Enhancing the performance of coherent Ising machines in the large-noise regime with a fifth-order nonlinearity.

Coherent Ising machines (CIMs), leveraging the bistable physical properties of coherent light to emulate Ising spins, exhibit great potential as hardware accelerators for tackling complex combinatorial optimization problems. Recent advances have demonstrated that the performance of CIMs can be enhanced either by incorporating large random noise or higher-order nonlinearities, yet their combined effects on CIM performance remain mainly unexplored. In this work, we develop a numerical CIM model that utilizes a tunable fifth-order polynomial nonlinear dynamic function under large noise levels, which has the potential to be implemented in all-optical platforms. We propose a normal form of a CIM model that allows for both supercritical and subcritical pitchfork bifurcation operational regimes, with fifth-order nonlinearity and tunable hyperparameters to control the Ising spin dynamics. In the benchmark studies, we simulate various sets of MaxCut problems using our fifth-order polynomial CIM model. The results show a significant performance improvement, achieving an average of 59.5% improvement in median time-to-solution (TTS) and an average of 6 times improvement in median success rate (SR) for dense Maxcut problems in the BiqMac library, compared to the commonly used third-order polynomial CIM model with low noise. The fifth-order polynomial CIM model in the large-noise regime also shows better performance trends as the problem size scales up. These findings reveal the enhancements on the computational performance of Ising machines in the large-nose regime from fifth-order nonlinearity, showing important implications for both simulation and hardware perspectives.

Adjoint optimization of polarization-splitting grating couplers.

We have designed a polarization-splitting grating coupler (PSGC) in silicon-on-insulator (SOI) that has 1.2 dB peak loss in numerical simulations, which is the best simulated performance of PSGCs without a bottom reflector to the best of our knowledge. Adjoint method-based shape optimization enables us to explore complex geometries that are intractable with conventional design approaches. Physics-based process-independent knowledge of PSGCs is extracted from the adjoint optimization and can be transferred to other platforms with a minimum of effort.

The boundary for quantum advantage in Gaussian boson sampling.

Identifying the boundary beyond which quantum machines provide a computational advantage over their classical counterparts is a crucial step in charting their usefulness. Gaussian boson sampling (GBS), in which photons are measured from a highly entangled Gaussian state, is a leading approach in pursuing quantum advantage. State-of-the-art GBS experiments that run in minutes would require 600 million years to simulate using the best preexisting classical algorithms. Here, we present faster classical GBS simulation methods, including speed and accuracy improvements to the calculation of loop hafnians. We test these on a ∼100,000-core supercomputer to emulate GBS experiments with up to 100 modes and up to 92 photons. This reduces the simulation time for state-of-the-art GBS experiments to several months, a nine-orders of magnitude improvement over previous estimates. Last, we introduce a distribution that is efficient to sample from classically and that passes a variety of GBS validation methods.

Wafer-level testing of inverse-designed and adjoint-inspired vertical grating coupler designs compatible with DUV lithography.

Perfectly vertical grating couplers have various applications in optical I/O such as connector design, coupling to multicore optical fibers and multilayer silicon photonics. However, it is challenging to achieve perfectly vertical coupling without simultaneously increasing reflection. In this paper, we use the adjoint method as well as an adjoint-inspired methodology to design devices that can be fabricated using only a single-etch step in a c-Si 193 nm DUV immersion lithography process, while maintaining good coupling and low reflection. Wafer-level testing of devices fabricated by a pilot line foundry confirms that both design paradigms result in state-of-the-art experimental insertion loss (<2 dB) and bandwidths (∼20 nm) while having only moderate in-band reflection (<-10 dB). Our best design has a (median) 1.82 dB insertion loss and 21.3 nm 1 dB-bandwidth.

Health-related quality of life overview after different curative treatment options in muscle-invasive bladder cancer: an umbrella review.

PURPOSE: This umbrella review aims to evaluate the quality, summarize and compare the conclusions of systematic reviews investigating the impact of curative treatment options on health-related quality of life (HRQoL) in muscle-invasive bladder cancer (MIBC). METHODS: The Cochrane Library, MEDLINE, Embase and Web of Science were searched independently by two authors from inception until 06 January 2020. Systematic reviews and meta-analyses assessing the impact of any curative treatment option on HRQol in MIBC patients were eligible. Risk of bias was assessed using the AMSTAR 2 tool. RESULTS: Thirty-two reviews were included. Robot-assisted RC with extracorporeal urinary diversion and open RC have similar HRQoL (n = 10). Evidence for pelvic organ-sparing RC was too limited (n = 2). Patients with a neobladder showed better overall and physical HRQoL outcomes, but worse urinary function in comparison with ileal conduit (n = 17). Bladder-preserving radiochemotherapy showed slightly better urinary and sexual but worse gastro-intestinal HRQoL outcomes in comparison with RC patients (n = 6). Quality of the reviews was low in more than 50% of the available reviews and most of the studies included in the reviews were nonrandomized studies. CONCLUSION: This umbrella review gives a comprehensive overview of the available evidence to date.

Adjoint-method-inspired grating couplers for CWDM O-band applications.

We have designed a grating coupler on Silicon-on-Insulator (SOI) platform that has sufficient bandwidth to cover the entire CWDM O-band from 1270 nm to 1330 nm. The grating architecture is inspired by adjoint method-based geometry optimization, and then parameterized to accommodate DOE construction for tapeouts at commercial CMOS foundries and wafer-level testing on fiber probe stations. One grating design achieved peak loss of 3.2 dB with 1-dB bandwidth spanning from 1265 nm to 1338 nm in simulations.

