (MPEP § 2128.01(I), see other citations as well in this MPEP section), along with the evidence above showing the minimum amount of people in the public disseminated was to the “20%” of participants who participated in the BMW-AWS Quantum Computing 2021 challenge on “Sensor Position optimization” (AWS, Re:Invent, 2021, slide 9), with very little barrier for entry for the challenge: Schuetz, 2021, as cited above: “The BMW Group Quantum Computing Challenge is open to participants from research groups and companies worldwide. You can register now, and prepare to submit your entries before the September 24 deadline… To learn more and to register for the challenge, visit the BMW Group’s Open Innovation website”, and knowledge of the challenge was disseminated in multiple sources (as cited above).
As one last point of clarity on this, see Pramanik, 2022, as cited above, citation # 1 in the URL: “210713_UC4_Sensors.pdf”, wherein the Old Dominion University (odu.edu) version of it has the same file name in its URL: “210713_UC4_Sensors” for a pdf document.
Regarding Claim 1
BMW teaches:
A method (100) for optimizing sensor positions in an autonomous vehicle comprising the steps of:
Limitation (a) - see (BMW, section “Evaluation of a sensor setup”: “To overcome this numerical challenge, one may map a vehicle's surrounding area onto a grid of 3D points and assign specific importance (critical index) of being observed to each grid point. The values for each point are collected through all scenarios, which allows validating an optimal sensor setup by comparing its resulting field of view.” – to clarify, see fig. 1: “VISUALISATION OF REGIONS OF INTEREST WHICH NEED TO BE COVERED BY A SENSOR CONFIGURATION IN THREE-DIMENSIONAL GRID. EACH GRID POINT REPRESENTS THE CRITICAL INDEX (𝒄𝒊) AS A FUNCTION OF THREE SPACE COORDINATES.”
Limitation (b) - see (BMW, section “Sensor configuration”: “The problem solution represents one specific sensor configuration that the algorithm evaluates as optimal. A sensor configuration is defined by a number of 𝑛 sensors placed on the vehicle surface, where 𝑛 is bound by a given maximum allowed number of sensors 𝐾. 𝑛 ∈ [0, 𝐾] (1) For each of the 𝑛 sensors multiple variables (degrees of freedom) must be set: • T: sensor's characteristic (i.e. type, range, field of view, price); • P: sensor's position; • O: sensor's orientation.” – and see the other sensor sections, incl.: “Sensor characteristics”: “In a simplified way, one individual sensor can be defined by three characteristics: type, geometry of its field of view, price.” – see the subsections “Range & field of view”; “Sensor position”; and “Price” to further clarify, incl. seeing fig. 2, also see the subsection “Sensor Orientation” for more details
Limitation (c) - see BMW, as cited above, then see section “Evaluating the covered regions of interest”: “As mentioned in the section above, evaluating a particular sensor configuration consists of computing and comparing the RoI grid covered by the sensor setup. It can be done by assigning to RoI grid points 𝑅 a boolean variable 𝐶 (1 - grid point is covered by sensor setup, 0 - otherwise). Such discrete coverage model can be computed based on the field of view characteristics (see equation (4)). Computing the sum over the resulting array and dividing it by the sum of the RoI grid points gives a measurement of how good the setup covers the full set of RoIs.” – followed by “Summary of the optimization problem”: “To summarize the statements above, the optimization problem of finding the maximum RoIs coverage with minimal cost can be described by following objective function:… where • 𝑉𝑐𝑜𝑣𝑒𝑟 is a value measuring the efficiency of RoIs coverage as defined in (6); • 𝐶 represents the cost of the sensor setup as defined in (5); • 𝐴 and 𝐵 are positive-valued weights.” – page 2 clarifies on this, incl. fig. 1 as well as “The problem presented in this document thus consists in finding an optimal configuration of sensors and can be mainly defined by two aspects: • an optimal configuration demands a specific sensor positioning: every important area/object of the vehicle's surroundings must be detected with the highest possible certainty. Some regions may even require a redundant cover (i.e. two sensors of different types covering the same part); • the cost of optimal configuration must be reduced to a minimum”
The following is the distinction from BMW, but it would have been obvious in view of Uotila and Choi:
