IP Library › Granted Patent US 12,265,882
Granted Patent B2
US 12,265,882 · App. 17/200,388 · Granted Apr 1, 2025

Systems and methods for quantum computing based subset summing

Inventors: Mark D. Rahmes (Melbourne, FL); Thomas J. Billhartz (Melbourne, FL); Rachele Cocks (Colombia City, IN)
Assignee: Eagle Technology, LLC
G06N10/00G06F15/82G06N7/01G06N3/044G06N3/045G06N3/08G06N10/20
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Quick Facts
Patent No.
US 12,265,882
App. No.
17/200,388
Granted
Apr 1, 2025
Kind
B2
Abstract

Systems and methods for operating a quantum processor. The methods comprise: receiving a reward matrix at the quantum processor, the reward matrix comprising a plurality of values that are in a given format and arranged in a plurality of rows and a plurality of columns; converting, by the quantum processor, the given format of the plurality of values to a qubit format; performing, by the quantum processor, subset summing operations to make a plurality of row selections based on different combinations of the values in the qubit format; using, by the quantum processor, the plurality of row selections to determine a normalized quantum probability for a selection of each row of the plurality of rows; making, by the quantum processor, a decision based on the normalized quantum probabilities; and causing, by the quantum processor, operations of an electronic device to be controlled or changed based on the decision.

Claims (34)

1. A method for operating a system with a quantum processor, comprising:

performing the following operations by the quantum processor to determine a modulation type for a communication signal, wherein the modulation type identifies an analog or digital modulation scheme;

receiving a reward matrix at the quantum processor, the reward matrix comprising a plurality of values that are in a given format and arranged in a plurality of rows and a plurality of columns, wherein the plurality of values comprise identifiers for a plurality of machine learning algorithms and identifiers for a plurality of modulation classes;

converting, by the quantum processor, the given format of the plurality of values to a qubit format;

performing, by the quantum processor, subset summing operations to make a plurality of row selections based on different combinations of the values in the qubit format;

using, by the quantum processor, the plurality of row selections to determine a quantum probability for a selection of each row of the plurality of rows;

making, by the quantum processor, a decision about the modulation type based on the quantum probabilities; and

selectively using a communication link for communicating another communication signal based on the decision about the modulation type.

2. The method according to claim 1 , wherein each row of the reward matrix has a respective choice associated therewith.

3. The method according to claim 2 , wherein the respective choice comprises a respective action of a plurality of actions, a respective task of a plurality of tasks, a respective direction of a plurality of directions, a respective plan of a plurality of plans, a respective grid of a plurality of grids, a respective position of a plurality of positions, a respective acoustic ray trace of a plurality of acoustic ray traces, a respective tag of a plurality of tags, a respective path of a plurality of paths, a respective machine learning algorithm of a plurality of machine learning algorithms, a respective network node of a plurality of network nodes, a respective person of a group, a respective emotion of a plurality of emotions, a respective personality of a plurality of personalities, a respective business opportunity of a plurality of business opportunities, or a respective vehicle of a plurality of vehicles.

4. The method according to claim 1 , wherein the subset summing operations are implemented by a plurality of quantum adder circuits and a plurality of quantum comparator circuits.

5. The method according to claim 1 , wherein the subset summing operations comprises an operation in which a single value of the reward matrix is considered and which results in a selection of the row of the reward matrix in which the single value resides.

6. The method according to claim 1 , wherein the subset summing operations comprise an operation in which at least two values of the reward matrix are considered and which results in a selection of the row of the reward matrix in which a largest value of the at least two values resides.

7. The method according to claim 1 , wherein the subset summing operations comprise an operation in which a single negative value of the reward matrix is considered and which results in a selection of the row of the reward matrix which is different than the row of the reward matrix in which the single negative value resides.

8. The method according to claim 1 , wherein the subset summing operations comprise an operation in which a plurality of values in at least two columns and at least two rows are considered, and which results in a selection of the row of the reward matrix associated with a largest value of the plurality of values in at least two columns and at least two rows.

9. The method according to claim 1 , wherein the subset summing operations comprise an operation in which a plurality of values in at least two columns and at least two rows are considered, and which results in a selection of the row of the reward matrix associated with a largest sum of values in the at least two columns.

10. The method according to claim 1 , wherein the quantum processor causes the electronic device to transition operational states, change position, change location, change a navigation parameter, perform a particular task, change a resource allocation, use a particular machine learning algorithm to optimize wireless communications, or use a particular object classification scheme or trajectory generation scheme to optimize autonomous driving operations.

