IP Library › Granted Patent US 12,689,446
Granted Patent B2
US 12,689,446 · App. 18/466,889 · Granted Jul 21, 2026

Channel coding over quantum channels

Inventors: Stephen Magno DiAdamo (Munich, DE); Lakshika Rathi (Jodhpur, IN); Alireza Shabani (Los Angeles, CA)
Assignee: CISCO TECHNOLOGY, INC.
H04B10/70G06N10/60
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,689,446
App. No.
18/466,889
Filed
Sep 14, 2023
Granted
Jul 21, 2026
Kind
B2
Art Unit
2634
USPC
398/140
Abstract

A method of managing communication over a quantum channel. The method includes generating a first set of quantum channel codes configured to encode a message to obtain a quantum encoded message, supplying the first set of quantum channel codes to an encoder, generating a second set of quantum channel codes configured to decode the quantum encoded message, and supplying the second set of quantum channel codes to a decoder that is configured to operate with encoder across a channel, wherein the first set of quantum channel codes and the second set of quantum channel codes are derived using a machine learning model.

Claims (41)

1 . A method comprising:

generating a first set of quantum channel codes configured to encode a message comprising at least one qubit to obtain a quantum encoded message;

supplying the first set of quantum channel codes to an encoder;

generating a second set of quantum channel codes configured to decode the quantum encoded message; and

supplying the second set of quantum channel codes to a decoder that is configured to operate with the encoder across a channel,

wherein the first set of quantum channel codes and the second set of quantum channel codes are derived using a machine learning model, and

wherein the first set of quantum channel codes comprises two subsets of quantum channel codes for two encoding passes, and the second set of quantum channel codes comprises two subsets of quantum channel codes for two decoding passes.

2 . The method of claim 1 , wherein the first set of quantum channel codes and the second set of quantum channel codes are different from each other.

3 . The method of claim 1 , further comprising training the machine learning model based on a cross-entropy loss calculation.

4 . The method of claim 1 , wherein the channel behaves as at least one of a bit-flip channel, a depolarization channel, or a ρ=1 amplitude damping channel.

5 . The method of claim 1 , wherein the decoder is configured to employ entanglement-assisted communication decoding techniques.

6 . The method of claim 5 , wherein the channel behaves as at least one of a phase-flip channel, a depolarization channel, or a ρ=½ amplitude damping channel.

7 . The method of claim 1 , wherein the decoder operates as a joint-detection receiver.

8 . The method of claim 1 , wherein the decoder is configured to decode the quantum encoded message using a parameterized pooling circuit.

9 . The method of claim 1 , wherein the first set of quantum channel codes comprises at least one set of three parameters representative of an arbitrary rotation.

10 . The method of claim 1 , wherein the second set of quantum channel codes comprises at least one set of three parameters representative of an arbitrary rotation.

11 . A device comprising:

an interface configured to enable network communications;

a memory; and

one or more processors coupled to the interface and the memory, and configured to:

generate a first set of quantum channel codes configured to encode a message comprising at least one qubit to obtain a quantum encoded message;

supply the first set of quantum channel codes to an encoder;

generate a second set of quantum channel codes configured to decode the quantum encoded message; and

supply the second set of quantum channel codes to a decoder that is configured to operate with the encoder across a channel,

wherein the first set of quantum channel codes and the second set of quantum channel codes are derived using a machine learning model, and

wherein the first set of quantum channel codes comprises two subsets of quantum channel codes for two encoding passes, and the second set of quantum channel codes comprises two subsets of quantum channel codes for two decoding passes.

12 . The device of claim 11 , wherein the first set of quantum channel codes and the second set of quantum channel codes are different from each other.

13 . The device of claim 11 , wherein the one or more processors are configured to train the machine learning model based on a cross-entropy loss calculation.

14 . The device of claim 11 , wherein the channel behaves as at least one of a bit-flip channel, a depolarization channel, or a ρ=1 amplitude damping channel.

15 . The device of claim 11 , wherein the decoder is configured to employ entanglement-assisted communication decoding techniques.

16 . The device of claim 15 , wherein the channel behaves as at least one of a phase-flip channel, a depolarization channel, or a ρ=½ amplitude damping channel.

