116 research outputs found
Theory of Acceleration of Decision Making by Correlated Time Sequences
Photonic accelerators have been intensively studied to provide enhanced
information processing capability to benefit from the unique attributes of
physical processes. Recently, it has been reported that chaotically oscillating
ultrafast time series from a laser, called laser chaos, provide the ability to
solve multi-armed bandit (MAB) problems or decision-making problems at GHz
order. Furthermore, it has been confirmed that the negatively correlated
time-domain structure of laser chaos contributes to the acceleration of
decision-making. However, the underlying mechanism of why decision-making is
accelerated by correlated time series is unknown. In this study, we demonstrate
a theoretical model to account for accelerating decision-making by correlated
time sequence. We first confirm the effectiveness of the negative
autocorrelation inherent in time series for solving two-armed bandit problems
using Fourier transform surrogate methods. We propose a theoretical model that
concerns the correlated time series subjected to the decision-making system and
the internal status of the system therein in a unified manner, inspired by
correlated random walks. We demonstrate that the performance derived
analytically by the theory agrees well with the numerical simulations, which
confirms the validity of the proposed model and leads to optimal system design.
The present study paves the way for improving the effectiveness of correlated
time series for decision-making, impacting artificial intelligence and other
applications
Conflict-free joint decision by lag and zero-lag synchronization in laser network
With the end of Moore's Law and the increasing demand for computing, photonic
accelerators are garnering considerable attention. This is due to the physical
characteristics of light, such as high bandwidth and multiplicity, and the
various synchronization phenomena that emerge in the realm of laser physics.
These factors come into play as computer performance approaches its limits. In
this study, we explore the application of a laser network, acting as a photonic
accelerator, to the competitive multi-armed bandit problem. In this context,
conflict avoidance is key to maximizing environmental rewards. We
experimentally demonstrate cooperative decision-making using zero-lag and lag
synchronization within a network of four semiconductor lasers. Lag
synchronization of chaos realizes effective decision-making and zero-delay
synchronization is responsible for the realization of the collision avoidance
function. We experimentally verified a low collision rate and high reward in a
fundamental 2-player, 2-slot scenario, and showed the scalability of this
system. This system architecture opens up new possibilities for intelligent
functionalities in laser dynamics
Chaotic mode-competition dynamics in a multimode semiconductor laser with optical feedback and injection
Photonic computing is attracting increasing interest to accelerate
information processing in machine learning applications. The mode-competition
dynamics of multimode semiconductor lasers is useful for solving the
multi-armed bandit problem in reinforcement learning for computing
applications. In this study, we numerically evaluate the chaotic
mode-competition dynamics in a multimode semiconductor laser with optical
feedback and injection. We observe the chaotic mode-competition dynamics among
the longitudinal modes and control them by injecting an external optical signal
into one of the longitudinal modes. We define the dominant mode as the mode
with the maximum intensity; the dominant-mode ratio for the injected mode
increases as the optical injection strength increases. We find that the
characteristics of the dominant mode ratio in terms of the optical injection
strength are different among the modes owing to the different optical feedback
phases. We propose a control technique for the characteristics of the dominant
mode ratio by precisely tuning the initial optical frequency detuning between
the optical injection signal and injected mode. We also evaluate the
relationship between the region for the large dominant mode ratio and injection
locking range. The region for the large dominant mode ratio does not correspond
to the injection-locking range. This discrepancy results from the complex
mode-competition dynamics in multimode semiconductor lasers with both optical
feedback and injection. This control technique of chaotic mode-competition
dynamics in multimode lasers is promising for applications in reinforcement
learning and reservoir computing as photonic artificial intelligence.Comment: 17 pages, 12 figures, 1 tabl
Solving multi-armed bandit problems using a chaotic microresonator comb
The Multi-Armed Bandit (MAB) problem, foundational to reinforcement
learning-based decision-making, addresses the challenge of maximizing rewards
amidst multiple uncertain choices. While algorithmic solutions are effective,
their computational efficiency diminishes with increasing problem complexity.
Photonic accelerators, leveraging temporal and spatial-temporal chaos, have
emerged as promising alternatives. However, despite these advancements, current
approaches either compromise computation speed or amplify system complexity. In
this paper, we introduce a chaotic microresonator frequency comb (chaos comb)
to tackle the MAB problem, where each comb mode is assigned to a slot machine.
Through a proof-of-concept experiment, we employ 44 comb modes to address an
MAB with 44 slot machines, demonstrating performance competitive with both
conventional software algorithms and other photonic methods. Further, the
scalability of decision making is explored with up to 512 slot machines using
experimentally obtained temporal chaos in different time slots. Power-law
scalability is achieved with an exponent of 0.96, outperforming conventional
software-based algorithms. Moreover, we find that a numerically calculated
chaos comb accurately reproduces experimental results, paving the way for
discussions on strategies to increase the number of slot machines
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