arXiv subjects

Alex Fischer

Publications and source records attributed to Alex Fischer.

6 recordsLinked to original sources

arXiv2026

Frequency-Time Multiplexing for Near-Deterministic Generation of n-Photon Frequency-Bin States

One of the primary challenges of photonic quantum information processing is the on-demand preparation of multiple single-photon-level quantum states from probabilistic photon pair sources. Motivated by recent developments in frequency-bin-encoded photonic quantum information processing, here we consider active time multiplexing to generate n-photon states, where n single photons with n distinct frequencies occupy the same spatiotemporal mode. We devise an approach that uses optical quantum memories to manipulate the temporal mode of heralded single photons and an array of fiber Bragg grating reflectors to jointly manipulate the frequency and temporal modes of the photons, overlapping n photons in n separate frequency bins into a single spatiotemporal mode. We calculate multiphoton state generation rates that, accounting for loss, are realistically achievable with commercially available hardware. Using only a single free-space switchable delay loop for an optical quantum memory, this scheme could feasibly produce 8-photon states at an average rate of 1 kHz.

quant-ph↗

arXiv2024

Bayesian Adaptive Trials for Social Policy

This paper proposes Bayesian Adaptive Trials (BAT) as both an efficient method to conduct trials and a unifying framework for evaluation social policy interventions, addressing limitations inherent in traditional methods such as Randomized Controlled Trials (RCT). Recognizing the crucial need for evidence-based approaches in public policy, the proposal aims to lower barriers to the adoption of evidence-based methods and align evaluation processes more closely with the dynamic nature of policy cycles. BATs, grounded in decision theory, offer a dynamic, ``learning as we go'' approach, enabling the integration of diverse information types and facilitating a continuous, iterative process of policy evaluation. BATs' adaptive nature is particularly advantageous in policy settings, allowing for more timely and context-sensitive decisions. Moreover, BATs' ability to value potential future information sources positions it as an optimal strategy for sequential data acquisition during policy implementation. While acknowledging the assumptions and models intrinsic to BATs, such as prior distributions and likelihood functions, the paper argues that these are advantageous for decision-makers in social policy, effectively merging the best features of various methodologies.

stat.AP↗

arXiv2023

Hardness results for decoding the surface code with Pauli noise

Real quantum computers will be subject to complicated, qubit-dependent noise, instead of simple noise such as depolarizing noise with the same strength for all qubits. We can do quantum error correction more effectively if our decoding algorithms take into account this prior information about the specific noise present. This motivates us to consider the complexity of surface code decoding where the input to the decoding problem is not only the syndrome-measurement results, but also a noise model in the form of probabilities of single-qubit Pauli errors for every qubit. In this setting, we show that quantum maximum likelihood decoding (QMLD) and degenerate quantum maximum likelihood decoding (DQMLD) for the surface code are NP-hard and #P-hard, respectively. We reduce directly from SAT for QMLD, and from #SAT for DQMLD, by showing how to transform a boolean formula into a qubit-dependent Pauli noise model and set of syndromes that encode the satisfiability properties of the formula. We also give hardness of approximation results for QMLD and DQMLD. These are worst-case hardness results that do not contradict the empirical fact that many efficient surface code decoders are correct in the average case (i.e., for most sets of syndromes and for most reasonable noise models). These hardness results are nicely analogous with the known hardness results for QMLD and DQMLD for arbitrary stabilizer codes with independent $X$ and $Z$ noise.

quant-ph↗

arXiv2021

Semi-Supervised Few-Shot Intent Classification and Slot Filling

Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such systems is not scalable. This problem can be addressed by learning from few examples using fast supervised meta-learning techniques such as prototypical networks. In this work, we systematically investigate how contrastive learning and unsupervised data augmentation methods can benefit these existing supervised meta-learning pipelines for jointly modelled IC/SF tasks. Through extensive experiments across standard IC/SF benchmarks (SNIPS and ATIS), we show that our proposed semi-supervised approaches outperform standard supervised meta-learning methods: contrastive losses in conjunction with prototypical networks consistently outperform the existing state-of-the-art for both IC and SF tasks, while data augmentation strategies primarily improve few-shot IC by a significant margin.

cs.CL↗

arXiv2020

Distributing Graph States Across Quantum Networks

Graph states are an important class of multipartite entangled quantum states. We propose a new approach for distributing graph states across a quantum network. We consider a quantum network consisting of nodes-quantum computers within which local operations are free-and EPR pairs shared between nodes that can continually be generated. We prove upper bounds for our approach on the number of EPR pairs consumed, number of timesteps taken, and amount of classical communication required, all of which are equal to or better than that of prior work. We also reduce the problem of minimizing the number of timesteps taken to distribute a graph state using our approach to a network flow problem having polynomial time complexity.

quant-ph↗

arXiv2017

A Real-Time Solver For Time-Optimal Control Of Omnidirectional Robots with Bounded Acceleration

We are interested in the problem of time-optimal control of omnidirectional robots with bounded acceleration (TOC-ORBA). While there exist approximate solutions for such robots, and exact solutions with unbounded acceleration, exact solvers to the TOC-ORBA problem have remained elusive until now. In this paper, we present a real-time solver for true time-optimal control of omnidirectional robots with bounded acceleration. We first derive the general parameterized form of the solution to the TOC-ORBA problem by application of Pontryagin's maximum principle. We then frame the boundary value problem of TOC-ORBA as an optimization problem over the parametrized control space. To overcome local minima and poor initial guesses to the optimization problem, we introduce a two-stage optimal control solver (TSOCS): The first stage computes an upper bound to the total time for the TOC-ORBA problem and holds the time constant while optimizing the parameters of the trajectory to approach the boundary value conditions. The second stage uses the parameters found by the first stage, and relaxes the constraint on the total time to solve for the parameters of the complete TOC-ORBA problem. We further implement TSOCS as a closed loop controller to overcome actuation errors on real robots in real-time. We empirically demonstrate the effectiveness of TSOCS in simulation and on real robots, showing that 1) it runs in real time, generating solutions in less than 0.5ms on average; 2) it generates faster trajectories compared to an approximate solver; and 3) it is able to solve TOC-ORBA problems with non-zero final velocities that were previously unsolvable in real-time.

cs.RO↗