17 . The device of claim 11 , wherein the encoder and the decoder operate as a joint-detection receiver.

18 . One or more non-transitory computer readable storage media encoded with instructions that, when executed by a processor, cause the processor to:

generate a first set of quantum channel codes configured to encode a message comprising at least one qubit to obtain a quantum encoded message;

supply the first set of quantum channel codes to an encoder;

generate a second set of quantum channel codes configured to decode the quantum encoded message; and

supply the second set of quantum channel codes to a decoder that is configured to operate with the encoder across a channel,

wherein the first set of quantum channel codes and the second set of quantum channel codes are derived using a machine learning model, and

wherein the first set of quantum channel codes comprises two subsets of quantum channel codes for two encoding passes, and the second set of quantum channel codes comprises two subsets of quantum channel codes for two decoding passes.

19 . The one or more non-transitory computer readable storage media of claim 18 , wherein the first set of quantum channel codes and the second set of quantum channel codes are different from each other.

20 . The one or more non-transitory computer readable storage media of claim 18 , wherein the decoder is configured to employ entanglement-assisted communication decoding techniques.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 14, 2023
From: DIADAMO, STEPHEN MAGNO; RATHI, LAKSHIKA; SHABANI, ALIREZA
To: CISCO TECHNOLOGY, INC.
Reel/Frame 064900/0659 →
Continuity (1)
Related Publication 20250096906A1 · Mar 20, 2025
References Cited (43)
US 10552738B2 · Holt · 2020 [cited by examiner]
US 10552739B1 · Rausch · 2020 [cited by examiner]
US 20180174050A1 · Holt · 2018 [cited by examiner]
US 20180322388A1 · O'Shea · 2018 [cited by examiner]
US 20190188565A1 · O'Shea · 2019 [cited by examiner]
US 20200274554A1 · Aspuru-Guzik et al. · 2020 [cited by applicant]
US 20210383189A1 · Cong et al. · 2021 [cited by applicant]
US 20220263694A1 · Iscan · 2022 [cited by examiner]
US 20230110591A1 · Routt et al. · 2023 [cited by applicant]
US 20230123083A1 · Huangfu et al. · 2023 [cited by applicant]
US 20250096906A1 · DiAdamo · 2025 [cited by examiner]
US 20250148343A1 · Villalonga Correa · 2025 [cited by examiner]
CN 108809436A · 2018 [cited by examiner]
CN 115580355A · 2023 [cited by applicant]
WO WO2023287487A2 · 2023 [cited by examiner]
WO WO2023128603A1 · 2023 [cited by examiner]
WO WO2023230352A1 · 2023 [cited by examiner]
Rathi et al; Quantum Autoencoders for Learning Quantum Channel Codes ;Jul. 2023, aRxiv; pp. 1-6. (Year: 2023). [cited by examiner]
Monnet et al; Pooling techniques in hybrid quantum-classical convolutional neural networks—May 2023; IEEE, pp. 1-10 (Year: 2023). [cited by examiner]
Khatri, S., et al., “Information-theoretic aspects of the generalized amplitude damping channel,” arXiv: 1903.07747v2 [quant-ph], Jul. 10, 2020, 33 pages. [cited by applicant]
James, R. G., et al., “dit: a Phython package for discrete information theory,” The Journal of Open Source Software, DOI: 10.21105/joss.00738, May 31, 2018, 3 pages, https:// doi.org/10.21105/joss.00738. [cited by applicant]
Banaszek, K., et al., “Quantum Limits in Optical Communications,” Journal of Lightwave Technology, vol. 38, No. 10, DOI: 10.1109, May 15, 2020, 14 pages. [cited by applicant]
Bergholm, V., et al., “PennyLane: Automatic differentiation of hybrid quantum-classical computations,” https://arxiv.org/abs/1811.04968, Jul. 29, 2022, 18 pages. [cited by applicant]
Bondarenko, D., et al., “Quantum autoencoders to denoise quantum data,” https://arxiv.org/abs/1910.09169, Oct. 21, 2019, 8 pages. [cited by applicant]
