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Dissipative cat qubits are a promising physical platform for quantum computing, since their large noise bias can enable more hardware-efficient quantum error correction. In this work we theoretically study the long-term prospects of a hybrid cat-transmon quantum computing architecture where dissipative cat qubits play the role of data qubits, and error syndromes are measured using ancillary transmon qubits. The cat qubits' noise bias enables more hardware-efficient quantum error correction, and the use of transmons allows for practical, high-fidelity syndrome measurement. While correction of the dominant cat Z errors with a repetition code has recently been demonstrated in experiment, here we show how the architecture can be scaled beyond a repetition code. In particular, we propose a cat-transmon entangling gate that enables the correction of residual cat X errors in a thin rectangular surface code, so that logical error can be arbitrarily suppressed by increasing code distance. We numerically estimate logical memory performance, finding significant overhead reductions in comparison to architectures without biased noise. For example, with current state-of-the-art coherence, physical error rates of $10^{-3}$ and noise biases in the range $10^{3} - 10^{4}$ are achievable. With this level of performance, the qubit overhead required to reach algorithmically-relevant logical error rates with the cat-transmon architecture matches that of an unbiased-noise architecture with physical error rates in the range $10^{-5} - 10^{-4}$., Comment: 13+14 pages, 8+9 figures

This article introduces a novel non-orthogonal multiple access (NOMA) scheme for coordinated uplink channels. The scheme builds on the recently proposed sparse-regression low-density parity-check (SR-LDPC) code, and extends the underlying notions to scenarios with many concurrent users. The resulting scheme, called Multi-User SR-LDPC (MU-SR-LDPC) coding, consists of each user transmitting its own SR-LDPC codeword using a unique sensing matrix in conjunction with a characteristic outer LDPC code. To recover the sent information, the decoder jointly processes the received signals using a low-complexity and highly-parallelizable AMP-BP algorithm. At finite blocklengths (FBL), MU-SR-LDPC codes are shown to achieve a target BER at a higher spectral efficiency than baseline orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes with similar computational complexity. Furthermore, MU-SR-LDPC codes are shown to match the performance of maximum a posteriori (MAP) decoding in certain regimes. For certain blocklengths and a sufficiently high number of users, MU-SR-LDPC codes may achieve a higher spectral efficiency than the approximate FBL capacity of the effective single-user Gaussian channel seen by each user in a comparable OMA scheme. Results are supported by numerical simulations., Comment: This work has been submitted to the IEEE for possible publication

One of the unspoken challenges of tractography is choosing the right parameters for a given dataset or bundle. In order to tackle this challenge, we explore the multi-dimensional parameter space of tractography using streamline-specific parameters (SSP). We 1) validate a state-of-the-art probabilistic tracking method using per-streamline parameters on synthetic data, and 2) show how we can gain insights into the parameter space by focusing on streamline acceptance using real-world data. We demonstrate the potential added value of SSP to the current state of tractography by showing how SSP can be used to reveal patterns in the parameter space., Comment: Accepted at MICCAI 2024 International Workshop on Computational Diffusion MRI

Operational meteorological forecasting has long relied on physics-based numerical weather prediction (NWP) models. Recently, this landscape is facing disruption by the advent of data-driven artificial intelligence (AI)-based weather models, which offer tremendous computational performance and competitive forecasting skill. However, data-driven models for medium-range forecasting generally suffer from major limitations, including low effective resolution and a narrow range of predicted variables. This study illustrates the relative strengths and weaknesses of these competing paradigms using the GEM (Global Environmental Multiscale) and GraphCast models to represent physics-based and AI-based approaches, respectively. By analyzing global predictions from these two models against observations and analyses in both physical and spectral spaces, this study demonstrates that GraphCast-predicted large scales outperform GEM, particularly for longer lead times. Building on this insight, a hybrid NWP-AI system is proposed, wherein GEM-predicted large-scale state variables are spectrally nudged toward GraphCast predictions, while allowing GEM to freely generate fine-scale details critical for weather extremes. Results indicate that this hybrid approach is capable of leveraging the strengths of GraphCast to enhance the prediction skill of the GEM model. Importantly, trajectories of tropical cyclones are predicted with enhanced accuracy without significant changes in intensity. Furthermore, this new hybrid system ensures that meteorologists have access to a complete set of forecast variables, including those relevant for high-impact weather events.