Limitations (d-f) - BMW, as was cited above, note this is a “Quantum Computing Challenge” – to clarify, section “Research focus”: “Regarding the computational complexity of the sensor configuration problem, the main goal is to develop a new efficient and robust numerical model to approach it by means of quantum or hybrid quantum-classical computing. Regarding the computational complexity of the sensor configuration problem, the main goal is to develop an efficient and robust numerical model and to approach it by means of quantum or hybrid quantum-classical computing. The principal metrics for the algorithm evaluation will be the quality of approximation (number of different characteristics taking into account, potential to scale) and the level of innovation with respect to the established classical/quantum techniques…”
as taken in view of Uotila, see that this is for the “BMW Quantum Computing Challenge -- Round 2” for “Optimizing Car Sensor Positioning with D-Wave Hybrid Quantum Computers”, i.e. the problem statement of BMW, see sections such as “Global parameters”, “Importing sensors”, “Initializing allowed sensor positions on car”, etc. to clarify on that then see the section: “Constructing quadratic unconstrained binary optimization model” – see the code, see the description accompanying it, e.g. in the subsection on “Constraint 1:…” see: “Every binary quadratic function which is part of the model contains four parameters: linear terms, quadratic terms, offset (constant) and variable type. Variable type is always BINARY since we are using QUBO. If we use Ising, we set variable type to be SPIN.” – i.e. this section (see the other constraints) are mapping the optimization problem to a “quadratic unconstrained binary optimization model” for solution on a quantum computer.
But, Uotila does not teach using variational algorithms (the VQA and VQIA) to solve the optimizations but rather uses (see section “Solve QUBO”) a “LeapHybridSampler” from D-wave
However, POSITA would have found this obvious to do, when BMW in view of Uotila was taken in further view of Quinones:
Quinones, see § 2 ¶¶ 1-2, incl.: “Heuristics based on quantum computing have recently been developed to solve the unconstrained version of the LCQBO, called Quadratic Unconstrained Binary Optimization (QUBO). Some of these heuristics are the Variational Quantum Eigensolver (VQE) [13, 22] and its special form called Quantum Approximation Algorithm (QAOA)[6]. The VQE was initially applied on quantum chemistry problems, such as the problem of searching for molecular ground states (minimum energy of the system). Once QUBO problems can be mapped as an ising formulation, which is one of the formulations for the Hamiltonian calculation, it is possible to solve the QUBO with VQE [18] and to approximate the solution of an LCQBO by using penalisation techniques, reducing it to QUBO [8, 14]”, then see the remaining parts of § 2 including: “Taking this into account, this paper aims to address this difficulty when VQE is formulated for LCQBO with the construction of special variational forms in a way that the search domain attain specific constraints.” - see §§ 3-4 to further clarify, for as summarized in § 2: “This paper is organized as follows, In Section 3 we presents a review of the VQE in order to understand the position of our contribution on the development of an efficient VQE solver for LCQBO problems, and circuit cost criteria to compare other Ansatze. Then in Section 4 the development of the variational forms in order to attain certain constraints for the search space domain is explained.” – see § 3.1 in particular for more details, including ¶ 1 and algorithm 3.1, and the following paragraphs for more details
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from BMW on the problem statement for the quantum computing challenge on sensor optimization with the teachings from Uotila on one of the proposed solutions to the challenge’s problem statement. The motivation to combine would have been that “The structure of the code is simple: first we import the data, then we create the binary variables as described in the proposal. After that we construct the three objective functions. Finally, we send the total objective functions to D-wave's quantum computer which solves it. The final result is printed.” - i.e. it’s a “simple” solution to the problem.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings from BMW, as taken in view of Uotila, on the BMW quantum computing challenge with the “simple” solution to it by Uotila with the teachings from Quinones on solving QUBO and LCQBO optimization problems with VQE. The motivation to combine would have been that:
(1) For the VQE solution for regular QUBOs: “Heuristics based on quantum computing have recently been developed to solve the unconstrained version of the LCQBO, called Quadratic Unconstrained Binary Optimization (QUBO). Some of these heuristics are the Variational Quantum Eigensolver (VQE) [13, 22] and its special form called Quantum Approximation Algorithm (QAOA)[6]. The VQE was initially applied on quantum chemistry problems, such as the problem of searching for molecular ground states (minimum energy of the system). Once QUBO problems can be mapped as an ising formulation, which is one of the formulations for the Hamiltonian calculation, it is possible to solve the QUBO with VQE [18] and to approximate the solution of an LCQBO by using penalisation techniques, reducing it to QUBO [8, 14].” (§ 2 ¶ 2)
