Payroll and compensation backends represent some of the most legally sensitive and financially consequential components of enterprise software systems. Traditional implementations often rely on mutable database records that overwrite prior state, complicating auditability, replay safety, and regulatory compliance. In cloud-native, distributed environments, mutable state models further amplify risks related to concurrency, partial failures, and inconsistent recovery. This paper proposes an immutable ledger-based modeling approach for payroll and compensation backends deployed in cloud-native architectures. By treating every compensation-relevant change as an append-only, versioned ledger entry, the system achieves deterministic state reconstruction, strong audit traceability, and resilience under distributed execution. The study examines canonical ledger design, event-sourced architectures, retroactive correction handling, concurrency isolation, and cross-entity coordination within compensation workflows. It also analyzes partitioning strategies, operational resilience, and anti-patterns associated with mutable payroll systems. The resulting framework demonstrates how immutable modeling principlesâwhen combined with identity-scoped partitioning and cloud-native scalability patternsâenable high-integrity financial backend systems that remain deterministic, replay-safe, and regulatorily compliant under high concurrency and infrastructure variability.
Haowen ZHANG, Xiaoqiang Zhu, Tao Zhang, Nan Wang ¡ 6 authors
The integration of blockchain technology with the Industrial Internet of Things (IIoT) has engendered transformative prospects for the exchange and dissemination of data within the industrial domain. Nonetheless, conventional blockchain frameworks encounter substantial impediments related to scalability, security, and efficiency in the context of IIoT environments. This scholarly inquiry introduces a novel credit-based distributed ledger paradigm, termed CreChain, to mitigate these challenges. The proposed model facilitates elevated throughput and reduced latency by introducing credit accounts and refining the transaction verification mechanism. Additionally, it ensures the integrity of transactions and the preservation of user privacy through the employment of Mtree and Credit proofs. Moreover, the refinement of credit selection, attestation, and interchange protocols augment the system's performance and scalability. Empirical evaluations demonstrate that CreChain is capable of achieving a throughput of 20000 TPS (transactions per second) and a transaction confirmation latency at sub-second thresholds, surpassing prevalent blockchain scaling solutions in terms of consensus efficacy and storage expenses. This innovation effectively harnesses the potential of blockchain for secure and efficient data exchange, rendering it a promising candidate for IIoT applications.
Zeta Avarikioti, Eleftherios Kokoris Kogias, Ray Neiheiser, Christos Stefo
The security of many Proof-of-Stake (PoS) payment systems relies on quorum-based State Machine Replication (SMR) protocols. While classical analyses assume purely Byzantine faults, real-world systems must tolerate both arbitrary failures and strategic, profit-driven validators. We therefore study quorum-based SMR under a hybrid model with honest, Byzantine, and rational participants. We first establish the fundamental limitations of traditional consensus mechanisms, proving two impossibility results: (1) in partially synchronous networks, no quorum-based protocol can achieve SMR when rational and Byzantine validators collectively exceed $1/3$ of the participants; and (2) even under synchronous network assumptions, SMR remains unattainable if this coalition comprises more than $2/3$ of the validator set. Assuming a synchrony bound $Î$, we show how to extend any quorum-based SMR protocol to tolerate up to $1/3$ Byzantine and $1/3$ rational validators by modifying only its finalization rule. Our approach enforces a necessary bound on the total transaction volume finalized within any time window $Î$ and introduces the \emph{strongest chain rule}, which enables efficient finalization of transactions when a supermajority of honest participants provably supports execution. Empirical analysis of Ethereum and Cosmos demonstrates validator participation exceeding the required $5/6$ threshold in over $99%$ of blocks, supporting the practicality of our design. Finally, we present a recovery mechanism that restores safety and liveness after consistency violations, even with up to $5/9$ Byzantine stake and $1/9$ rational stake, guaranteeing full reimbursement of provable client losses.
