- Research
- Open Access
A coherent perceptron for all-optical learning
- Nikolas Tezak^{1}Email author and
- Hideo Mabuchi^{1}
https://doi.org/10.1140/epjqt/s40507-015-0023-3
© Tezak and Mabuchi; licensee Springer. 2015
- Received: 2 January 2015
- Accepted: 8 April 2015
- Published: 30 April 2015
Abstract
We present nonlinear photonic circuit models for constructing programmable linear transformations and use these to realize a coherent perceptron, i.e., an all-optical linear classifier capable of learning the classification boundary iteratively from training data through a coherent feedback rule. Through extensive semi-classical stochastic simulations we demonstrate that the device nearly attains the theoretical error bound for a model classification problem.
Keywords
- optical information processing
- coherent feedback
- machine learning
- photonic circuits
- nonlinear optics
- perceptron
1 Introduction
Recent progress in integrated nanophotonic engineering [1–10] has motivated follow-up proposals [11, 12] of nanophotonic circuits for all-optical information processing. While most of these focus on implementations of digital logic, we present here an approach to all-optical analog, neuromorphic computation and propose design schemes for a set of devices to be used as building blocks for large scale circuits.
Optical computation has been a long-time goal [13, 14], with research interest surging regularly after new engineering capabilities are attained [15, 16], but so far the parallel progress and momentum of CMOS based integrated electronics has outperformed all-optical devices.
In recent years we have seen rapid progress in the domain of machine learning, and artificial intelligence in general. Although most current ‘big data’-applications are realized on digital computing architectures, there is now an increasing amount of computation done in specialized hardware such as GPUs. Specialized analog computational devices for solving specific subproblems more efficiently than possible with either GPUs or general purpose computers are being considered or already implemented by companies such as IBM, Google and HP and in academia, as well [17–20]. Specifically in the field of neuromorphic computation, there has been impressive progress on CMOS based analog computation platforms [21, 22].
Several neuromorphic approaches to use complex nonlinear optical systems for machine learning applications have recently been proposed [23–26] and some initial schemes have been implemented [9, 27]. So far, however, all of these ‘optical reservoir computers’ have still required digital computers to prepare the inputs and process the output of these devices with the optical systems only being employed as static nonlinear mappings for dimensional lifting to a high dimensional feature space [28], in which one then applies straightforward linear regression or classification for learning an input-output map [29].
In this work, we address how the final stage of such a system, i.e., the linear classifier could be realized all-optically. We provide a universal scheme, i.e., independent of which particular kind of optical nonlinearity is employed, for constructing tunable all-optical, phase-sensitive amplifiers and then outline how these can be combined with self-oscillating systems to realize an optical amplifier with programmable gain, i.e., where the gain can be set once and is then fixed subsequently.
Using these as building blocks we construct an all-optical perceptron [30, 31], a system that can classify multi-dimensional input data and, using pre-classified training data learn the correct classification boundary ‘on-line’, i.e., incrementally. The perceptron can be seen as a highly simplified model of a neuron. While the idea of all-optical neural networks has been proposed before [32] and an impressive scheme using electronic, measurement-based feedback for spiking optical signals has been realized [33], to our knowledge, we offer the first complete description for how the synaptic weights can be stored in an optical memory and programmed via feedback.
The physical models underlying the employed circuit components are high intrinsic-Q optical resonators with strong optical nonlinearities. For theoretical simplicity we assume resonators with either a \(\chi_{2}\) or a \(\chi_{3}\) nonlinearity, but the design can be adapted to depend on only one of these two or alternative nonlinearities such as those based on free carrier effects or optomechanical interactions.
The strength of the optical nonlinearity and the achievable Q-factors of the optical resonators determine the overall power scale and rate at which a real physical device could operate. Both a stronger nonlinearity and higher Q allow operating at lower overall power.
We present numerical simulations of the system dynamics based on the semi-classical Wigner-approximation to the full coherent quantum dynamics presented in [10]. For photon numbers as low as (∼10-20) this approximation allows us to accurately model the effect of optical quantum shot noise even in large-scale circuits.
In the limit of both very high Q and very strong nonlinearity, we expect quantum effects to become significant as entanglement can arise between the field modes of physically separated resonators. In the Appendix, we provide full quantum models for all basic components of our circuit. The possibility of a quantum speedup is being addressed in ongoing work. Recently, D-Wave Systems has generated a lot of interest in their own superconducting qubit based quantum annealer. Although the exact benefits of quantum dynamics in their machines has not been conclusively established [34], recent results analyzing the role of tunneling in a quantum annealer [35] are intriguing and suggest that quantum effects can be harnessed in computational devices that are not unitary quantum computers.
