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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.

Following the simple observation that the interconnection of a set of quantum optical input-output devices can be specified using structural mode VHSIC hardware description language, we demonstrate a computer-aided schematic capture workflow for modelling and simulating multi-component photonic circuits. We describe an algorithm for parsing circuit descriptions to derive quantum equations of motion, illustrate our approach using simple examples based on linear and cavity-nonlinear optical components, and demonstrate a computational approach to hierarchical model reduction.

In the quest for creating \"quantum enhanced\" systems for information processing many currently pursued design strategies are difficult to scale significantly beyond a few dozen qubits. The dominant design paradigm relies on starting with near perfect quantum components and a vast overhead of classical external control. In my thesis I present tools and methods for a more integrated framework which treats quantum and hybrid quantum-classical systems on equal footing.We have recently defined a Quantum Hardware Description Language (QHDL) capable of describing networks of interconnected open quantum systems. QHDL is compiled to symbolic and numerical system models by a custom software tool suite named QNET. This allows us to rapidly iterate over quantum network designs and derive the associated equations of motion.Building on a recently developed model reduction technique for describing networks of nonlinear oscillators in the semi-classical regime, I present a library of nonlinear optical circuit designs useful for all-optical computation. I further present an end-to-end theoretical proposal to create all-optical neuromorphic circuits capable of supervised learning. The system is hierarchically composed of tunable linear amplifiers, analog phase memories and thresholding non-linear circuits which can be used to construct more general quantum feedback networks for nonlinear information processing.Finally, I introduce a novel model transformation capable of dividing the description of quantum states into a low-dimensional quasi-classical part coupled to a lower complexity quantum state. This approach is exact and naturally tailored to simulating coupled quantum systems with varying degrees of dissipation.

In this note we extend the definition of the Information Processing Capacity (IPC) by Dambre et al [1] to include the effects of stochastic reservoir dynamics. We quantify the degradation of the IPC in the presence of this noise. [1] Dambre et al. Scientific Reports 2, 514, (2012)

Weakly nonlinear degrees of freedom in dissipative quantum systems tend to localize near manifolds of quasi-classical states. We present a family of analytical and computational methods for deriving optimal unitary model transformations based on representations of finite dimensional Lie groups. The transformations are optimal in that they minimize the quantum relative entropy distance between a given state and the quasi-classical manifold. This naturally splits the description of quantum states into quasi-classical coordinates that specify the nearest quasi-classical state and a transformed quantum state that can be represented in fewer basis levels. We derive coupled equations of motion for the coordinates and the transformed state and demonstrate how this can be exploited for efficient numerical simulation. Our optimization objective naturally quantifies the non-classicality of states occurring in some given open system dynamics. This allows us to compare the intrinsic complexity of different open quantum systems.

In order to support near-term applications of quantum computing, a new compute paradigm has emerged--the quantum-classical cloud--in which quantum computers (QPUs) work in tandem with classical computers (CPUs) via a shared cloud infrastructure. In this work, we enumerate the architectural requirements of a quantum-classical cloud platform, and present a framework for benchmarking its runtime performance. In addition, we walk through two platform-level enhancements, parametric compilation and active qubit reset, that specifically optimize a quantum-classical architecture to support variational hybrid algorithms (VHAs), the most promising applications of near-term quantum hardware. Finally, we show that integrating these two features into the Rigetti Quantum Cloud Services (QCS) platform results in considerable improvements to the latencies that govern algorithm runtime.

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.

We show that autoregressive language models can learn to infill text after we apply a straightforward transformation to the dataset, which simply moves a span of text from the middle of a document to its end. While this data augmentation has garnered much interest in recent years, we provide extensive evidence that training models with a large fraction of data transformed in this way does not harm the original left-to-right generative capability, as measured by perplexity and sampling evaluations across a wide range of scales. Given the usefulness, simplicity, and efficiency of training models to fill-in-the-middle (FIM), we suggest that future autoregressive language models be trained with FIM by default. To this end, we run a series of ablations on key hyperparameters, such as the data transformation frequency, the structure of the transformation, and the method of selecting the infill span. We use these ablations to prescribe strong default settings and best practices to train FIM models. We have released our best infilling model trained with best practices in our API, and release our infilling benchmarks to aid future research.

We show that the ability to make direct measurements of momentum, in addition to the usual direct measurements of position, allows a simple configuration of two identical mechanical oscillators to be used for broadband back-action-free force metrology. This would eliminate the need for an optical reference oscillator in the scheme of Tsang and Caves [Phys. Rev. Lett. 105, 123601 (2010)], along with its associated disadvantages. We also show that if one is restricted to position measurements alone then two copies of the same two-oscillator configuration can be used for narrow-band back-action-free force metrology.

We investigate the spectral properties of a finite laser-driven lattice of ultracold Rydberg atoms exploiting the dipole blockade effect in the frozen Rydberg gas regime. Uniform one-dimensional lattices as well as lattices with variable spacings are considered. In the case of a weak laser coupling, we find a multitude of many-body Rydberg states with well-defined excitation properties which are adiabatically accessible starting from the ground state. A comprehensive analysis of the degeneracies of the spectrum as well as of the single and pair excitations numbers of the eigenstates is performed. In the strong laser regime, analytical solutions for the pseudo-fermionic eigenmodes are derived. Perturbative energy corrections for this approximative approach are provided.