Mallellu Sai Prashanth, Seetha Srujana, V. Maheswari, Rajanikanth Aluvalu ¡ 6 authors
Smart healthcare technologies are of paramount importance due to their potential to revolutionize healthcare delivery and improve patient outcomes. They enable the collection and analysis of real-time health data, allowing for early detection and prevention of diseases. Smart technologies facilitate remote patient monitoring, enhancing access to healthcare in underserved areas. This chapter presents an advanced and efficient Solidity-backed blockchain solution for smart healthcare technologies. The proposed system leverages the power of blockchain technology, specifically utilizing the Solidity programming language, to address critical challenges in healthcare data management, security, and transactional efficiency. The research introduces two key components: the HealthCoin token contract and the HealthRecord contract. The HealthCoin contract implements a token economy for healthcare transactions, allowing secure and transparent exchange of value within the healthcare ecosystem. It incorporates innovative features such as token transfers, approvals, and allowances, ensuring seamless interoperability between participants. The HealthRecord contract addresses the critical need for secure and accessible storage of healthcare records on the blockchain. It introduces a novel approach to record management, utilizing a timestamped structure coupled with a private mapping, allowing individuals to store and retrieve their health records efficiently. The novelty of the proposed solution lies in several key aspects. Firstly, the implementation demonstrates the effective utilization of Solidity, a widely adopted programming language for Ethereum smart contracts, to develop robust and scalable blockchain ventures in the healthcare domain. Secondly, the HealthCoin token contract offers a unique value proposition by providing a dedicated token economy designed specifically for healthcare transactions.
Distributed ledgers are common in the industry. Some of them can use blockchains as their underlying infrastructure. A blockchain requires participants to agree on its contents. This can be achieved via a consensus protocol. How do these protocols differ in performance, and how are the differences affected by the communication network? Moreover, such a protocol would need a timer to ensure progress, but how should the timer be set? This article presents an analytical model to address these and related issues when there are crash faults. Specifically, it focuses on two consensus protocols (Istanbul BFT and HotStuff) and two network topologies (Folded-Clos and Dragonfly). The model provides formulas that express the consensus time in terms of protocol and topology parameters. No other model in the literature provides such a global view of the parameter space. Analysis of the closed-form expressions yield new insights into how the timers should be set, how faults affect the consensus time, when one protocol is faster than the other, and how the two topologies differ in their impact. The formulas and analyses are validated with simulations. The conclusion also offers some tips for the analytical modeling of similar protocols.
We present SmartShards: a new sharding algorithm for improving Byzantine tolerance and churn resistance in blockchains. Our algorithm places a peer in multiple shards to create an overlap. This simplifies cross-shard communication and shard membership management. We describe SmartShards, prove it correct and evaluate its performance. We propose several SmartShards extensions: defense against a slowly adaptive adversary, combining transactions into blocks, fortification against the join/leave attack.
In the realm of smart contract security, smart contracts can be instrumented with simple safety properties to enhance their security. However, these instrumentable safety properties are limited and fail to block certain types of hacks. This limitation primarily stems from the Ethereum Virtual Machineâs (EVM) inability to allow a smart contract to access the complete transaction trace in real-time. On the other hand, transaction malice detection methods can leverage properties of complete transaction traces to identify hacks with high accuracy. To bridge this gap, we propose modifications to both the EVM and Ethereum clients, enabling smart contracts to access the complete transaction trace and define and validate complex transaction trace properties in real-time without affecting traditional EVM execution. We use past-time linear temporal logic (PLTL) to express transaction trace properties, showcasing that most existing detection metrics can be expressed using PLTL.
This article explores blockchain technology's transformative role in ensuring data integrity and security across modern enterprise systems. The article examines the fundamental architecture of blockchain security, emphasizing distributed ledger technology, consensus mechanisms, and cryptographic foundations that collectively create an immutable and transparent system. The article investigation delves into core security features, including the implementation of advanced cryptographic techniques, decentralization strategies, and innovative security protocols that protect against various cyber threats. Through detailed analysis of industry applications, the article demonstrates blockchain's impact across financial services, supply chain management, healthcare, and IoT sectors, highlighting significant improvements in operational efficiency, security, and cost reduction. The article further evaluates implementation benefits, encompassing operational advantages, economic impacts, and technical improvements that organizations experience through blockchain adoption. Finally, the article addresses future implications and challenges, including technology integration hurdles, regulatory considerations, and scalability solutions, providing insights into the evolving landscape of enterprise blockchain implementation.