Plouffe conjectured rapidly converging series formulas for $\pi^{2n+1}$ and $\zeta (2n+1)$ for small values of $n$. We find the general pattern for all nonnegative integer values of $n$ and offer a proof., Comment: Revised version of an article published in the Journal of Integer Sequences (Article 11.2.5). Comments welcome

In [1], the linked loop code (LLC) is presented as a promising code for the unsourced A-channel with erasures (UACE). The UACE is an unsourced multiple access channel in which active users' transmitted symbols are erased with a given probability and the channel output is obtained as the union of the non-erased symbols. In this paper, we extend the UACE channel model to the unsourced B-channel with erasures (UBCE). The UBCE differs from the UACE in that the channel output is the multiset union, or bag union, of the non-erased input symbols. In other words, the UBCE preserves the symbol multiplicity of the channel output while the UACE does not. Both the UACE and UBCE find applications in modeling aspects of unsourced random access. The LLC from [1] is enhanced and shown to outperform the tree code over the UBCE. Findings are supported by numerical simulations., Comment: 5 pages, 2 figures, accepted by ICASSP 2024

This report summarizes some considerations on possible evolutions of grant-free random access in the next generation of the 3GPP wireless cellular standard. The analysis is carried out by mapping the problem to the recently-introduced unsourced multiple access channel (UMAC) setup. By doing so, the performance of existing solutions can be benchmarked with information-theoretic bounds, assessing the potential gains that can be achieved over legacy 3GPP schemes. The study focuses on the two-step random access (2SRA) protocol introduced by Release 16 of the 5G New Radio standard, investigating its applicability to support large MTC / IoT terminal populations in a grant-free fashion. The analysis shows that the existing 2SRA scheme may not succeed in providing energy-efficient support to large user populations. Modifications to the protocol are proposed that enable remarkable gains in both energy and spectral efficiency while retaining a strong resemblance to the legacy protocol., Comment: Version 1.0 of the report

Novel sparse regression LDPC (SR-LDPC) codes exhibit excellent performance over additive white Gaussian noise (AWGN) channels in part due to their natural provision of shaping gains. Though SR-LDPC-like codes have been considered within the context of single-user error correction and massive random access, they are yet to be examined as candidates for coordinated multi-user communication scenarios. This article explores this gap in the literature and demonstrates that SR-LDPC codes, when combined with coded demixing techniques, offer a new framework for efficient non-orthogonal multiple access (NOMA) in the context of coordinated multi-user communication channels. The ensuing communication scheme is referred to as MU-SR-LDPC coding. Empirical evidence suggests that, for a fixed SNR, MU-SR-LDPC coding can achieve a target bit error rate (BER) at a higher sum rate than orthogonal multiple access (OMA) techniques such as time division multiple access (TDMA) and frequency division multiple access (FDMA). Importantly, MU-SR-LDPC codes enable a pragmatic solution path for user-centric cell-free communication systems with (local) joint decoding. Results are supported by numerical simulations., Comment: Submitted to ISIT 2024

