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Quantum computing is an emerging technology whose positive and negative impacts on society are not yet fully known. As government, individuals, institutions, and corporations fund and develop this technology, they must ensure that they anticipate its impacts, prepare for its consequences, and steer its development in such a way that it enables the most good and prevents the most harm. However, individual stakeholders are not equipped to fully anticipate these consequences on their own it requires a diverse community that is well-informed about quantum computing and its impacts. Collaborations and community-building across domains incorporating a variety of viewpoints, especially those from stakeholders most likely to be harmed, are fundamental pillars of developing and deploying quantum computing responsibly. This paper reviews responsible quantum computing proposals and literature, highlights the challenges in implementing these, and presents strategies developed at IBM aimed at building a diverse community of users and stakeholders to support the responsible development of this technology.

This paper explores artificial intelligence (AI) methods for the approximate compiling of unitaries, focusing on the use of fixed two-qubit gates and arbitrary single-qubit rotations typical in superconducting hardware. Our approach involves three main stages: identifying an initial template that approximates the target unitary, predicting initial parameters for this template, and refining these parameters to maximize the fidelity of the circuit. We propose AI-driven approaches for the first two stages, with a deep learning model that suggests initial templates and an autoencoder-like model that suggests parameter values, which are refined through gradient descent to achieve the desired fidelity. We demonstrate the method on 2 and 3-qubit unitaries, showcasing promising improvements over exhaustive search and random parameter initialization. The results highlight the potential of AI to enhance the transpiling process, supporting more efficient quantum computations on current and future quantum hardware.

Quantum programs are typically developed using quantum Software Development Kits (SDKs). The rapid advancement of quantum computing necessitates new tools to streamline this development process, and one such tool could be Generative Artificial intelligence (GenAI). In this study, we introduce and use the Qiskit HumanEval dataset, a hand-curated collection of tasks designed to benchmark the ability of Large Language Models (LLMs) to produce quantum code using Qiskit - a quantum SDK. This dataset consists of more than 100 quantum computing tasks, each accompanied by a prompt, a canonical solution, a comprehensive test case, and a difficulty scale to evaluate the correctness of the generated solutions. We systematically assess the performance of a set of LLMs against the Qiskit HumanEval dataset's tasks and focus on the models ability in producing executable quantum code. Our findings not only demonstrate the feasibility of using LLMs for generating quantum code but also establish a new benchmark for ongoing advancements in the field and encourage further exploration and development of GenAI-driven tools for quantum code generation.

Code Large Language Models (Code LLMs) have emerged as powerful tools, revolutionizing the software development landscape by automating the coding process and reducing time and effort required to build applications. This paper focuses on training Code LLMs to specialize in the field of quantum computing. We begin by discussing the unique needs of quantum computing programming, which differ significantly from classical programming approaches or languages. A Code LLM specializing in quantum computing requires a foundational understanding of quantum computing and quantum information theory. However, the scarcity of available quantum code examples and the rapidly evolving field, which necessitates continuous dataset updates, present significant challenges. Moreover, we discuss our work on training Code LLMs to produce high-quality quantum code using the Qiskit library. This work includes an examination of the various aspects of the LLMs used for training and the specific training conditions, as well as the results obtained with our current models. To evaluate our models, we have developed a custom benchmark, similar to HumanEval, which includes a set of tests specifically designed for the field of quantum computing programming using Qiskit. Our findings indicate that our model outperforms existing state-of-the-art models in quantum computing tasks. We also provide examples of code suggestions, comparing our model to other relevant code LLMs. Finally, we introduce a discussion on the potential benefits of Code LLMs for quantum computing computational scientists, researchers, and practitioners. We also explore various features and future work that could be relevant in this context.

