Harnessing microcomb-based parallel chaos for random number generation and optical decision making

Abstract

Optical chaos is vital for various applications such as private communication, encryption, anti-interference sensing, and reinforcement learning. Chaotic microcombs have emerged as promising sources for generating massive optical chaos. However, their inter-channel correlation behavior remains elusive, limiting their potential for on-chip parallel chaotic systems with high throughput. In this study, we present massively parallel chaos based on chaotic microcombs and high-nonlinearity AlGaAsOI platforms. We demonstrate the feasibility of generating parallel chaotic signals with inter-channel correlation <0.04 and a high random number generation rate of 3.84 Tbps. We further show the application of our approach by demonstrating a 15-channel integrated random bit generator with a 20 Gbps channel rate using silicon photonic chips. Additionally, we achieved a scalable decision-making accelerator for up to 256-armed bandit problems. Our work opens new possibilities for chaos-based information processing systems using integrated photonics, and potentially can revolutionize the current architecture of communication, sensing and computations.

Fig. 1. Microcomb based massively parallel chaotic signal generation and applications.

a Different methods to obtain parallel chaotic sources, (a1) multiple electric chaotic oscillators; (a2)spatiotemporal chaos in free space; (a3) multiple chaotic lasers; (a4) chaotic comb. LUT, look-up table; Disp, dispersion element. b The principle of the chaotic comb function as the parallel chaotic source. As a continuous wave injected into the high-quality and high-nonlinear optical microcavity, the intracavity field will evolve into the spatiotemporal chaos. The output consists of multiple comb lines in the frequency domain. Each comb line carries a chaotic signal, whose autocorrelation function is a dirac-like function. The cross-correlation between different channels is negligible. c Scalable chaos-based systems empowered by chaotic combs. Using the wavelength division multiplexing technology, hundreds of chaotic sources could be distributed, detected, and processed in parallel, and employed for random number generation, reinforcement learning, lidar, radar and private communication.

Fig. 2. The characterization of chaotic combs.

a The optical spectrum of the generated chaotic comb. b Setup for characterizing the chaotic comb. ECL external cavity diode laser, EDFA erbium-doped fiber amplifier, NF notch filter, WSS wavelength selective switch, PD photodetector, OSC oscilloscope, ESA electrical spectrum analyzer, OSA optical spectrum analyzer. c Radio frequency noise spectrum of chaotic combs pumped by different power levels. The gray line shows the spectra without optical input. d The time serial of a single comb line recorded by the oscilloscope. e The amplitude distribution of the time serial shown in (d). f The autocorrelation function (ACF) for all comb lines in C band. g The full width at half maximum (FWHM) of the ACF for different comb lines varies with the detuning of the pump laser. h The correlation between different comb lines. i The cross-correlation between symmetric comb lines in experiment (red line) and simulation (blue line).

Fig. 3. The parallel random bit generator based on a microresonator and a SOI chip.

a Optical microscope photograph of the SiPh receiver. b The set-up scheme for parallel random number generation; AWG arrayed waveguide grating, PD photodetector, OSC oscillator, D delay unit, XOR exclusive-OR, BPF band-pass filter, EDFA erbium-doped fiber amplifier. c The setup for random bit generation using two chaotic combs. d The possibility distribution function of the differential data. e The distribution of the extracted 3 LSBs. f The ACF of the generated bit sequence. The red line indicates the lower limit determined by 1/$\sqrt{n}$. The NIST SP800-22 test results for signals detected by SOI PD with setup shown in (b) (g) and commercial InP PD with setup shown in (c) (h).

Fig. 4. Multi-armed bandit problem solving based on chaotic combs.

a The scheme of the optical decision making based on the parallel chaotic source. b One decision process for 32-armed bandit problem. The left figure shows the hit probability distribution of 32 slots, where the third slot have the highest hit probability 0.9. c The evolution of corrected decision rate with the increase of cycles. The red dashed line marks the corrected decision rate of 95%. d The evolution of corrected decision rate under different scales. e The comparation of scalability between chaotic-comb-based decision maker and other methods.