This article introduces a novel concatenated coding scheme called sparse regression LDPC (SR-LDPC) codes. An SR-LDPC code consists of an outer non-binary LDPC code and an inner sparse regression code (SPARC) whose respective field size and section sizes are equal. For such codes, an efficient decoding algorithm is proposed based on approximate message passing (AMP) that dynamically shares soft information between inner and outer decoders. This dynamic exchange of information is facilitated by a denoiser that runs belief propagation (BP) on the factor graph of the outer LDPC code within each AMP iteration. It is shown that this denoiser falls within the class of non-separable pseudo-Lipschitz denoising functions and thus that state evolution holds for the proposed AMP-BP algorithm. Leveraging the rich structure of SR-LDPC codes, this article proposes an efficient low-dimensional approximate state evolution recursion that can be used for efficient hyperparameter tuning, thus paving the way for future work on optimal code design. Finally, numerical simulations demonstrate that SR-LDPC codes outperform contemporary codes over the AWGN channel for parameters of practical interest. SR-LDPC codes are shown to be viable means to obtain shaping gains over the AWGN channel., Comment: This work has been submitted to the IEEE for possible publication. arXiv admin note: substantial text overlap with arXiv:2301.01899

We are entering an era in which we will be able to detect and characterize hundreds of dwarf galaxies within the Local Volume. It is already known that a strong dichotomy exists in the gas content and star formation properties of field dwarf galaxies versus satellite dwarfs of larger galaxies. In this work, we study the more subtle differences that may be detectable in galaxies as a function of distance from a massive galaxy, such as the Milky Way. We compare smoothed particle hydrodynamic simulations of dwarf galaxies formed in a Local Volume-like environment (several Mpc away from a massive galaxy) to those formed nearer to Milky Way-mass halos. We find that the impact of environment on dwarf galaxies extends even beyond the immediate region surrounding Milky Way-mass halos. Even before being accreted as satellites, dwarf galaxies near a Milky Way-mass halo tend to have higher stellar masses for their halo mass than more isolated galaxies. Dwarf galaxies in high-density environments also tend to grow faster and form their stars earlier. We show observational predictions that demonstrate how these trends manifest in lower quenching rates, higher HI fractions, and bluer colors for more isolated dwarf galaxies., Comment: 26 pages, 14 figures, submitted to ApJ

Pre-trained transformers exhibit the capability of adapting to new tasks through in-context learning (ICL), where they efficiently utilize a limited set of prompts without explicit model optimization. The canonical communication problem of estimating transmitted symbols from received observations can be modelled as an in-context learning problem: Received observations are essentially a noisy function of transmitted symbols, and this function can be represented by an unknown parameter whose statistics depend on an (also unknown) latent context. This problem, which we term in-context estimation (ICE), has significantly greater complexity than the extensively studied linear regression problem. The optimal solution to the ICE problem is a non-linear function of the underlying context. In this paper, we prove that, for a subclass of such problems, a single layer softmax attention transformer (SAT) computes the optimal solution of the above estimation problem in the limit of large prompt length. We also prove that the optimal configuration of such transformer is indeed the minimizer of the corresponding training loss. Further, we empirically demonstrate the proficiency of multi-layer transformers in efficiently solving broader in-context estimation problems. Through extensive simulations, we show that solving ICE problems using transformers significantly outperforms standard approaches. Moreover, just with a few context examples, it achieves the same performance as an estimator with perfect knowledge of the latent context., Comment: 13 pages, 2 figures, 2 tables, preprint; abstract, references, theory updated

The A-channel is a noiseless multiple access channel in which users simultaneously transmit Q-ary symbols and the receiver observes the set of transmitted symbols, but not their multiplicities. An A-channel is said to be unsourced if, additionally, users transmissions are encoded across time using a common codebook and decoding of the transmitted messages is done without regard to the identities of the active users. An interesting variant of the unsourced A-channel is the unsourced A-channel with erasures (UACE), in which transmitted symbols are erased with a given independent and identically distributed probability. In this paper, we focus on designing a code that enables a list of transmitted codewords to be recovered despite the erasures of some of the transmitted symbols. To this end, we propose the linked-loop code (LLC), which uses parity bits to link each symbol to the previous M symbols in a tail-biting manner, i.e., the first symbols of the transmission are linked to the last ones. The decoding process occurs in two phases: the first phase decodes the codewords that do not suffer from any erasures, and the second phase attempts to recover the erased symbols using the available parities. We compare the performance of the LLC over the UACE with other codes in the literature and argue for the effectiveness of the construction. Our motivation for studying the UACE comes from its relevance in machine-type communication and coded compressed sensing., Comment: 5 pages, 4 figures, to be published in the 31st European Signal Processing Conference, EUSIPCO 2023