In this tutorial paper, we provide an introduction to the briskly expanding research field of Majorana fermions in topological superconductors. We discuss several aspects of topological superconductivity and the advantages it brings to quantum computing. Mathematical derivation of the Kitaev model and BdG Hamiltonian is carried out to explain the phenomena of superconductivity and Majorana fermions. The Majorana fermions and the Non-Abelian statistics are described in detail along with their significance for quantum engineers. The theory provided led towards the engineering of the topological qubits using Majoranas., Comment: More proper mathematical equations are needed to be added to provided comprehensive information and re modify some section

In this paper, we provide an overview of the recent work in the quantum finance realm from various perspectives. The applications in consideration are Portfolio Optimization, Fraud Detection, and Monte Carlo methods for derivative pricing and risk calculation. Furthermore, we give a comprehensive overview of the applications of quantum computing in the field of blockchain technology which is a main concept in fintech. In that sense, we first introduce the general overview of blockchain with its main cryptographic primitives such as digital signature algorithms, hash functions, and random number generators as well as the security vulnerabilities of blockchain technologies after the merge of quantum computers considering Shor's quantum factoring and Grover's quantum search algorithms. We then discuss the privacy preserving quantum-resistant blockchain systems via threshold signatures, ring signatures, and zero-knowledge proof systems i.e. ZK-SNARKs in quantum resistant blockchains. After emphasizing the difference between the quantum-resistant blockchain and quantum-safe blockchain we mention the security countermeasures to take against the possible quantumized attacks aiming these systems. We finalize our discussion with quantum blockchain, efficient quantum mining and necessary infrastructures for constructing such systems based on quantum computing. This review has the intention to be a bridge to fill the gap between quantum computing and one of its most prominent application realms: Finance. We provide the state-of-the-art results in the intersection of finance and quantum technology for both industrial practitioners and academicians., Comment: 64 pages. arXiv admin note: text overlap with arXiv:2211.13191 by other authors

Quantum computing is an emerging technology in which quantum mechanical properties are suitably utilized to perform certain compute-intensive operations faster than classical computers. Quantum algorithms are designed as a combination of quantum circuits that each require a large number of quantum gates, which is a challenge considering the limited number of qubit resources available in quantum computing systems. Our work proposes a technique to optimize quantum arithmetic algorithms by reducing the hardware resources and the number of qubits based on ZX calculus. We have utilised ZX calculus rewrite rules for the optimization of fault-tolerant quantum multiplier circuits where we are able to achieve a significant reduction in the number of ancilla bits and T-gates as compared to the originally required numbers to achieve fault-tolerance. Our work is the first step in the series of arithmetic circuit optimization using graphical rewrite tools and it paves the way for advancing the optimization of various complex quantum circuits and establishing the potential for new applications of the same.

The Kagome lattice, a captivating lattice structure composed of interconnected triangles with frustrated magnetic properties, has garnered considerable interest in condensed matter physics, quantum magnetism, and quantum computing.The Ansatz optimization provided in this study along with extensive research on optimisation technique results us with high accuracy. This study focuses on using multiple ansatz models to create an effective Variational Quantum Eigensolver (VQE) on the Kagome lattice. By comparing various optimisation methods and optimising the VQE ansatz models, the main goal is to estimate ground state attributes with high accuracy. This study advances quantum computing and advances our knowledge of quantum materials with complex lattice structures by taking advantage of the distinctive geometric configuration and features of the Kagome lattice. Aiming to improve the effectiveness and accuracy of VQE implementations, the study examines how Ansatz Modelling, quantum effects, and optimization techniques interact in VQE algorithm. The findings and understandings from this study provide useful direction for upcoming improvements in quantum algorithms,quantum machine learning and the investigation of quantum materials on the Kagome Lattice., Comment: 7 pages,7 figures

Quantum technology is an emerging cutting-edge field which offers a new paradigm for computation and research in the field of physics, mathematics and other scientific disciplines. This technology is of strategic importance to governments globally and heavy investments and budgets are being sanctioned to gain competitive advantage in terms of military, space and education. Due to this, it is important to understand the educational and research needs required to implement this technology at a large scale. Here, we propose a novel universal quantum technology master's curriculum which comprises a balance between quantum hardware and software skills to enhance the employability of professionals thereby reducing the skill shortage faced by the academic institutions and organizations today. The proposed curriculum holds the potential to revolutionize the quantum education ecosystem by reducing the pressure of hiring PhDs faced by startups and promoting the growth of a balanced scientific mindset in quantum research., Comment: 6 pages, 1 table