1.1 The perceptron algorithm
In the above parametrization the decision boundary always contains the origin \(z=0\), but the more general case of an affine decision boundary \(\{\tilde{z}\in \mathbb {R}^{n}: \tilde{w}^{T} \tilde{z} = b\}\) can be obtained by extending the input vector by a constant \(z = (\tilde {z}^{T}, 1)^{T}\in \mathbb {R}^{n+1}\) and similarly defining an extended weight vector \(w=(\tilde{w}^{T}, -b)^{T}\).
1.2 The circuit modeling framework
Circuits are fully described via Quantum Hardware Description Language (QHDL) [37] based on Gough and James’ SLH-framework [38, 39]. To carry out numerical simulations for large scale networks, we derive a system of semi-classical Langevin equations based on the Wigner-transformation as described in [10]. Note that there is a perfect one-to-one correspondence between nonlinear cavity models expressed via SLH and the Wigner method as long as the nonlinearities involve only oscillator degrees of freedom. There is ongoing research in our group to establish similar results for more general nonlinearities [40].
In the Wigner-based formalism, a system is described in terms of time-dependent complex coherent amplitudes \(\alpha (t)=(\alpha _{1}(t), \alpha _{2}(t),\dots, \alpha _{m}(t))^{T}\) for the internal cavity modes and external inputs \(\beta _{\mathrm {in}}(t) = (\beta _{\mathrm {in},{1}}(t), \beta _{\mathrm {in},{2}}(t), \dots, \beta _{\mathrm {in},{n}}(t))^{T}\). These amplitudes relate to quantum mechanical expectations as \(\langle \alpha _{j} \rangle\approx\langle a_{j}\rangle_{\mathrm{QM}}\), where \(\langle\cdot\rangle\) denotes the expectation with respect to the Wigner quasi distribution and \(\langle\cdot\rangle_{\mathrm{QM}}\) a quantum mechanical expectation value. See [10] for the corresponding relations of higher order moments.
Each input consists of a coherent, deterministic part and a stochastic contribution \(\beta _{\mathrm {in},{j}}(t)=\bar {\beta }_{\mathrm {in},j}(t) + \eta_{j}(t)\). The stochastic terms \(\eta_{j}(t) = \eta_{j,1}(t) + i \eta_{j, 2}(t)\) are assumed to be independent complex Gaussian white noise processes with correlation function \(\langle\eta_{j,s}(t)\eta_{k,r}(t')\rangle = \frac{1}{4}\delta_{jk}\delta_{sr}\delta(t-t')\).
The linearity of the input-output relationship in either framework (5) and (7) in the external degrees of freedom leads to algebraic rules for deriving reduced models for whole circuits of nonlinear optical resonators by concatenating component models and algebraically solving for their interconnections [10, 39]. To see the basic component models used in this work see the Appendix. Netlists for composite components and the whole circuit will be made available at [41].
2 The coherent perceptron circuit
- 1
Each input \(x_{j}\) is multiplied by a weight \(w_{j}\).
- 2
The weighted inputs are coherently added.
- 3
The sum drives a thresholding element to generate the estimated class label \(\hat{y}\).
- 4
In the training phase (input \(T=1\)) the estimated class label \(\hat{y}\) is compared with the true class label (input Y) and based on the outcome, feedback is applied to modify the weights \(\{w_{j}\}\).
2.1 Tunable gain Kerr-amplifier
A single mode Kerr-nonlinear resonator driven by an appropriately detuned coherent drive ϵ can have a strongly nonlinear dependence of the intra-cavity energy on the drive power. When the drive of a single resonator is given by the sum of a constant large bias amplitude and a small signal \(\epsilon=\frac{1}{\sqrt{2}} (\epsilon_{0} +\delta\epsilon)\), the steady state reflected amplitude is \(\epsilon'=\frac{1}{\sqrt{2}} (\eta\epsilon_{0} + g_{-}(\epsilon _{0}) \delta\epsilon+ g_{+}(\epsilon_{0}) \delta\epsilon^{\ast}) +O(\delta\epsilon^{2})\), where \(|\eta|\le1\) with equality for the ideal case of negligible intrinsic cavity losses. The small signal thus experiences phase sensitive gain dependent on the bias amplitude and phase. We provide analytic expressions for the gain in Appendix A.2.1.