As tractography datasets continue to grow in size, there is a need for improved visualization methods that can capture structural patterns occurring in large tractography datasets. Transparency is an increasingly important aspect of finding these patterns in large datasets but is inaccessible to tractography due to performance limitations. In this paper, we propose a rendering method that achieves performant rendering of transparent streamlines, allowing for exploration of deeper brain structures interactively. The method achieves this through a novel approximate order-independent transparency method that utilizes voxelization and caching view-dependent line orders per voxel. We compare our transparency method with existing tractography visualization software in terms of performance and the ability to capture deeper structures in the dataset., Comment: 12 pages. 4 figures. Accepted at Computational Diffusion MRI Workshop (CDMRI) at Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023

We present a novel way to model diffusion magnetic resonance imaging (dMRI) datasets, that benefits from the structural coherence of the human brain while only using data from a single subject. Current methods model the dMRI signal in individual voxels, disregarding the intervoxel coherence that is present. We use a neural network to parameterize a spherical harmonics series (NeSH) to represent the dMRI signal of a single subject from the Human Connectome Project dataset, continuous in both the angular and spatial domain. The reconstructed dMRI signal using this method shows a more structurally coherent representation of the data. Noise in gradient images is removed and the fiber orientation distribution functions show a smooth change in direction along a fiber tract. We showcase how the reconstruction can be used to calculate mean diffusivity, fractional anisotropy, and total apparent fiber density. These results can be achieved with a single model architecture, tuning only one hyperparameter. In this paper we also demonstrate how upsampling in both the angular and spatial domain yields reconstructions that are on par or better than existing methods., Comment: 12 pages, 6 figures, accepted for cdMRI workshop at MICCAI 2023 Updated to fix typo in author name (Villanova -> Vilanova)

We provide new estimates of an asymptotic upper bound on the entropy of English using the large language model LLaMA-7B as a predictor for the next token given a window of past tokens. This estimate is significantly smaller than currently available estimates in \cite{cover1978convergent}, \cite{lutati2023focus}. A natural byproduct is an algorithm for lossless compression of English text which combines the prediction from the large language model with a lossless compression scheme. Preliminary results from limited experiments suggest that our scheme outperforms state-of-the-art text compression schemes such as BSC, ZPAQ, and paq8h., Comment: 7 pages, 4 figures, 4 tables, preprint, added results on using LLMs with arithmetic coding

Cell-Free Massive MIMO systems aim to expand the coverage area of wireless networks by replacing a single high-performance Access Point (AP) with multiple small, distributed APs connected to a Central Processing Unit (CPU) through a fronthaul. Another novel wireless approach, known as the unsourced random access (URA) paradigm, enables a large number of devices to communicate concurrently on the uplink. We consider a quasi-static Rayleigh fading channel paired to a scalable cell-free system, wherein a small number of receive antennas in the distributed APs serve devices equipped with a single antenna each. The goal of the study is to extend previous URA results to more realistic channels by examining the performance of a scalable cell-free system. To achieve this goal, we construct a coding scheme that adapts the URA paradigm to various cell-free scenarios. Empirical evidence suggests that using a cell-free architecture can improve the performance of a URA system, especially when taking into account large-scale attenuation and fading., Comment: Changed the channel model