11. A quantum processor, comprising:

a circuit configured to determine a modulation type for a communication signal and control an electronic device, wherein the modulation type identifies an analog or digital modulation scheme:

receive a reward matrix comprising a plurality of values that are in a given format and arranged in a plurality of rows and a plurality of columns, wherein the plurality of values comprise identifiers for a plurality of machine learning algorithms and identifiers for a plurality of modulation classes;

convert the given format of the plurality of values to a qubit format;

perform subset summing operations to make a plurality of row selections based on different combinations of the values in the qubit format;

use the plurality of row selections to determine a quantum probability for a selection of each row of the plurality of rows;

make a decision about the modulation type based on the quantum probabilities; and

control operations of the electronic device to use a communication link for communicating another communication signal based on the decision about the modulation type.

12. The quantum processor according to claim 11 , wherein each row of the reward matrix has a respective choice associated therewith.

13. The quantum processor according to claim 12 , wherein the respective choice comprises a respective action of a plurality of actions, a respective task of a plurality of tasks, a respective direction of a plurality of directions, a respective plan of a plurality of plans, a respective grid of a plurality of grids, a respective position of a plurality of positions, a respective acoustic ray trace of a plurality of acoustic ray traces, a respective tag of a plurality of tags, a respective path of a plurality of paths, a respective machine learning algorithm of a plurality of machine learning algorithms, a respective network node of a plurality of network nodes, a respective person of a group, a respective emotion of a plurality of emotions, a respective personality of a plurality of personalities, a respective business opportunity of a plurality of business opportunities, or a respective vehicle of a plurality of vehicles.

14. The quantum processor according to claim 11 , wherein the subset summing operations are implemented by a plurality of quantum adder circuits and a plurality of quantum comparator circuits.

15. The quantum processor according to claim 11 , wherein the subset summing operations comprises an operation in which a single value of the reward matrix is considered and which results in a selection of the row of the reward matrix in which the single value resides.

16. The quantum processor according to claim 11 , wherein the subset summing operations comprise an operation in which at least two values of the reward matrix are considered and which results in a selection of the row of the reward matrix in which a largest value of the at least two values resides.

17. The quantum processor according to claim 11 , wherein the subset summing operations comprise an operation in which a single negative value of the a plurality of values of the reward matrix is considered and which results in a selection of the row of the reward matrix which is different than the row of the reward matrix in which the single negative value resides.

18. The quantum processor according to claim 11 , wherein the subset summing operations comprise an operation in which a plurality of values in at least two columns and at least two rows are considered, and which results in a selection of the row of the reward matrix associated with a largest value of the plurality of values in at least two columns and at least two rows.

19. The quantum processor according to claim 11 , wherein the subset summing operations comprise an operation in which a plurality of values in at least two columns and at least two rows are considered, and which results in a selection of the row of the reward matrix associated with a largest sum of values in the at least two columns.