An almost identical circuit, but featuring resonators with additional internal loss equal to the wave-guide coupling^{2} and constantly biased to dynamic resonance \(\langle|\alpha|^{2} \rangle_{\mathrm{ss}} = -\Delta/\chi\) can be used to realize a quadrature filter, i.e., an element that has unity gain for the real quadrature and zero for the imaginary one. Now the quadrature filtered signal still has an imaginary component, but to linear order this only consists of transmitted noise from the additional internal loss. While it would be possible to add one of these downstream of every tunable Kerr amplifier, in our specific application it is more efficient to add just a single one downstream of where the individual amplifier outputs are summed (cf. Section 2.5). This also reduces the total amount of additional noise introduced to the system.
2.2 Encoding and storing the gain
In the preceding section we have seen how to realize a tunable gain amplifier, but for programming and storing this gain (or equivalently its bias amplitude) an additional component is needed. Although it is straightforward to design a multi-stable system capable of outputting a discrete set of different output powers to be used as the amplifier bias, such schemes would likely require multiple nonlinear resonators and it would be more cumbersome to drive transitions between the output states.
An alternative to such schemes is given by systems that have a continuous set of stable states. Recent analysis of continuous time recurrent neural network models trained for complex temporal information processing tasks has revealed multi-dimensional stable attractors in the internal network dynamics that are used to store information over time [43].
A simple semi-classical nonlinear resonator model to exhibit this is given by a non-degenerate optical parametric oscillator (NOPO) pumped above threshold; for low pump input powers this system allows for parametric amplification of a weak coherent signal (or idler) input. In this case vacuum inputs for the signal and idler lead to outputs with zero expected photon number. Above a critical threshold pump power, however, the system down-converts pump photons into pairs of signal and idler photons.
Vacuum shot noise on the inputs leads to phase diffusion with a rate of \(\gamma_{\Phi}= \frac{\kappa}{8n_{0}}\), where κ is the signal and idler line width and \(n_{0}\) is the steady state intra cavity photon number in either mode. We point out that this diffusion rate does not directly depend on the strength of the nonlinearity which only determines how strongly the system must be pumped to achieve a given intra cavity photon number \(n_{0}\).
A weak external signal input breaks the symmetry and biases the signal output phase towards the external signal’s phase. This allows for changing the programmed phase value.
Finally, we note that parametric oscillators can also be realized in materials with vanishing \(\chi_{2}\) nonlinearity. They have been successfully realized via four-wave mixing (i.e., exploiting a \(\chi _{3}\) nonlinearity) in [1, 2, 44] and even in opto-mechanical systems [8] in which case the idler mode is given by a mechanical degree of freedom.
In principle any nonlinear optical system that has a stable limit cycle could be used to store and encode a continuous value in its oscillation phase. Non-degenerate parametric oscillators stand out because of their theoretical simplicity allowing for a ‘static’ analysis inside a rotating frame.
2.3 Programmable gain amplifier
Combining the circuits described in the preceding sections allows us to construct a fully programmable phase sensitive amplifier. In Figure 2(b) we see that there exists a particular bias amplitude at which the real to real quadrature gain vanishes \(g_{rr}(\epsilon_{0}^{\mathrm{min}}) = 0\). We combine the NOPO signal output \(\xi=r e^{i\Phi}\) with a constant phase bias input \(\xi_{0}\) (cf. Figure 3(a)) on a beamsplitter such that the outputs vary between zero gain and the maximal gain bias values \(\vert \frac{\xi_{0} \pm r e^{i\Phi}}{\sqrt{2}}\vert \in[\epsilon _{0}^{\mathrm{min}}, \epsilon_{0}^{\mathrm{max}}]\). To realize both positive and negative gain, we use the second output of that beamsplitter to bias another tunable amplifier. The two amplifiers are always biased oppositely meaning that one will have maximal gain when the other’s gain vanishes and vice versa. The overall input signal is split and sent through both amplifiers and then re-combined with a relative π phase shift. This complementary setup leads to an overall effective gain tunable within \(G_{rr}(\Phi) \in[-\frac{g_{rr}^{\mathrm{max}}}{2}, \frac{g_{rr}^{\mathrm{max}}}{2}]\) (cf. Figure 3(b)).
The resulting synapse model is quite complex and certainly not optimized for a minimal component number but rather the ease of theoretical analysis. A more resource efficient programmable amplifier could easily be implemented using just two or three nonlinear resonators. E.g., inspecting the real to imaginary quadrature gain \(g_{ir}(\epsilon_{0})\) in Figure 2(b) we see that close to \(\epsilon _{0}^{\mathrm{max}}\) it passes through zero fairly linearly and with an almost symmetric range. This indicates that we could use a single tunable amplifier to realize both positive and negative gain. Using only a single resonator for the tunable amplifier could work as well, but it would require careful interferometric bias cancellation and more tedious upfront analysis. We do not think it is feasible to use just a single resonator for both the parametric oscillator and the amplifier because any amplified input signal would have an undesirable back-action on the oscillator phase.