Distributed ledger technology (DLT) enable the immutable, transparent, and tamper-proof storage of transaction records in a shared electronic register. Due to these intrinsic properties, DLT has the potential to be highly disruptive to businesses. However, the heterogeneity and intricacy of the underlying technologies impede their more widespread adoption. A primary challenge lies in comprehending the interrelationships between the organizational, economical, and technical dimensions of DLT systems. An integrated perspective on the fundamental concepts of DLT within each dimension can be accomplished through the implementation of domain-specific conceptual modeling languages that explicitly account for intrinsic properties. Conceptual models adhere to well-defined elements and rules for the utilization of these elements. This enables the algorithmic processing of models to generate derivations and new insights.The focus of prior research on modeling support for DLT has been on the technical dimension. However, research on conceptual modeling approaches enabling the joint consideration of organizational, economical, and technical factors is lacking. This situation is undesirable both from a scientific standpoint and from a practical one. To address this gap, this thesis advances the current state-of-the-art in the design of DLT applications by introducing several domain-specific approaches addressing concerns across dimensions. This includes support for the decision-making in organizations, for the design of their business model, its alignment with the underlying DLT systems, and for the development of smart contracts and subsequent generation of functional code. The artifacts are consolidated into a domain-specific conceptual modeling method, facilitating the design of DLT applications and business cases.
Order fairness in distributed ledgers refers to properties that relate the order in which transactions are sent or received to the order in which they are eventually finalized, i.e., totally ordered. The study of such properties is relatively new and has been especially stimulated by the rise of Maximal Extractable Value (MEV) attacks in blockchain environments. Indeed, in many classical blockchain protocols, leaders are responsible for selecting the transactions to be included in blocks, which creates a clear vulnerability and opportunity for transaction order manipulation. Unlike blockchains, DAG-based ledgers allow participants in the network to independently propose blocks, which are then arranged as vertices of a directed acyclic graph. Interestingly, leaders in DAG-based ledgers are elected only after the fact, once transactions are already part of the graph, to determine their total order. In other words, transactions are not chosen by single leaders; instead, they are collectively validated by the nodes, and leaders are only elected to establish an ordering. This approach intuitively reduces the risk of transaction manipulation and enhances fairness. In this paper, we aim to quantify the capability of DAG-based ledgers to achieve order fairness. To this end, we define new variants of order fairness adapted to DAG-based ledgers and evaluate the impact of an adversary capable of compromising a limited number of nodes (below the one-third threshold) to reorder transactions. We analyze how often our order fairness properties are violated under different network conditions and parameterizations of the DAG algorithm, depending on the adversary's power. Our study shows that DAG-based ledgers are still vulnerable to reordering attacks, as an adversary can coordinate a minority of Byzantine nodes to manipulate the DAG's structure.
Abstract Applying Distributed Ledger Technologies to securely manage intercommunicated data between IoT applications has recently been adopted on an enormous scale. They enable data integrity, privacy, and robustness to public, open, permission-less P2P networks. Voting-based consensus algorithms proved high efficiency even with limited computing and less power IoT devices. Moreover, they can identify legitimate information and isolate malicious attackers through repetitive voting queries to adjacent peers asking their opinions about the validity of each transaction. Several lightweight validation models are introduced to enrich IoT networks with better performance and higher security. Nevertheless, the current algorithms struggle to find adequate parameters that balance network security and operability, in addition to balancing fairness in distributed environments. This paper introduces an Autonomous Lightweight Ledger Constructor to resolve common defects and threats. Based on Reinforcement Learning, it can dynamically construct a valid distributed ledger in limited-computing systems under several adversarial conditions. The validity of transactions in this approach is calculated based on their cumulative weights and the issuerâs reputation, which are inferred subjectively by a lightweight Bayesian-like function. A new simulator is developed to evaluate ALLC performance and security. The experimental results demonstrate reasonable performance and high resistance against known compromises targeting Distributed Ledger Technologies.