20. The quantum processor according to claim 11 , wherein the quantum processor causes the electronic device to transition operational states, change position, change location, change a navigation parameter, perform a particular task, change a resource allocation, use a particular machine learning algorithm to optimize wireless communications, or use a particular object classification scheme or trajectory generation scheme to optimize autonomous driving operations.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 12, 2021
From: RAHMES, MARK D.; BILLHARTZ, THOMAS J.; COCKS, RACHELE
To: EAGLE TECHNOLOGY, LLC
Reel/Frame 055580/0124 →
Continuity (1)
Related Publication 20220300843A1 · Sep 22, 2022
References Cited (105)
US 6518018B1 · Szostak et al. · 2003 [cited by applicant]
US 7069282B2 · Rizzotto et al. · 2006 [cited by applicant]
US 7203715B2 · Rizzotto et al. · 2007 [cited by applicant]
US 7334008B2 · Branciforte et al. · 2008 [cited by applicant]
US 7359928B2 · Porto et al. · 2008 [cited by applicant]
US 7400282B2 · Tanaka et al. · 2008 [cited by applicant]
US 8077988B2 · Donoho · 2011 [cited by applicant]
US 8583903B2 · Freedman et al. · 2013 [cited by applicant]
US 9047571B2 · Miller et al. · 2015 [cited by applicant]
US 10176433B2 · Hastings et al. · 2019 [cited by applicant]
US 10423887B2 · Roetteler et al. · 2019 [cited by applicant]
US 10505524B1 · Cohen et al. · 2019 [cited by applicant]
US 10592216B1 · Richardson et al. · 2020 [cited by applicant]
US 10664761B2 · Haener et al. · 2020 [cited by applicant]
US 10666238B1 · Cohen et al. · 2020 [cited by applicant]
US 10713582B2 · Dadashikelayeh · 2020 [cited by applicant]
US 20030149511A1 · Rizzotto et al. · 2003 [cited by applicant]
US 20040059765A1 · Rizzotto et al. · 2004 [cited by applicant]
US 20040130956A1 · Porto et al. · 2004 [cited by applicant]
US 20040162640A1 · Branciforte et al. · 2004 [cited by applicant]
US 20040179622A1 · Calabro et al. · 2004 [cited by applicant]
US 20060224547A1 · Ulyanov et al. · 2006 [cited by applicant]
US 20070120727A1 · Tanaka et al. · 2007 [cited by applicant]
US 20080140746A1 · Papageorgiou et al. · 2008 [cited by applicant]
US 20080140749A1 · Amato et al. · 2008 [cited by applicant]
US 20110161638A1 · Freedman et al. · 2011 [cited by applicant]
US 20140192388A1 · Miller et al. · 2014 [cited by applicant]
US 20150006443A1 · Rose · 2015 [cited by examiner]
US 20160202195A1 · Wittwer et al. · 2016 [cited by applicant]
US 20170147695A1 · Shih · 2017 [cited by applicant]
US 20170330101A1 · Hastings et al. · 2017 [cited by applicant]
US 20180038973A1 · Ascough et al. · 2018 [cited by applicant]
US 20180038975A1 · Rahmes et al. · 2018 [cited by applicant]
US 20180144262A1 · Roetteler et al. · 2018 [cited by applicant]
US 20190340532A1 · Ducore et al. · 2019 [cited by applicant]
US 20190361675A1 · Haener et al. · 2019 [cited by applicant]
US 20190362270A1 · Haener et al. · 2019 [cited by applicant]
US 20200097859A1 · Hu et al. · 2020 [cited by applicant]
US 20200125402A1 · Griffin et al. · 2020 [cited by applicant]
US 20200134107A1 · Low et al. · 2020 [cited by applicant]
US 20200169317A1 · Rahmes et al. · 2020 [cited by applicant]
US 20200169458A1 · Rahmes et al. · 2020 [cited by applicant]
US 20200198138A1 · Nagayama et al. · 2020 [cited by applicant]
US 20200202247A1 · Hu et al. · 2020 [cited by applicant]
US 20200202249A1 · Hastings · 2020 [cited by applicant]
US 20200218518A1 · Gambetta et al. · 2020 [cited by applicant]
Ramezani et al., (“Machine Learning Algorithms in Quantum Computing: A Survey ” Published 2020 -total 8 pages IEEE (Year: 2020). [cited by examiner]
Abualsaud, K., Mahmuddin, M., Hussein, R., & Mohamed, A. (Jul. 2013). Performance evaluation for compression- accuracy trade-off using compressive sensing for EEG-based epileptic seizure detection in wireless tele-monit… [cited by applicant]
Baraniuk, R. G., Goldstein, T., Sankaranarayanan, A. C., Studer, C., Veeraraghavan, A., & Wakin, M. B. (2017). Compressive video sensing: algorithms, architectures, and applications. IEEE Signal Processing Magazine, 34(… [cited by applicant]
Hanif, M. S., & Bilal, M. (2019). Competitive residual neural network for image classification. ICT Express. [cited by applicant]
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). [cited by applicant]
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [cited by applicant]
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). [cited by applicant]
Nawaz, T., Marcenaro, L., & Regazzoni, C. S. (2017). Cyclostationary-based jammer detection for wideband radios using compressed sensing and artificial neural network. International Journal of Distributed Sensor Network… [cited by applicant]