Quantum error correction (QEC) plays an essential role in fault-tolerantly realizing quantum algorithms of practical interest. Among different approaches to QEC, encoding logical quantum information in harmonic oscillator modes has been shown to be promising and hardware efficient. In this work, we study multimode Gottesman-Kitaev-Preskill (GKP) codes, encoding a qubit in many oscillators, through a lattice perspective. In particular, we implement a closest point decoding strategy for correcting random Gaussian shift errors. For decoding a generic multimode GKP code, we first identify its corresponding lattice followed by finding the closest lattice point in its symplectic dual lattice to a candidate shift error compatible with the error syndrome. We use this method to characterize the error correction capabilities of several known multimode GKP codes, including their code distances and fidelities. We also perform numerical optimization of multimode GKP codes up to ten modes and find three instances (with three, seven and nine modes) with better code distances and fidelities compared to the known GKP codes with the same number of modes. While exact closest point decoding incurs exponential time cost in the number of modes for general unstructured GKP codes, we give several examples of structured GKP codes (i.e., of the repetition-rectangular GKP code types) where the closest point decoding can be performed exactly in linear time. For the surface-GKP code, we show that the closest point decoding can be performed exactly in polynomial time with the help of a minimum-weight-perfect-matching algorithm (MWPM). We show that this MWPM closest point decoder improves both the fidelity and the noise threshold of the surface-GKP code to 0.602 compared to the previously studied MWPM decoder assisted by log-likelihood analog information which yields a noise threshold of 0.599., Comment: Correct the mistake in Eq. 118, and a number of typos

Quantum computing is poised to solve practically useful problems which are computationally intractable for classical supercomputers. However, the current generation of quantum computers are limited by errors that may only partially be mitigated by developing higher-quality qubits. Quantum error correction (QEC) will thus be necessary to ensure fault tolerance. QEC protects the logical information by cyclically measuring syndrome information about the errors. An essential part of QEC is the decoder, which uses the syndrome to compute the likely effect of the errors on the logical degrees of freedom and provide a tentative correction. The decoder must be accurate, fast enough to keep pace with the QEC cycle (e.g., on a microsecond timescale for superconducting qubits) and with hard real-time system integration to support logical operations. As such, real-time decoding is essential to realize fault-tolerant quantum computing and to achieve quantum advantage. In this work, we highlight some of the key challenges facing the implementation of real-time decoders while providing a succinct summary of the progress to-date. Furthermore, we lay out our perspective for the future development and provide a possible roadmap for the field of real-time decoding in the next few years. As the quantum hardware is anticipated to scale up, this perspective article will provide a guidance for researchers, focusing on the most pressing issues in real-time decoding and facilitating the development of solutions across quantum and computer science.

We explore a scheme that enables the training of a deep neural network in a Federated Learning configuration over an additive white Gaussian noise channel. The goal is to create a low complexity, linear compression strategy, called PolarAir, that reduces the size of the gradient at the user side to lower the number of channel uses needed to transmit it. The suggested approach belongs to the family of compressed sensing techniques, yet it constructs the sensing matrix and the recovery procedure using multiple access techniques. Simulations show that it can reduce the number of channel uses by ~30% when compared to conveying the gradient without compression. The main advantage of the proposed scheme over other schemes in the literature is its low time complexity. We also investigate the behavior of gradient updates and the performance of PolarAir throughout the training process to obtain insight on how best to construct this compression scheme based on compressed sensing.

Belief propagation applied to iterative decoding and sparse recovery through approximate message passing (AMP) are two research areas that have seen monumental progress in recent decades. Inspired by these advances, this article introduces sparse regression LDPC codes and their decoding. Sparse regression codes (SPARCs) are a class of error correcting codes that build on ideas from compressed sensing and can be decoded using AMP. In certain settings, SPARCs are known to achieve capacity; yet, their performance suffers at finite block lengths. Likewise, LDPC codes can be decoded efficiently using belief propagation and can also be capacity achieving. This article introduces a novel concatenated coding structure that combines an LDPC outer code with a SPARC-inspired inner code. Efficient decoding for such a code can be achieved using AMP with a denoiser that performs belief propagation on the factor graph of the outer LDPC code. The proposed framework exhibits performance improvements over SPARCs and standard LDPC codes for finite block lengths and results in a steep waterfall in error performance, a phenomenon not observed in uncoded SPARCs. Findings are supported by numerical results.