Cerezo, M., et al., “Variational quantum algorithms,” https://arxiv.org/abs/2012.09265, Oct. 4, 2021, 33 pages. [cited by applicant]
Devetak, I., et al., “The capacity of a quantum channel for simultaneous transmission of classical and quantum information,” https://arxiv.org/abs/quant-ph/0311131, Oct. 21, 2004, 17 pages. [cited by applicant]
Frostig, R., et al., “Compiling machine learning programs via high-level tracing,” Systems for Machine Learning, https://mlsys.org/Conferences/doc/2018/146.pdf, Feb. 2018, 3 pages. [cited by applicant]
Guha, S., “Structured optical receivers to attain super additive capacity and the Holevo limit,” https://arxiv.org/abs/1101.1550v1, Jan. 7, 2011, 4 pages. [cited by applicant]
Holevo, A. S., “The Capacity of the Quantum Channel with General Signal States,” IEEE Transactions on Information Theory, Correspondence, IEEE Xplore, vol. 44, No. 1, Jan. 1998, 5 pages. [cited by applicant]
Huang, C-J., et al., “Realization of a quantum autoencoder for lossless compression of quantum data,” https://arxiv.org/abs/1903.08699v3, Apr. 7, 2020, 15 pages. [cited by applicant]
Jiang, Y., et al., “Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels,” 33rd Conference on Neural Information Processing Systems, NeurIPS 2019, Vancouver, Canada, Dec. 2019, … [cited by applicant]
Letizia, N. A., et al., “Capacity-Driven Autoencoders for Communications,” IEEE Open Journal of the Communications Society, vol. 2, https://ieeexplore.ieee.org/abstract/document/9449919, Jun. 18, 2021, 13 pages. [cited by applicant]
Liang, X-T., et al., “Entanglement-Assisted Classical Capacities of Some Single Qubit Quantum Noisy Channels,” Modern Physics Letters B, vol. 16, No. 12, World Scientific Publishing Company, May 2002, 8 pages. [cited by applicant]
Liu, L., et al., “On the Variance of the Adaptive Learning Rate and Beyond,” Published as a conference paper at ICLR 2020, https://arxiv.org/abs/1908.03265, Oct. 26, 2021, 14 pages. [cited by applicant]
Monnet, M., et al., “Pooling techniques in hybrid quantum-classical convolutional neural networks,” https://arxiv.org/abs/2305.05603, May 9, 2023, 10 pages. [cited by applicant]
Ngairangbam, V.S., et al., “Anomaly detection in high-energy physics using a quantum autoencoder,” American Physical Society, Physical Review D 105, 095004, https://journals.aps.org/prd/abstract/10.1103/PhysRevD.105.095… [cited by applicant]
O'Shea, T., et al., “An Introduction to Deep Learning for the Physical Layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, No. 4, https://ieeexplore.ieee.org/abstract/document/8054694, Dec. 201… [cited by applicant]
Qin, Z., et al., “Deep Learning in Physical Layer Communications,” IEEE Wireless Communications, vol. 26, No. 2, https://ieeexplore.ieee.org/abstract/document/8663966, Apr. 2019, 7 pages. [cited by applicant]
Romero, J., et al., “Quantum autoencoders for efficient compression of quantum data,” https://arxiv.org/abs/1612.02806, Feb. 10, 2017, 10 pages. [cited by applicant]
Shannon, C.E., “A Mathematical Theory of Communication,” The Bell System Technical Journal, vol. 27, No. 3, https://ieeexplore.ieee.org/document/6773024, Jul. 1948, 55 pages. [cited by applicant]
Tian, J., et al., “Recent Advances for Quantum Neural Networks in Generative Learning,” https://arxiv.org/abs/2206.03066, Jun. 7, 2022, 30 pages. [cited by applicant]
Wilde, M. M., “From Classical to Quantum Shannon Theory,” Cambridge University Press, https://arxiv.org/abs/1106.1445, Jul. 14, 2019, 774 pages. [cited by applicant]
Zhu, Y., et al., “Quantum autoencoders for communication-efficient quantum cloud computing,” https://arxiv.org/abs/2112.12369, Dec. 23, 2021, 14 pages. [cited by applicant]