2.4 Optical switches
The feedback to the perceptron weights (cf. Equation (3)) is conditional on the binary values of the given and estimated class labels y and \(\hat{y}\), respectively. The logic necessary for implementing this can be realized by means of all-optical switches. There have been various proposals and demonstrations [7, 45] of all-optical gates/switches and quantum optical switches [46].
Note that at control input amplitudes significantly different from the two control levels the outputs are coherent mixtures of the inputs, i.e., the switch then realizes a tunable beamsplitter.
Finally, we point out that using two different (frequency non-degenerate) resonator modes has the advantage that the interaction between control and signal inputs is phase insensitive which greatly simplifies the design and analysis of cascaded networks of such switches.
2.5 Generation of the estimated label
The estimated classifier label \(\hat{y}\) should be a step function applied to the inner product of the weight vector and the input. In the preceding sections we have shown how individual inputs \(x_{j}\) can be amplified with programmable gain to give \(\tilde{s}_{j} = \tilde {G}(\Phi_{j})x_{j}\), thus realizing the individual contributions to the inner product. These are then summed on an n-port beamsplitter that has an output which gives the uniformly weighted sum \(\tilde{s} := \frac{1}{\sqrt {N}}\sum_{k=1}^{N} \tilde{G}(\Phi_{k})x_{k}\).
The gain factors \(\tilde{G}(\Phi_{k}) = G_{rr}(\Phi_{k}) + i G_{ir}(\Phi _{k})\) generally have an unwanted imaginary part which we subtract by passing the summed output through a quadrature filter circuit (cf. the last paragraph of Section 2.1), which has unit gain for the real quadrature and zero gain for the imaginary quadrature leading to an overall output \(s = \operatorname{Re} \tilde{s} = \frac{1}{\sqrt{N}}\sum_{k=1}^{N} G_{rr}(\Phi_{k})x_{k}\). The thresholding circuit should now produce a high output if \(s>0\) and a zero output if \(s \le0\).
It turns out that the optical Fredkin gate described in the previous section already works almost as a two mode thresholder, where the control input leads to a step-like response in the signal outputs: A constant signal input amplitude which encodes the logical ‘1’ state is applied to one of the signal inputs. When the control input amplitude is varied from zero to \(\xi_{0}\), the signal output turns on fairly abruptly at some threshold \(\xi_{\mathrm{th}} < \xi_{0}\). To make the thresholding phase sensitive, the control input is given by the sum of s and a constant offset \(s_{0}\) that provides a phase reference: \(c = \frac{1}{\sqrt{2}}(s + s_{0})\).
For a Fredkin gate operated with continuous control inputs the signal output is almost zero for a considerable range of small control inputs. However, for very high control inputs, i.e., significantly above \(\xi _{0}\), the signal output decreases instead of staying constant as would be desirable for a step-function like profile. We found that this issue can be addressed by transmitting the control input through a single mode Kerr-nonlinear cavity, with resonance frequency chosen such that the transmission gain \(|c'/c|\) is peaked close to \(c'=\xi_{0}\). For input amplitudes larger than c, the transmission gain is lower (although \(|c'|\) still grows monotonically with \(|c|\)) which extends the input range over which the subsequent Fredkin gate stays in the on-state.
3 Results
The perceptron’s SDEs where simulated using a newly developed custom software package named QHDLJ [48] implemented in Julia [49] which allows for dynamic compilation of circuit models to LLVM [50] bytecode that runs at speed comparable to C/C++. All individual simulations can be carried out on a laptop, but the results in Figure 8 were obtained by averaging over the results of 100 stochastic simulation run on an HP ProLiant server with 80 cores. The current version of QHDLJ uses one process per trajectory, but the code could easily be vectorized.
The learning rate of the perceptron is determined by two things, the overall strength of the learning feedback as well as the time for which each example is presented to the circuit. In Figure 8(b) we plot the estimated error rate for varying feedback strength and duration. As can be expected intuitively, we find that there are trade-offs between speed (smaller Δt preferable) and energy consumption (smaller α preferable).
3.1 Time scales and power budget
Here we roughly estimate the power consumption of the whole device and discuss how to scale it up to a higher input dimension.
Any real-world implementation will depend strongly on the engineering paradigm, i.e., the choice of material/nonlinearity as well as the engineering precision, but based on recently achieved progress in nonlinear optics we will estimate an order of magnitude range for the input power.
The signal and feedback input power to the circuit will scale linearly in the number of synapses N.