PURPOSE: To update and extend version 2 of the Carleton Laboratory for Radiotherapy Physics (CLRP) TG-43 dosimetry database (CLRP_TG43v2) for 33 high-energy (HE, $\geq50$~keV) brachytherapy sources using egs_brachy, an open-source EGSnrc application. A comprehensive dataset of TG-43 parameters is compiled, including detailed source descriptions, dose-rate constants, radial dose functions, 1D and 2D anisotropy functions, along-away dose-rate tables, Primary and Scatter Separated (PSS) dose tables, and mean photon energies escaping each source. The database also documents the source models which will be freely distributed with egs_brachy. ACQUISITION AND VALIDATION METHODS: Datasets are calculated after a recoding of the source geometries using the egs++ geometry package and its egs_brachy extensions. Air kerma per history is calculated in a 10x10x0.05 cm3 voxel located 100 cm from the source along the transverse axis and then corrected for the lateral and thickness dimensions of the scoring voxel to give the air kerma on the central axis at a point 100cm from the source's mid-point. Full-scatter water phantoms with varying voxel resolutions in cylindrical coordinates are used for dose calculations. For validation, data are compared to those from CLRP_TG43v1 and published data. DATA FORMAT AND ACCESS: Data are available at https://physics.carleton.ca/clrp/egs_brachy/seed_database_HDRv2 or http://doi.org/10.22215/clrp/tg43v2. POTENTIAL APPLICATIONS: The CLRP_TG43v2 database has applications in research, dosimetry, and brachytherapy planning. This comprehensive update provides the medical physics community with more precise and in some cases more accurate Monte Carlo TG-43 dose calculation parameters, as well as fully benchmarked and described source models which are distributed with egs_brachy., Comment: 28 pages, 11 figures

Fault-tolerant quantum computers, with error correction implemented using topological codes, will most likely require lattice surgery protocols in order to implement a universal gate set. Timelike failures during lattice surgery protocols can result in logical failures during the execution of an algorithm. In addition to the spacelike distance of the topological code used to protect the qubits from errors, there is also the timelike distance which is given by the number of syndrome measurement rounds during a lattice surgery protocol. As such, a larger timelike distance requirement will result in the slowdown of an algorithm's runtime. Temporal encoding of lattice surgery (TELS) is a technique which can be used to reduce the number of syndrome measurement rounds that are required during a lattice surgery protocol. This is done by measuring an over-complete set of mutually commuting multi-qubit Pauli operators (referred to as a parallelizable Pauli set) which form codewords of a classical error correcting code. The results of the over-complete set of Pauli measurements can then be used to detect and possibly correct timelike lattice surgery failures. In this work, we introduce an improved TELS protocol and subsequently augment it with the ability to correct low-weight classical errors, resulting in greater speedups in algorithm runtimes. We also explore large families of classical error correcting codes for a wide range of parallelizable Pauli set sizes. We also apply TELS to magic state distillation protocols in the context of biased noise, where logical qubits are encoded in asymmetric surface codes. Using optimized layouts, we show improvements in the space-time cost of our magic state factories compared to previous protocols. Such improvements are achieved using computations performed in the Clifford frame., Comment: 35 pages, 16 figures