The Naive Bayesian classifier is a popular classification method employing the Bayesian paradigm. The concept of having conditional dependence among input variables sounds good in theory but can lead to a majority vote style behaviour. Achieving conditional independence is often difficult, and they introduce decision biases in the estimates. In Naive Bayes, certain features are called independent features as they have no conditional correlation or dependency when predicting a classification. In this paper, we focus on the optimal partition of features by proposing a novel technique called the Comonotone-Independence Classifier (CIBer) which is able to overcome the challenges posed by the Naive Bayes method. For different datasets, we clearly demonstrate the efficacy of our technique, where we achieve lower error rates and higher or equivalent accuracy compared to models such as Random Forests and XGBoost., Comment: 7 pages, 13 figures

We introduce a new synthetic data generator PSP-HDRI$+$ that proves to be a superior pre-training alternative to ImageNet and other large-scale synthetic data counterparts. We demonstrate that pre-training with our synthetic data will yield a more general model that performs better than alternatives even when tested on out-of-distribution (OOD) sets. Furthermore, using ablation studies guided by person keypoint estimation metrics with an off-the-shelf model architecture, we show how to manipulate our synthetic data generator to further improve model performance., Comment: PSP-HDRI$+$ template Unity environment, benchmark binaries, and source code will be made available at: https://github.com/Unity-Technologies/PeopleSansPeople

In recent years, the use of deep learning in language models gained much attention. Some research projects claim that they can generate text that can be interpreted as human-writing, enabling new possibilities in many application areas. Among the different areas related to language processing, one of the most notable in applying this type of modeling is programming languages. For years, the Machine Learning community has been researching this software engineering area, pursuing goals like applying different approaches to auto-complete, generate, fix, or evaluate code programmed by humans. Considering the increasing popularity of the Deep-Learning-enabled language models approach, we detected a lack of empirical papers that compare different deep learning architectures to create and use language models based on programming code. This paper compares different neural network architectures like AWD-LSTMs, AWD-QRNNs, and Transformer while using transfer learning and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks. Considering the results, we discuss each approach's different strengths and weaknesses and what gaps we find to evaluate the language models or apply them in a real programming context.

In this paper, we consider the transmitter optimization problem in slow fading multiple-input-single-output (MISO) wiretap channel. The source transmits a secret message intended for $K$ users in the presence of $J$ non-colluding eavesdroppers, and operates under a total power constraint. The channels between the source and all users and eavesdroppers are assumed to be slow fading, and only statistical channel state information (CSI) is known at the source. For a given code rate and secrecy rate pair of the wiretap code, denoted by $(R_{D}, R_{s})$, we define the non-outage event as the joint event of the link information rates to $K$ users be greater than or equal to $R_{D}$ and the link information rates to $J$ eavesdroppers be less than or equal to $(R_{D} - R_{s})$. We minimize the transmit power subject to the total power constraint and satisfying the probability of the non-outage event to be greater than or equal to a desired threshold $(1-\epsilon)$.

In this paper, we consider decode-and-forward (DF) relay beamforming with imperfect channel state information (CSI), cooperative artificial noise (AN) injection, and finite-alphabet input in the presence of an user and $J$ non-colluding eavesdroppers. The communication between the source and the user is aided by a multiple-input-multiple-output (MIMO) DF relay. We use the fact that a wiretap code consists of two parts: i) common message (non-secret), and ii) secret message. The source transmits two independent messages: i) common message (non-secret), and ii) secret message. The common message is transmitted at a fixed rate $R_{0}$, and it is intended for the user. The secret message is also intended for the user but it should be kept secret from the $J$ eavesdroppers. The source and the MIMO DF relay operate under individual power constraints. In order to improve the secrecy rate, the MIMO relay also injects artificial noise. The CSI on all the links are assumed to be imperfect and CSI errors are assumed to be norm bounded. In order to maximize the worst case secrecy rate, we maximize the worst case link information rate to the user subject to: i) the individual power constraints on the source and the MIMO relay, and ii) the best case link information rates to $J$ eavesdroppers be less than or equal to $R_{0}$ in order to support a fixed common message rate $R_{0}$. Numerical results showing the effect of perfect/imperfect CSI, presence/absence of AN with finite-alphabet input on the secrecy rate are presented.