The bias inputs for the amplifiers has to be larger than the signal to ensure linearly operation, but it should be expected that some of the scattered bias amplitudes can be reused to power multiple synapses.
In our models we have defined all rates relative to the line width of the signal and idler mode of the NOPO, because this is the component that should necessarily have the smallest decay rate to ensure a long lifetime for the memory.
All other resonators are employed as nonlinear input-output transformation devices and therefore a high bandwidth (corresponding to much lower loaded quality factor) is necessary for achieving a high bit rate. For our simulations we typically assumed quality factors that were lower than the NOPO’s by 1-2 orders of magnitude. Based on self-oscillation threshold powers reported in [1, 2, 4, 53] and the switching powers of [7] we estimate the necessary power per synapse to be in the range of ∼10-100 μWatt. By re-using the scattered pump and bias fields it should be possible to reduce the power consumption per amplifier even further. Even for the continuous wave signal paradigm we have assumed (as opposed to pulsed/spiking signals such as considered in [25]) the devices proposed here could be competitive with the current state of the art CMOS-based neuromorphic electrical circuits [22].
In the simulations for the 8-dimensional perceptron our input rate for training data was set to \(\Delta t^{-1} = \frac{\kappa}{2}\). This value corresponds to roughly ten times the average feedback delay time between arrival of an input pattern and the conditional switching of the feedback logic upon arrival of the generated estimated state label \(\hat{y}\). This time can be estimated as \(\tau_{fb}(n) \approx G_{\mathrm{max}}\kappa_{A}^{-1} + \kappa_{QF}^{-1} + \kappa_{\mathrm{thresh}}^{-1} + n \kappa_{F}^{-1}\), where n is the index of the synaptic weight, \(G_{\mathrm{max}}\) is the amplifier gain range and \(\kappa_{A}\), \(\kappa_{QF}\), \(\kappa _{\mathrm{thresh}}\) and \(\kappa_{F}\) are the line widths of the amplifier, quadrature filter, the combined thresholding circuit (cf. Figure 5) and the feedback Fredkin gates. There is a contribution scaling with n because the feedback traverses the individual weights sequentially to save power.
When scaling up the perceptron to a higher dimension while retaining approximately the same input signal powers, it is intuitively clear that the combined ‘inner product’ signal amplitude s scales as \(s\propto\sqrt{N}s_{1}\), where \(s_{1}\) is the signal amplitude for a single input. This allows to similarly scale up the amplitude \(\zeta _{0}\) of the signal encoding the generated estimated state label \(\hat {y}\) and consequently the bandwidth of the feedback Fredkin gates that it drives. A detailed analysis reveals that the Fredkin gate threshold scales as \(\sqrt{N}\), in particular we find that \(\sqrt{|\chi |}\zeta_{0} \propto\kappa_{F} \propto\sqrt{|\chi|}\xi_{0} \propto \kappa_{\mathrm{thresh}}\propto\sqrt{|\chi|} s\propto\sqrt{N|\chi|}s_{1}\). The first two scaling relationships are due to the constraints on the Fredkin gate construction (cf. Appendix A.2.2), the next two scaling relationships follow from demanding that the additional thresholding resonator be approximately dynamically resonant at the highest input level (cf. Appendices A.2.1 and A.2.2). The last proportionality is simply due to the amplitude summation at the N-port beamsplitter.
This reveals that when increasing N the perceptron as constructed here would have to be driven at a lower input bit rate scaling as \(\Delta t^{-1} \propto N^{-\frac{1}{2}}\) or alternatively be driven with higher signal input powers. A possible solution that could greatly reduce the difference in arrival time \(\sim\kappa_{F}^{-1}\) at each synapse could be to increase the waveguide-coupling to the control signal and thus decrease the delay per synapse. The resulting increase in the required control amplitude \(\zeta_{0}\) can be counter-acted with feedback, i.e., by effectively creating a large cavity around the control loop. When even this strategy fails one could add fan-out stages for \(\hat {y}\) which introduce a delay that grows only logarithmically with N.
Finally, we note that the bias power of all the Kerr-effect based models considered here scales inversely with the respective nonlinear coefficient \(\{|\zeta_{0}|^{2}, |s|^{2}\} \times|\chi| \sim\mathrm{const}\) when keeping the bandwidth fixed. This implies that improvements in the nonlinear coefficient translate to lower power requirements or alternatively a faster speed of operation.