Implementing algorithms on a fault-tolerant quantum computer will require fast decoding throughput and latency times to prevent an exponential increase in buffer times between the applications of gates. In this work we begin by quantifying these requirements. We then introduce the construction of local neural network (NN) decoders using three-dimensional convolutions. These local decoders are adapted to circuit-level noise and can be applied to surface code volumes of arbitrary size. Their application removes errors arising from a certain number of faults, which serves to substantially reduce the syndrome density. Remaining errors can then be corrected by a global decoder, such as Blossom or Union Find, with their implementation significantly accelerated due to the reduced syndrome density. However, in the circuit-level setting, the corrections applied by the local decoder introduce many vertical pairs of highlighted vertices. To obtain a low syndrome density in the presence of vertical pairs, we consider a strategy of performing a syndrome collapse which removes many vertical pairs and reduces the size of the decoding graph used by the global decoder. We also consider a strategy of performing a vertical cleanup, which consists of removing all local vertical pairs prior to implementing the global decoder. Lastly, we estimate the cost of implementing our local decoders on Field Programmable Gate Arrays (FPGAs)., Comment: 28 pages, 24 figures. Comments welcome! V2 Contains a more detailed FPGA analysis

Unsourced random access (URA) has emerged as a candidate paradigm for massive machine-type communication (MTC) in next-generation wireless networks. While many excellent uplink schemes have been developed for URA, these schemes do not specify a mechanism for providing feedback regarding whether a user's message was successfully decoded. While this may be acceptable in some MTC scenarios, the lack of feedback is inadequate for applications that demand a high level of reliability. However, the problem of providing feedback to active users is complicated by the fact that the base station does not know the identities of the active users. In this paper, a novel downlink beamforming scheme called HashBeam is presented that enables the base station to provide feedback to the active users within URA, despite not knowing their identities. The key idea of this scheme is that the users' channels and hashes of their messages may be used as proxies for their true identities. The proposed scheme may be adapted to any number of antennas at the base station and it is shown that the required number of channel uses is linear in the number of users to acknowledge. The idea of using channel gains in conjunction with user hashes as discriminating attributes of active users is novel and expands the design space of URA schemes.

Unsourced random access (URA) is a recently proposed multiple access paradigm tailored to the uplink channel of machine-type communication networks. By exploiting a strong connection between URA and compressed sensing, the massive multiple access problem may be cast as a compressed sensing (CS) problem, albeit one in exceedingly large dimensions. To efficiently handle the dimensionality of the problem, coded compressed sensing (CCS) has emerged as a pragmatic signal processing tool that, when applied to URA, offers good performance at low complexity. While CCS is effective at recovering a signal that is sparse with respect to a single basis, it is unable to jointly recover signals that are sparse with respect to separate bases. In this article, the CCS framework is extended to the demixing setting, yielding a novel technique called coded demixing. A generalized framework for coded demixing is presented and a low-complexity recovery algorithm based on approximate message passing (AMP) is developed. Coded demixing is applied to heterogeneous multi-class URA networks and traditional single-class networks. Its performance is analyzed and numerical simulations are presented to highlight the benefits of coded demixing., Comment: 1053-587X Copyright 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information

This article considers the massive MIMO unsourced random access problem on a quasi-static Rayleigh fading channel. Given a fixed message length and a prescribed number of channel uses, the objective is to construct a coding scheme that minimizes the energy-per-bit subject to a fixed probability of error. The proposed scheme differs from other state-of-the-art schemes in that it blends activity detection, single-user coding, pilot-aided and temporary decisions-aided iterative channel estimation and decoding, minimum-mean squared error (MMSE) estimation, and successive interference cancellation (SIC). We show that an appropriate combination of these ideas can substantially outperform state-of-the-art coding schemes when the number of active users is more than 100, making this the best performing scheme known for this regime.

Universal fault-tolerant quantum computers will require the use of efficient protocols to implement encoded operations necessary in the execution of algorithms. In this work, we show how solvers for satisfiability modulo theories (SMT solvers) can be used to automate the construction of Clifford circuits with certain fault-tolerance properties and we apply our techniques to a fault-tolerant magic-state-preparation protocol. Part of the protocol requires converting magic states encoded in the color code to magic states encoded in the surface code. Since the teleportation step involves decoding a color code merged with a surface code, we develop a decoding algorithm that is applicable to such codes., Comment: 23 pages, 9 figures; v5 conforms to journal specifications