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014). Learning and transferring mid-level image representations using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern … [cited by applicant]
O'Shea, T. J., Roy, T., & Clancy, T. C. (2018). Over-the-air deep learning-based radio signal classification. IEEE Journal of Selected Topics in Signal Processing, 12(1), 168-179. [cited by applicant]
Pandian, R., Vigneswara, T., & Kumari, S. L. (2016). Effects of Decomposition Levels of Wavelets in Image Compression Algorithms. Journal of Biomedical Sciences, 5(4), 29. [cited by applicant]
Ronneberger, O., Fischer, P., & Brox, T. (Oct. 2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234-2… [cited by applicant]
Rajendran, S., Meert, W., Giustiniano, D., Lenders, V., & Pollin, S. (2018). Deep learning models for wireless signal classification with distributed low-cost spectrum sensors. IEEE Transactions on Cognitive Communicati… [cited by applicant]
Ramjee, S., Ju, S., Yang, D., Liu, X., Gamal, A. E., & Eldar, Y. C. (2019). Fast deep learning for automatic modulation classification. arXiv preprint arXiv:1901.05850. [cited by applicant]
Schilling, A., Matzner, C., Rietsch, J., Gerum, R., Schulze, H., & Krauss, P. (2018). How deep is deep enough ?—Quantifying class separability in the hidden layers of deep neural networks. arXiv preprint arXiv:1811.0175… [cited by applicant]
Sejdic, E., Orovic, I., & Stankovic, S. (2018). Compressive sensing meets time-frequency: an overview of recent advances in time-frequency processing of sparse signals. Digital signal processing, 77, 22-35. [cited by applicant]
Sharma, D. K., Mishra, A., & Saxena, R. (2010). Analog & Digital Modulation Techniques: An overview. International Journal of Computing Science and Communication Technologies, 3(1), 2007. [cited by applicant]
Sharma, S. K., Lagunas, E., Chatzinotas, S., & Ottersten, B. (2016). Application of compressive sensing in cognitive radio communications: A survey. IEEE communications surveys & tutorials, 18(3), 1838-1860. [cited by applicant]
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. [cited by applicant]
Talukder, K. H., & Harada, K. (2010). Haar wavelet-based approach for image compression and quality assessment of compressed image. arXiv preprint arXiv:1010.4084. [cited by applicant]
Tembine, H. (2019). Deep learning meets game theory: Bregman-based algorithms for interactive deep generative adversarial networks. IEEE transactions on cybernetics. [cited by applicant]
Wang, S., Sun, Z., Liu, S., Chen, X., & Wang, W. (Oct. 2014). Modulation classification of linear digital signals based on compressive sensing using high-order moments. In 2014 European Modelling Symposium (pp. 145-150)… [cited by applicant]
Junyi Liu, Bohua Zhan, Shuling Wang. Shenggang Ying, Tao Liu, Yangjia Li, Mingsheng Ying, Naijun Zhan. International Conference on Computer Aided Verification. Formal Verification of Quantum Algorithms Using Quantum Hoa… [cited by applicant]
Zhou, L., & Man, H. (Nov. 2013). Distributed automatic modulation classification based on cyclic feature via compressive sensing. In MILCOM 2013-2013 IEEE Military Communications Conference (pp. 40-45). IEEE. [cited by applicant]
Ali, S. Coming to a Battlefield Near You: Quantum Computing, Artificial Intelligence, & Machine Learning's Impact on Proportionality. [cited by applicant]
Berendsen, R. G. (2019). The Weaponization of Quantum Mechanics: Quantum Technology in Future Warfare. US Army School of Advanced Military Studies Fort Leavenworth United States. [cited by applicant]
Cariolaro, Gianfranco, Quantum Communications, (2015), Springer International Publishing. [cited by applicant]
Cuccaro, S. A., Draper, T. G., Kutin, S. A., & Moulton, D. P. (2004). A new quantum ripple-carry addition circuit. arXiv preprint quant-ph/0410184. [cited by applicant]
Farhi, E., & Harrow, A. W. (2016). Quantum supremacy through the quantum approximate optimization algorithm. arXiv preprint arXiv:1602.07674. [cited by applicant]
Flitney, A. P., & Abbott, D. (2002). An introduction to quantum game theory. Fluctuation and Noise Letters, 2(04), R175-R187. [cited by applicant]
Hilary, Jack D., (2019) Quantum Computing: An Applied Approach, Springer Nature Switzerland AG. [cited by applicant]
Iqbal, A., & Toor, A. H. Quantum mechanics gives stability to a Nash equilibrium. Physical Review A Phys Rev A, 65, 022306. [cited by applicant]
