Quantum Receiver Adaptive Displacement via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing quantum receivers, such as the homodyne detector, struggle with decoding signals in very lossy communication channels due to fundamental quantum noise, and techniques like the Dolinar receiver face challenges with time-dependent noise, leading to increased latency and reduced symbol rates.
Innovation Solution
A quantum receiver utilizing deep learning and machine-learning techniques, including artificial neural networks, probabilistic binary tree classifiers, and runtime stochastic optimization with automatic differentiation, to rapidly provide feed-forward signals for decoding optical signals, allowing for history-dependent displacements and adaptive measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the Dolinar receiver uses information feed-forward to approach the Helstrom bound, then the error rate is reduced, but the processing time increases due to finite feed-forward processing speed
Solution Approach 1:
The Dolinar receiver segments the signal into infinitely small time segments and processes them sequentially with feed-forward information. The patent applies this by dividing the continuous signal into discrete time modes, allowing parallel processing of multiple segments simultaneously, thus reducing total processing time while maintaining the error rate benefits of iterative refinement.
Solution Approach 2:
The receiver performs preliminary displacement operations based on prior measurement outcomes before the final detection. By pre-processing the signal with appropriate displacements guided by previous measurements, the system prepares the signal in advance for optimal detection, reducing the time needed for final decision-making while achieving low error rates.
2Productivity
If the time segment duration is reduced to increase symbol rate, then the symbol rate increases, but the feed-forward processing cannot keep up with the processing speed requirement
Solution Approach 1:
The patent segments the signal into multiple parallel time modes that can be processed simultaneously. By increasing the number of parallel segments rather than reducing individual segment duration, the system achieves higher effective symbol rates without exceeding the feed-forward processing speed capability of each individual segment.
Solution Approach 2:
The system transitions from sequential processing in one time dimension to parallel processing across multiple time mode dimensions. This dimensional expansion allows the receiver to handle more signal information simultaneously, effectively increasing symbol rate while maintaining manageable processing speeds for each parallel channel.
3Measurement precision
If the number of coefficients to describe time-dependent noise increases, then the noise characterization improves, but the computation latency increases rapidly
Solution Approach 1:
The patent transforms the noise characterization from a high-dimensional time-dependent function into a lower-dimensional parameter space by modeling noise statistics in terms of a limited set of parameters. This parameter reduction maintains sufficient noise characterization accuracy while dramatically reducing the computational latency required to process and adapt to noise conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed solution significantly reduces error rates and latency, surpassing the performance of traditional receivers like the Dolinar receiver, even in noisy conditions, by efficiently handling time-dependent noise and improving symbol rate without increasing segment duration.
Implementation Method 1
a beamsplitter for interfering the optical signal with a local-oscillator field to generate a displaced field
Implementation Method 2
a single-photon detector for detecting the displaced field
Data Source
AI summary
A quantum receiver for decoding an optical signal includes a beamsplitter for interfering the optical signal with a local-oscillator field to generate a displaced field, and a single-photon detector for detecting the displaced field. The quantum receiver also includes a signal-processing circuit for determining, based on an electrical output of the single-photon detector, a measurement outcome. The signal-processing circuit also determines, based on the measurement outcome and a feed-forward machine-learning model, a next displacement. The quantum receiver also includes at least one modulator for modulating, based on the next displacement, one or both of the optical signal and the local-oscillator field. Like a Dolinar receiver, the quantum receiver implements adaptive measurements to reduce the error probability of the decoded symbol. The use of machine-learning reduces the latency of the signal-processing circuit, thereby increasing the number of measurements that may be performed for each received symbol.