In this paper, we consider the achievable sum secrecy rate in MISO (multiple-input-single-output) {\em full-duplex} wiretap channel in the presence of a passive eavesdropper and imperfect channel state information (CSI). We assume that the users participating in full-duplex communication have multiple transmit antennas, and that the users and the eavesdropper have single receive antenna each. The users have individual transmit power constraints. They also transmit jamming signals to improve the secrecy rates. We obtain the achievable perfect secrecy rate region by maximizing the worst case sum secrecy rate. We also obtain the corresponding transmit covariance matrices associated with the message signals and the jamming signals. Numerical results that show the impact of imperfect CSI on the achievable secrecy rate region are presented., Comment: arXiv admin note: substantial text overlap with arXiv:1311.3918

In this paper, we study beamforming in decode-and-forward (DF) relaying using multiple relays, where the source node sends a secret message as well as a non-secret message to the destination node in the presence of multiple non-colluding eavesdroppers. The non-secret message is transmitted at a fixed rate $R_{0}$ and requires no protection from the eavesdroppers, whereas the secret message needs to be protected from the eavesdroppers. The source and relays operate under a total power constraint. We find the optimum source powers and weights of the relays for both secret and non-secret messages which maximize the worst case secrecy rate for the secret message as well as meet the information rate constraint $R_{0}$ for the non-secret message. We solve this problem for the cases when ($i$) perfect channel state information (CSI) of all links is known, and ($ii$) only the statistical CSI of the eavesdroppers links and perfect CSI of other links are known.

In this paper, we consider transmitter optimization in multiple-input single-output (MISO) broadcast channel with common and secret messages. The secret message is intended for $K$ users and it is transmitted with perfect secrecy with respect to $J$ eavesdroppers which are also assumed to be legitimate users in the network. The common message is transmitted at a fixed rate $R_{0}$ and it is intended for all $K$ users and $J$ eavesdroppers. The source operates under a total power constraint. It also injects artificial noise to improve the secrecy rate. We obtain the optimum covariance matrices associated with the common message, secret message, and artificial noise, which maximize the achievable secrecy rate and simultaneously meet the fixed rate $R_{0}$ for the common message.

In this paper, we consider the problem of power allocation in MIMO wiretap channel for secrecy in the presence of multiple eavesdroppers. Perfect knowledge of the destination channel state information (CSI) and only the statistical knowledge of the eavesdroppers CSI are assumed. We first consider the MIMO wiretap channel with Gaussian input. Using Jensen's inequality, we transform the secrecy rate max-min optimization problem to a single maximization problem. We use generalized singular value decomposition and transform the problem to a concave maximization problem which maximizes the sum secrecy rate of scalar wiretap channels subject to linear constraints on the transmit covariance matrix. We then consider the MIMO wiretap channel with finite-alphabet input. We show that the transmit covariance matrix obtained for the case of Gaussian input, when used in the MIMO wiretap channel with finite-alphabet input, can lead to zero secrecy rate at high transmit powers. We then propose a power allocation scheme with an additional power constraint which alleviates this secrecy rate loss problem, and gives non-zero secrecy rates at high transmit powers.

In this paper, we consider the achievable sum secrecy rate in full-duplex wiretap channel in the presence of an eavesdropper and imperfect channel state information (CSI). We assume that the users participating in full-duplex communication and the eavesdropper have single antenna each. The users have individual transmit power constraints. They also transmit jamming signals to improve the secrecy rates. We obtain the achievable perfect secrecy rate region by maximizing the sum secrecy rate. We also obtain the corresponding optimum powers of the message signals and the jamming signals. Numerical results that show the impact of imperfect CSI on the achievable secrecy rate region are presented.