4 Conclusion and outlook
In conclusion we have shown how to design an all-optical device that is capable of supervised learning from input data, by describing how tunable gain amplifiers with signal/bias isolation can be constructed from nonlinear resonators and subsequently combined with self-oscillating resonators to encode the programmed amplifier gain in their oscillation phase. By considering a few additional nonlinear devices for thresholding and all-optical switching we then show how to construct a perceptron, including the perceptron feedback rule. To our knowledge this is the first end-to-end description of an all-optical circuit capable of learning from data. We have furthermore demonstrated that despite optical shot-noise it nearly attains the performance of the optimal software algorithm for the classification task that we considered. Finally, we have discussed the relevant time-scales and pointed out how to scale the circuit up to large input dimensions while retaining the signal processing bandwidth and a low power consumption per input.
Possible applications of an all-optical perceptron are as the trainable output filter of an optical reservoir computer or as a building block in a multi-layer all-optical neural network.
The programmable amplifier could be used as a building block to construct other learning models that rely on continuously tunable gain such as Boltzmann machines and hardware implementations of message passing algorithms.
An interesting next step would be to design a perceptron that can handle inputs at different carrier frequencies. In this case wavelength division multiplexing (WDM) might allow to significantly reduce the physical footprint of the device.
A simple modification of the perceptron circuit could autonomously learn to invert linear transformations that were applied to its input signals. This could be used for implementing a circuit capable of solving linear regression problems. In combination with a multi-mode optical fibers such a device could also have applications for all-optical sensing.
Finally, an extremely interesting question is whether harnessing quantum dynamics could lead to a performance increase. We hope to address these ideas in future work.
One can easily convince oneself that all even order contributions are scattered into the bias output.
In the photonics community this is referred to as critically coupled, whereas the amplifier circuit would ideally be strongly overcoupled such that additional internal losses are negligible.
In this appendix we denote expectations with respect to the Wigner function as \(\langle\cdot\rangle_{\mathrm{W}}\) and quantum mechanical expectations as \(\langle\cdot\rangle\).
Declarations
Acknowledgements
This work is supported by DARPA-MTO under award no. N66001-11-1-4106. NT acknowledges support from a Stanford Graduate Fellowship. We would also like to thank Ryan Hamerly, Jeff Hill, Peter McMahon and Amir Safavi-Naeini for helpful discussion.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
- Kippenberg T, Spillane S, Vahala K. Kerr-nonlinearity optical parametric oscillation in an ultrahigh-Q toroid microcavity. Phys Rev Lett. 2004;93(8):83904. ADSView ArticleGoogle Scholar
- Del’Haye P, Schliesser A, Arcizet O, Wilken T, Holzwarth R, Kippenberg TJ. Optical frequency comb generation from a monolithic microresonator. Nature. 2007;450(7173):1214-7. ADSView ArticleGoogle Scholar
- Levy M. Nanomagnetic route to bias-magnet-free, on-chip Faraday rotators. J Opt Soc Am B. 2005;22(1):254-60. ADSView ArticleGoogle Scholar
- Razzari L, Duchesne D, Ferrera M, Morandotti R, Chu S, Little BE, Moss DJ. CMOS-compatible integrated optical hyper-parametric oscillator. Nat Photonics. 2009;4(1):41-5. ADSView ArticleGoogle Scholar
- Englund D, Faraon A, Fushman I, Stoltz N, Petroff P, Vucković J. Controlling cavity reflectivity with a single quantum dot. Nature. 2007;450(7171):857-61. ADSView ArticleGoogle Scholar
- Fushman I, Englund D, Faraon A, Stoltz N, Petroff P, Vuckovic J. Controlled phase shifts with a single quantum dot. Science. 2008;320(5877):769-72. ADSView ArticleGoogle Scholar
- Nozaki K, Tanabe T, Shinya A, Matsuo S. Sub-femtojoule all-optical switching using a photonic-crystal nanocavity. Nat Photonics. 2010;4:477-83. ADSView ArticleGoogle Scholar
- Cohen JD, Meenehan SM, MacCabe GS, Gröblacher S, Safavi-Naeini AH, Marsili F, Shaw MD, Painter O. Phonon counting and intensity interferometry of a nanomechanical resonator. p. 1-10. arXiv preprint, arXiv:1410.1047 (2014).