(2) For the VQE for solving LCQBOs: “The main advantage of the proposed methodology is that the number of parameters on the variational form remain constant and depend on the number of variables that appear on the constraints. Moreover, this variational form always produces feasible solutions for the represented constraints differing from penalization techniques commonly used to translate constrained problems into unconstrained one.” (abstract)
Regarding Claim 3
BMW, in view of Uotila and Quinones teaches this – see BMW, as cited above, eg.. see fig. 1, fig. 4., and: “To overcome this numerical challenge, one may map a vehicle's surrounding area onto a grid of 3D points and assign specific importance (critical index) of being observed to each grid point. The values for each point are collected through all scenarios, which allows validating an optimal sensor setup by comparing its resulting field of view.” – note the index goes from 0 to 1; section “Sensor type”: “For example, all regions of interest with a critical index above 𝑐𝑖 ≥ 0.7 need to be covered by at least two sensors of a different type.” -also Uotila: “We took the values for these parameters from the data set criticallity_grid which BMW provided.”
Regarding Claim 4
BMW, in view of Uotila and Quinones teaches this – see BMW, as cited above, see fig. 1, then see section “Sensor position” for details along with fig. 3, i.e.: “Sensor position is mainly constrained by the vehicle's surface geometry, as it must be positioned directly on the vehicle's body (except its specific parts such as windows, lights, etc.). Furthermore, some external factors can be considered as well. For example, due to the risk of the sensor getting covered by dirt or snow, a minimum height for sensor position should be defined. To simplify the definition of possible sensor position, one or multiple polygons can be drawn over the surface area in the vehicle's front, side, and back view. As a first approximation, these rectangles can be used as flat surfaces for sensor placement. The rectangles are set by their four corners and the allowed sensor types. The regions visualised in figure 3 are defined similarly to those from the provided test data.” - then, see Uotila, for “Initializing allowed sensor positions on car” – in particular, see the code near the end – note that the “bonnet” can have “cameras” and “lidar”, etc. (see the remaining parts of the code)– i.e. this is specifying for each region of a plurality of regions of the vehicle what sensors are permitted and what types of sensors, wherein these sensors would provide coverage for all sides of a vehicle
Regarding Claim 6
BMW, in view of Uotila and Quinones teaches this – see BMW, summary of optimization problem equation (do note the prior equations which define the variables here, e.g. eq. 6 for Vcover and “c” is eq. 5), and see eq. 2-4 to further clarify on the “T, P, O” [the combinations of n, m, and o]):
PNG
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214
682
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Greyscale
Regarding Claim 7
BMW, in view of Uotila and Quinones teaches this – see BMW, as discussed above, taken in view of Uotila for formulating it into a QUBO, taken in further view of Quinones of solving QUBOs and the like with VQE – to clarify, Quinones, ¶ 2: “The VQE was initially applied on quantum chemistry problems, such as the problem of searching for molecular ground states (minimum energy of the system). Once QUBO problems can be mapped as an ising formulation, which is one of the formulations for the Hamiltonian calculation, it is possible to solve the QUBO with VQE [18] and to approximate the solution of an LCQBO by using penalisation techniques, reducing it to QUBO [8, 14]” – see Quinones § 3.1 for more details
Regarding Claim 8
BMW, in view of Uotila and Quinones teaches this – see BMW, as discussed above, taken in view of Uotila for formulating it into a QUBO, taken in further view of Quinones of solving QUBOs and the like with VQE – in particular, in Quinones, see algorithm 3.1 for the “while classical optimization condition do…end while”, e.g. fig. 11: “Comparison of evolution of the objective function for the LAP instance (eqs. (36) to (40)), using TVF, 2-Local and QAOA”) – x-axis is the total number of “iterations”)
Regarding Claim 9, 11, 13-16
Rejected under a similar rationale as the parallel claims above.