Lattice surgery is a measurement-based technique for performing fault-tolerant quantum computation in two dimensions. When using the surface code, the most general lattice surgery operations require lattice irregularities called twist defects. However, implementing twist-based lattice surgery may require additional resources, such as extra device connectivity, and could lower the threshold and overall performance for the surface code. Here we provide an explicit twist-based lattice surgery protocol and its requisite connectivity layout. We also provide new stabilizer measurement circuits for measuring twist defects which are compatible with our chosen gate scheduling. We undertake the first circuit-level error correction simulations during twist-based lattice surgery using a biased depolarizing noise model. Our results indicate a slight decrease in the threshold for timelike logical failures compared to lattice surgery protocols with no twist defects in the bulk. However, comfortably below threshold (i.e. with CNOT infidelities below $5 \times 10^{-3}$), the performance degradation is mild and in fact preferable over proposed alternative twist-free schemes. Lastly, we provide an efficient scheme for measuring $Y$ operators along boundaries of surface codes which bypasses certain steps that were required in previous schemes., Comment: 12 pages, 9 figures. Comments welcome!

Safe reinforcement learning is extremely challenging--not only must the agent explore an unknown environment, it must do so while ensuring no safety constraint violations. We formulate this safe reinforcement learning (RL) problem using the framework of a finite-horizon Constrained Markov Decision Process (CMDP) with an unknown transition probability function, where we model the safety requirements as constraints on the expected cumulative costs that must be satisfied during all episodes of learning. We propose a model-based safe RL algorithm that we call Doubly Optimistic and Pessimistic Exploration (DOPE), and show that it achieves an objective regret $\tilde{O}(|\mathcal{S}|\sqrt{|\mathcal{A}| K})$ without violating the safety constraints during learning, where $|\mathcal{S}|$ is the number of states, $|\mathcal{A}|$ is the number of actions, and $K$ is the number of learning episodes. Our key idea is to combine a reward bonus for exploration (optimism) with a conservative constraint (pessimism), in addition to the standard optimistic model-based exploration. DOPE is not only able to improve the objective regret bound, but also shows a significant empirical performance improvement as compared to earlier optimism-pessimism approaches., Comment: Accepted to NeurIPS 2022

Unsourced random access (URA) has emerged as a pragmatic framework for next-generation distributed sensor networks. Within URA, concatenated coding structures are often employed to ensure that the central base station can accurately recover the set of sent codewords during a given transmission period. Many URA algorithms employ independent inner and outer decoders, which can help reduce computational complexity at the expense of a decay in performance. In this article, an enhanced decoding algorithm is presented for a concatenated coding structure consisting of a wide range of inner codes and an outer tree-based code. It is shown that this algorithmic enhancement has the potential to simultaneously improve error performance and decrease the computational complexity of the decoder. This enhanced decoding algorithm is applied to two existing URA algorithms and the performance benefits of the algorithm are characterized. Findings are supported by numerical simulations., Comment: Submitted to MDPI Sensors

Lattice surgery protocols allow for the efficient implementation of universal gate sets with two-dimensional topological codes where qubits are constrained to interact with one another locally. In this work, we first introduce a decoder capable of correcting spacelike and timelike errors during lattice surgery protocols. Afterwards, we compute logical failure rates of a lattice surgery protocol for a biased circuit-level noise model. We then provide a new protocol for performing twist-free lattice surgery, where we avoid twist defects in the bulk of the lattice. Our twist-free protocol eliminates the extra circuit components and gate scheduling complexities associated with the measurement of higher weight stabilizers when using twist defects. We also provide a protocol for temporally encoded lattice surgery that can be used to reduce both runtimes and the total space-time costs of quantum algorithms. Lastly, we propose a layout for a quantum processor that is more efficient for rectangular surface codes exploiting noise bias, and which is compatible with the other techniques mentioned above., Comment: 23 pages, 18 figures, comments welcome! V2 includes 2 added figures and fixed typos. V3 fixed a typo in Eq. (10)