- Vandoorne K, Mechet P, Van Vaerenbergh T, Fiers M, Morthier G, Verstraeten D, Schrauwen B, Dambre J, Bienstman P. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat Commun. 2014;5:1-6. View ArticleGoogle Scholar
- Santori C, Pelc JS, Beausoleil RG, Tezak N, Hamerly R, Mabuchi H. Quantum noise in large-scale coherent nonlinear photonic circuits. Phys Rev Applied. 2014;1:054005. ADSView ArticleGoogle Scholar
- Mabuchi H. Nonlinear interferometry approach to photonic sequential logic. Appl Phys Lett. 2011;99(15):153103. ADSView ArticleGoogle Scholar
- Pavlichin DS, Mabuchi H. Photonic circuits for iterative decoding of a class of low-density parity-check codes. New J Phys. 2013;16:105017. doi:10.1088/1367-2630/16/10/105017. View ArticleGoogle Scholar
- Abraham E, Smith SD. Optical bistability and related devices. Rep Prog Phys. 1982;45:815. ADSView ArticleGoogle Scholar
- Smith SD. Optical bistability: towards the optical computer. Nature. 1984;307(5949):315-6. ADSView ArticleGoogle Scholar
- Miller DAB. Physical reasons for optical interconnection. Int J Optoelectron. 1997;11(3):155-68. Google Scholar
- Miller DAB. Are optical transistors the logical next step? Nat Photonics. 2010;4(1):3-5. ADSView ArticleGoogle Scholar
- Ananthanarayanan R, Esser SK, Simon HD, Modha DS. The cat is out of the bag. In: Proceedings of the conference on high performance computing networking, storage and analysis - SC ’09 (c); 2009. Google Scholar
- Neven H. Hardware initiative at quantum artificial intelligence lab. 2014. http://googleresearch.blogspot.com/2014/09/ucsb-partners-with-google-on-hardware.html. Google Scholar
- Strukov DB, Snider GS, Stewart DR, Williams RS. The missing memristor found. Nature. 2008;453(7191):80-3. ADSView ArticleGoogle Scholar
- Wang Z, Marandi A, Wen K, Byer RL, Yamamoto Y. Coherent Ising machine based on degenerate optical parametric oscillators. Phys Rev A. 2013;88(6):063853. ADSView ArticleGoogle Scholar
- Choudhary S, Sloan S, Fok S, Neckar A, Trautmann E, Gao P, Stewart T, Eliasmith C, Boahen K. Silicon neurons that compute. In: Artificial neural networks and machine learning ICANN 2012. Lecture notes in computer science vol. 7552. Berlin: Springer; 2012. p. 121-8. View ArticleGoogle Scholar
- Cassidy AS, Alvarez-Icaza R, Akopyan F, Sawada J, Arthur JV, Merolla PA, Datta P, Tallada MG, Taba B, Andreopoulos A, Amir A, Esser SK, Kusnitz J, Appuswamy R, Haymes C, Brezzo B, Moussalli R, Bellofatto R, Baks C, Mastro M, Schleupen K, Cox CE, Inoue K, Millman S, Imam N, Mcquinn E, Nakamura YY, Vo I, Guok C, Nguyen D, Lekuch S, Asaad S, Friedman D, Jackson BL, Flickner MD, Risk WP, Manohar R, Modha DS. Real-time scalable cortical computing at 46 giga-synaptic OPS/Watt with \({\sim}100 \times\) speedup in time-to-solution and \({\sim}100\mbox{,}000\times\) reduction in energy-to-solution. In: Proceedings of the international conference for high performance computing, networking, storage and analysis; 2014. p. 27-38. View ArticleGoogle Scholar
- Duport F, Schneider B, Smerieri A, Haelterman M, Massar S. All-optical reservoir computing. Opt Express. 2012;20(20):22783. ADSView ArticleGoogle Scholar
- Vandoorne K, Dambre J, Verstraeten D, Schrauwen B, Bienstman P. Parallel reservoir computing using optical amplifiers. IEEE Trans Neural Netw. 2011;22(9):1469-81. View ArticleGoogle Scholar
- Van Vaerenbergh T, Fiers M, Mechet P, Spuesens T, Kumar R, Morthier G, Schrauwen B, Dambre J, Bienstman P. Cascadable excitability in microrings. Opt Express. 2012;20(18):20292. ADSView ArticleGoogle Scholar
- Dejonckheere A, Duport F, Smerieri A, Fang L, Oudar J-L, Haelterman M, Massar S. All-optical reservoir computer based on saturation of absorption. Opt Express. 2014;22(9):10868. ADSView ArticleGoogle Scholar
- Larger L, Soriano MC, Brunner D, Appeltant L, Gutierrez JM, Pesquera L, Mirasso CR, Fischer I. Photonic information processing beyond turing: an optoelectronic implementation of reservoir computing. Opt Express. 2012;20(3):3241-9. ADSView ArticleGoogle Scholar
- Cover TM. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans Electron Comput. 1965;EC-14(3):326-34. View ArticleMATHGoogle Scholar
- Verstraeten D. Reservoir computing: computation with dynamical systems. PhD thesis (2010). Google Scholar
- Rosenblatt F. The perceptron - a perceiving and recognizing automaton. Report 85, Cornell Aeronautical Laboratory; 1957. Google Scholar
- Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65:386-408. MathSciNetView ArticleGoogle Scholar
- Miller DAB. Novel analog self-electrooptic-effect devices. IEEE J Quantum Electron. 1993;29:678-98. ADSView ArticleGoogle Scholar
- Fok MP, Tian Y, Rosenbluth D, Prucnal PR. Pulse lead/lag timing detection for adaptive feedback and control based on optical spike-timing-dependent plasticity. Opt Lett. 2013;38(4):419-21. ADSView ArticleGoogle Scholar
- Boixo S, Rønnow TF, Isakov SV, Wang Z, Wecker D, Lidar DA, Martinis JM, Troyer M. Evidence for quantum annealing with more than one hundred qubits. Nat Phys. 2014;10(3):218-24. View ArticleGoogle Scholar
- Boixo S, Smelyanskiy VN, Shabani A, Isakov SV, Dykman M, Denchev VS, Amin M, Smirnov A, Mohseni M, Neven H. Computational role of collective tunneling in a quantum annealer. arXiv:1411.4036v1 (2014).
- Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273-97. MATHGoogle Scholar
- Tezak N, Niederberger A, Pavlichin DS, Sarma G, Mabuchi H. Specification of photonic circuits using Quantum Hardware Description Language. Philos Trans R Soc A, Math Phys Eng Sci. 2012;370(1979):5270-90. ADSMathSciNetView ArticleGoogle Scholar
- Gough J, James MRR. The series product and its application to quantum feedforward and feedback networks. IEEE Trans Autom Control. 2009;54(11):2530-44. MathSciNetView ArticleGoogle Scholar
- Gough J, James MR. Quantum feedback networks: Hamiltonian formulation. Commun Math Phys. 2008;287(3):1109-32. ADSMathSciNetView ArticleMATHGoogle Scholar
- Hamerly R, Mabuchi H. Quantum noise of free-carrier dispersion in semiconductor optical cavities. arXiv:1504.04409 (2015).
- Tezak N. Perceptron-files. https://github.com/ntezak/perceptron-files (2014).
- Tait AN, Shastri BJ, Fok MP, Nahmias MA, Prucnal PR. The DREAM: an integrated photonic thresholder. J Lightwave Technol. 2013;31(8):1263-72. ADSView ArticleGoogle Scholar
- Sussillo D, Barak O. Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 2013;25(3):626-49. MathSciNetView ArticleMATHGoogle Scholar
- Savchenkov A, Matsko A, Strekalov D, Mohageg M, Ilchenko V, Maleki L. Low threshold optical oscillations in a whispering gallery mode CaF_{2} resonator. Phys Rev Lett. 2004;93(24):243905. ADSView ArticleGoogle Scholar
- Poustie AJJ, Blow KJJ. Demonstration of an all-optical Fredkin gate. Opt Commun. 2000;174(1-4):317-20. ADSView ArticleGoogle Scholar
- Milburn G. Quantum optical Fredkin gate. Phys Rev Lett. 1989;62(18):2124-7. ADSView ArticleGoogle Scholar
- Fredkin E, Toffoli T. Conservative logic. Int J Theor Phys. 1982;21(3/4):219-53. MathSciNetView ArticleMATHGoogle Scholar
- Tezak N. QHDLJ. https://bitbucket.org/ntezak/qhdlj.jl (2014).
- Bezanson J, Edelman A, Karpinski S, Shah VBJ. Julia: A fresh approach to numerical computing. arXiv:1411.1607 (2014).
- Lattner C, Adve V. LLVM: a compilation framework for lifelong program analysis and transformation. In: International symposium on code generation and optimization, 2004. CGO 2004; 2014. p. 75-86. Google Scholar
- Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen. 1936;7(2):179-88. View ArticleGoogle Scholar
- McLachlan GJ. Discriminant analysis and statistical pattern recognition. Wiley series in probability and statistics. Hoboken: Wiley; 1992. View ArticleMATHGoogle Scholar
- Levy JS, Gondarenko A, Foster MA, Turner-Foster AC, Gaeta AL, Lipson M. CMOS-compatible multiple-wavelength oscillator for on-chip optical interconnects. Nat Photonics. 2009;4(1):37-40. ADSView ArticleGoogle Scholar
- Graham R, Haken H. The quantum-fluctuations of the optical parametric oscillator. I. Z Phys. 1968;210(3):276-302. ADSView ArticleGoogle Scholar