Claim(s) 2 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over BMW, “SENSOR POSITIONS OPTIMIZATION - BMW Quantum Computing Challenge”, July 13th 2021, URL: cs795(dot)cs(dot)odu(dot)edu/papers/210713_UC4_Sensors(dot)pdf in view of Uotila, Valter et al.. New Angles from Right Range: Optimizing Car Sensor Positioning with D-Wave Hybrid Quantum Computers. Dec. 2nd, 2021. GitHub Repo. URL: github(dot)com/valterUo/Quantum-computing-drafts/blob/main/sensor_bmw/main_3D_connecting_Dwave_Leap(dot)ipynb in further view of Quinones, Miguel Paredes, and Catarina Junqueira. "Modeling linear inequality constraints in quadratic binary optimization for variational quantum eigensolver." arXiv preprint arXiv:2007.13245 (2020), wherein a term has its meaning explained/a showing that a characteristic not disclosed is inherent (MPEP § 2131.01) by Hughes, C., Isaacson, J., Perry, A., Sun, R.F., Turner, J. (2021). What Is a Qubit?. In: Quantum Computing for the Quantum Curious. Springer, Cham as well as National Academy of Engineering 2019. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2018 Symposium. Washington, DC: The National Academies Press.
Regarding Claim 2 and 10 (claim 2 as representative)
BMW, in view of Uotila and Quinones teaches:
The method (100) according to Claim 1, wherein the method (100) is executed through a quantum computer; and wherein the quantum computer performs calculations based on the probability of an object's state before it is measured (Uotila: introduction: “Finally, we send the total objective functions to D-wave's quantum computer which solves it. The final result is printed” – also see Quinones abstract: “The methodology is implemented in a real quantum computer for two known optimization problems: the Facility Location Problem and the Set Packing Problem” and fig. 6 and 9
Also, see Quinonens, e.g. page 2, ¶ 2: “qubits”, page 3, ¶¶ 1-2, § 4 ¶ 1, § 4.1 eq. 8, etc. – i.e. it doing the calculations with “qubits” on a quantum computer
The only distinction here is neither of the above references expressly state that quantum computers do calculations based on the probabilities as claimed, but that is merely part of using qubits on a QC for a quantum algorithm – see Hughes, §.2.1.1: “In order to work with qubits, it is useful to know how one can express quantum mechanical states with mathematical formulas… The state of a qubit is enclosed in the right half of an angled bracket, called the “ket”. A qubit, |, could be in a |0or |1state or even a superposition of both |0and |1. This is written as with α and β called the amplitudes of the states (Fig. 2.2).… Amplitudes are very important because they give us the probability of finding the particle in that specific state when performing a measurement. The probability of measuring the particle in state |0is |α|2, and the probability of measuring the particle in state |1is |β|2. Why is it squared? The short answer is that it gives the correct experimental predictions for this choice of representation” – e.g. see § 2.1.2 for a coin flip analogy to describe this, and footnote 1: “We know that quantum physics is probabilistic from experiments. The squared coefficients are needed to make a quantity that behaves like a probability distribution, i.e., it is a real number and positive. There cannot be a negative probability by definition.”
To clarify, see National Academy of Engineering, page 6, second to last paragraph: “In reality a quantum computer leverages entanglement between qubits and the probabilities associated with superpositions to carry out a series of operations (a quantum algorithm) such that certain probabilities are enhanced (i.e., those of the right answers) and others depressed, even to zero (i.e., those of the wrong answers). When a measurement is made at the end of a computation, the probability of measuring the correct answer should be maximized. The way quantum computers leverage probabilities and entanglement is what makes them so different from classical computers.”
Conclusion
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