Trainable hardware for dynamical computing using error backpropagation through physical media.

Neural networks are currently implemented on digital Von Neumann machines, which do not fully leverage their intrinsic parallelism. We demonstrate how to use a novel class of reconfigurable dynamical systems for analogue information processing, mitigating this problem. Our generic hardware platform for dynamic, analogue computing consists of a reciprocal linear dynamical system with nonlinear feedback. Thanks to reciprocity, a ubiquitous property of many physical phenomena like the propagation of light and sound, the error backpropagation-a crucial step for tuning such systems towards a specific task-can happen in hardware. This can potentially speed up the optimization process significantly, offering important benefits for the scalability of neuro-inspired hardware. In this paper, we show, using one experimentally validated and one conceptual example, that such systems may provide a straightforward mechanism for constructing highly scalable, fully dynamical analogue computers.

Nanophotonic reservoir computing with photonic crystal cavities to generate periodic patterns.

Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been used successfully to solve complex problems such as signal classification and signal generation. These systems are mainly implemented in software, and thereby they are limited in speed and power efficiency. Several optical and optoelectronic implementations have been demonstrated, in which the system has signals with an amplitude and phase. It is proven that these enrich the dynamics of the system, which is beneficial for the performance. In this paper, we introduce a novel optical architecture based on nanophotonic crystal cavities. This allows us to integrate many neurons on one chip, which, compared with other photonic solutions, closest resembles a classical neural network. Furthermore, the components are passive, which simplifies the design and reduces the power consumption. To assess the performance of this network, we train a photonic network to generate periodic patterns, using an alternative online learning rule called first-order reduced and corrected error. For this, we first train a classical hyperbolic tangent reservoir, but then we vary some of the properties to incorporate typical aspects of a photonics reservoir, such as the use of continuous-time versus discrete-time signals and the use of complex-valued versus real-valued signals. Then, the nanophotonic reservoir is simulated and we explore the role of relevant parameters such as the topology, the phases between the resonators, the number of nodes that are biased and the delay between the resonators. It is important that these parameters are chosen such that no strong self-oscillations occur. Finally, our results show that for a signal generation task a complex-valued, continuous-time nanophotonic reservoir outperforms a classical (i.e., discrete-time, real-valued) leaky hyperbolic tangent reservoir (normalized root-mean-square errors=0.030 versus NRMSE=0.127).

Experimental demonstration of reservoir computing on a silicon photonics chip.

In today's age, companies employ machine learning to extract information from large quantities of data. One of those techniques, reservoir computing (RC), is a decade old and has achieved state-of-the-art performance for processing sequential data. Dedicated hardware realizations of RC could enable speed gains and power savings. Here we propose the first integrated passive silicon photonics reservoir. We demonstrate experimentally and through simulations that, thanks to the RC paradigm, this generic chip can be used to perform arbitrary Boolean logic operations with memory as well as 5-bit header recognition up to 12.5 Gbit s(-1), without power consumption in the reservoir. It can also perform isolated spoken digit recognition. Our realization exploits optical phase for computing. It is scalable to larger networks and much higher bitrates, up to speeds >100 Gbit s(-1). These results pave the way for the application of integrated photonic RC for a wide range of applications.

Excitability in optically injected microdisk lasers with phase controlled excitatory and inhibitory response.

We demonstrate class I excitability in optically injected microdisk lasers, and propose a possible optical spiking neuron design. The neuron has a clear threshold and an integrating behavior, leading to an output rate-input rate dependency that is comparable to the characteristic of sigmoidal artificial neurons. We also show that the optical phase of the input pulses has influence on the neuron response, and can be used to create inhibitory, as well as excitatory perturbations.

Excitation transfer between optically injected microdisk lasers.

Recently, we have theoretically demonstrated that optically injected microdisk lasers can be tuned in a class I excitable regime, where they are sensitive to both inhibitory and excitatory external input pulses. In this paper, we propose, using simulations, a topology that allows the disks to react on excitations from other disks. Phase tuning of the intermediate connections allows to control the disk response. Additionally, we investigate the sensitivity of the disk circuit to deviations in driving current and locking signal wavelength detuning. Using state-of-the-art fabrication techniques for microdisk laser, the standard deviation of the lasing wavelength is still about one order of magnitude too large. Therefore, compensation techniques, such as wavelength tuning by heating, are necessary.

Cascadable excitability in microrings.

To emulate a spiking neuron, a photonic component needs to be excitable. In this paper, we theoretically simulate and experimentally demonstrate cascadable excitability near a self-pulsation regime in high-Q-factor silicon-on-insulator microrings. For the theoretical study we use Coupled Mode Theory. While neglecting the fast energy and phase dynamics of the cavity light, we can still preserve the most important microring dynamics, by only keeping the temperature difference with the surroundings and the amount of free carriers as dynamical variables of the system. Therefore we can analyse the microring dynamics in a 2D phase portrait. For some wavelengths, when changing the input power, the microring undergoes a subcritical Andronov-Hopf bifurcation at the self-pulsation onset. As a consequence the system shows class II excitability. Experimental single ring excitability and self-pulsation behaviour follows the theoretic predictions. Moreover, simulations and experiments show that this excitation mechanism is cascadable.