Jordan, J. D. (2007). Updating Optimal Decisions Using Game Theory and Exploring Risk Behavior Through Response Surface Methodology. [cited by applicant]
Kalogeras, D. (2013). The quantum theory in decision making. Journal of Computations y Modelling, 3(4), 61-81. [cited by applicant]
Koiliaris, K., & Xu, C. (2019). Faster pseudopolynomial time algorithms for subset sum. ACM Transactions on Algorithms (TALG), 15(3), 1-20. [cited by applicant]
Iffiton, M. H., & Sakallah, K. A. (2008). Algorithms for computing minimal unsatisfiable subsets of constraints. Journal of Automated Reasoning, 40(1), 1-33. [cited by applicant]
Moll, N., Barkoutsos, P., Bishop, L. S., Chow, J. M., Cross, A., Egger, D. J., . . . & Kandala, A. (2018). Quantum optimization using variational algorithms on near-term quantum devices. Quantum Science and Technology, … [cited by applicant]
Oliveira, D. S., & Ramos, R. V. (2007). Quantum bit string comparator: circuits and applications. Quantum Comput. Comput, 7(1), 17-26. [cited by applicant]
Pac, A. B. (2010). Row generation techniques for approximate solution of linear programming problems (Doctoral dissertation, bilkent university). [cited by applicant]
Preskill, John, Quantum Computing in the NISQ era and beyond, 2018, California Institute of Technology. [cited by applicant]
Rahmes, M., Wilder, K., Yates, H., & Fox, K. (2013). Near real time discovery and conversion of open source Information to a reward matrix. WMSCI 2013, 12. [cited by applicant]
Ruffinelli, D., & Baran, B. (2017). Linear nearest neighbor optimization in quantum circuits: a multiobjective perspective. Quantum Information Processing, 16(9), 220. [cited by applicant]
Takahashi, Y., Tani, S., & Kunihiro, N. (2009). Quantum addition circuits and unbounded fan-out. arXiv preprint arXiv:0910.2530. [cited by applicant]
Ulyanov, S. V., Litvintseva, L. V., Ulyanov, S. S., Takahashi, K., & Panfilov, A. L. (2004). Computational intelligence with quantum game's approach and robust decision-making in communication information uncertainty. I… [cited by applicant]
Wayne, L. (2004). Winston (2004). Operations Research, Applications and Algorithms. Duxbury Press. [cited by applicant]
Wendin, G. (2017). Quantum information processing with superconducting circuits: a review. Reports on Progress in Physics, 80(10), 106001. [cited by applicant]
Wolff, M., Wirsching, G., Huber, M., Graben, P. B., Romer, R., & Schmitt, I. (2018). A Fock Space Toolbox and Some Applications in Computational Cognition. Unpublished. Retrieved from http://rgdoi.net/10.13140/RG.2.2.10… [cited by applicant]
Yukalov, V. I., & Somnette, D. (2009). Processing information in quantum decision theory. Entropy, 11(4), 1073-1120. [cited by applicant]
Robert Rand, Jennifer Paykin, Steve Zdancewic. 14th International Conference on Quantum Physics and Logic (QPL). 2018. QWIRE Practice: Formal Verification of Quantum Circuits in Coq. [cited by applicant]
Robert Rand, Kesha Hietala, and Kartik Singhal. POPL 2020. Verified Quantum Computing. [cited by applicant]
Roch C, Phan T, Feld S, et al. A Quantum Annealing Algorithm for Finding Pure Nash Equilibria in Graphical Games. Computational Science—ICCS 2020. 2020;12142:488-501. Published May 25, 2020. [cited by applicant]
Piotrowski, Edward & Sladkowski, Jan. (2003). The next stage: quantum game theory. [cited by applicant]
Chiu Fan Lee and Neil F. Johnson (2002) “Let the Quantum Games Begin” Centre for Quantum Computation, Clarendon Laboratory, Oxford University Oct. 2, 2002. [cited by applicant]
E Farhi, J Goldstone, S Gutmann, A quantum approximate optimization algorithm (QAOA), arXiv preprint arXiv:1411.4028, 2014—arxiv.org. [cited by applicant]
Rahmes, Billhartz, Chester, Lau (2020) “Optimal Deep Learning Signal Classification with Wavelet Compressive Sensing” Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Aug. 24, 2020. [cited by applicant]
Rahmes, Billhartz, Cocks (2020) “A Quantum Computing Algorithm for Subset Summing Optimization” Interservice/ Industry Training, Simulation, and Education Conference (I/ITSEC), Sep. 24, 2020. [cited by applicant]
https://www.cei.se/quantum-computing-and-qc-assisted-communications. [cited by applicant]
M. Rahmes, R. Clouse, J. Virts, G. Yakimovicz, B. Rees and W. Talbert, “Cognitive mission planning and system orchestration,” 2017 International Conference on Computing, Networking and Communications (ICNC), 2017, pp. 7… [cited by applicant]
Daniel Evans (2018) “Dissecting a Quantum Program”, Seidenberg School of CSIS, Pace University, Pleasantville, New York, Proceedings of Student-Faculty Research Day, CSIS, Pace University, May 4, 2018. [cited by applicant]