In modern distributed systems, achieving consensus and reconciliation among diverse nodes across varying network conditions is a significant challenge. CohortSync, a novel micro-cohort-based protocol, addresses this challenge by leveraging scalable and fault-tolerant mechanisms to ensure data consistency and system reliability. The core innovation of CohortSync lies in its utilization of dynamically formed micro-cohorts, which are small, manageable groups of nodes that collaborate to achieve consensus without the overhead associated with traditional large-scale consensus protocols. CohortSync operates by first classifying nodes based on their network latency, data relevance, and operational load. This classification enables the protocol to intelligently form micro-cohorts that are geographically and contextually optimized, reducing the latency typically experienced in global consensus operations. Each micro-cohort is responsible for a subset of the reconciliation tasks, allowing for parallel processing and significantly reducing the time to reach consensus. The protocol incorporates a hybrid approach to consensus that combines elements of both deterministic and probabilistic consensus mechanisms. This hybrid model allows CohortSync to maintain high availability and consistency, even in the face of node failures or network partitions. By adapting the consensus mechanism based on real-time network performance and node responsiveness, CohortSync can dynamically adjust its operations to maintain system performance and data accuracy. Another key feature of CohortSync is its reconciliation process, which uses a version-controlled state reconciliation algorithm. This algorithm ensures that all nodes within a micro-cohort maintain a synchronized state, with conflicts resolved through a majority rule among the cohort members. This approach not only minimizes the risk of data divergence but also optimizes the reconciliation process to be both time-efficient and resource-conservative. CohortSync also integrates a continuous learning component that analyzes past consensus rounds to optimize future cohort formation and consensus strategies. This machine learning-driven adaptability makes the protocol robust against evolving network conditions and varying operational loads across nodes. The protocol has been tested in various simulated environments that mimic real-world distributed systems across different industries, including finance, healthcare, and e-commerce. The results demonstrate that CohortSync significantly outperforms existing consensus protocols in terms of scalability, fault tolerance, and operational efficiency. In conclusion, CohortSync presents a transformative approach to consensus and reconciliation in distributed systems. By decentralizing the consensus process into manageable micro-cohorts and integrating adaptive learning mechanisms, CohortSync offers a scalable, efficient, and robust solution that can meet the demands of contemporary distributed computing environments.
Ensuring data integrity is crucial for IoT-based healthcare and emotion care services, which utilize Fog computing to bring resources and services closer to the network edge. This proximity, however, increases the risks of data tampering, loss, and unauthorized access. To mitigate these risks, Distributed Ledger Technology (DLT) platforms such as Hash graph, Big chain-DB, IOTA (Internet of Things Application) and Blockchain are being investigated for their potential to enhance data integrity within Fog computing environments. This study presents a framework designed to ensure data integrity in IoT-based healthcare and emotion care services by leveraging IOTA technology. IOTA, which employs a directed a-cyclic graph (DAG) structure known as the Tangle, provides a secure, decentralised and tamper-resistant method for data storage and sharing. Unlike traditional blockchain, IOTAâs consensus mechanism operates without miners, offering improved scalability and efficiency suitable for IoT environments. Our proposed framework exploits IOTAâs capabilities to deliver a robust solution for maintaining data integrity in Fog computing contexts. The evaluation results demonstrate the frameworkâs feasibility and effectiveness in enhancing data integrity for IoT-based healthcare and emotion care services. Although IOTA significantly improves data integrity by complicating unauthorized data alterations, it is essential to acknowledge that complete immutability is influenced by various factors, such as consensus mechanisms and the number of network participants, similar to the limitations observed in other DLTs. ⢠Integrating Fog Computing with Distributed Ledger Technology (DLT) utilizing IOTA. ⢠Leveraging the âImmutable Data Tangleâ structure to safeguard data against unauthorized modifications and tampering. ⢠Fortifying Resilience against Security Threats using DLT (IOTA). ⢠Provides insights into the effectiveness of hybrid cryptanalytic attacks and the role of DLT (IOTA) integration in countering them. ⢠Practical implementations are meticulously presented, accompanied by real-world case studies.
The rapid increase in Internet of Things (IoT) applications has exposed critical security vulnerabilities, particularly concerning user privacy and identity forgery. To address these concerns, Blockchain technology offers a promising solution by providing strong security and ensuring data integrity through its transparent ledger system. By leveraging blockchain, IoT systems can enhance their security protocols, making it more difficult for attackers to exploit vulnerabilities and access sensitive data. We proposed Attribute-Based Access Control (ABAC) integrated with trust-based delegated consensus blockchain (TDCB) technology. The ABAC scheme employs Fully Homomorphic Encryption (FHE) processes to encrypt attributes and access regulations, enabling analytical operations directly on encrypted data. Dueling Double Deep Q-Networks with Prioritized Experience Replay (D3P) with Deep Reinforcement Learning (DRL) collaborate with Multiple blockchain nodes to decode the ABAC systemâs data and optimize the performances of the blockchain. Our proposed scheme ABAC-TDBC-D3P enhances throughput and security and reduces total computing costs. The simulation results demonstrate that the suggested ABAC-TDCB-D3P scheme has a percentage of 86% for Collusive Rumour Attack (CRA) and 91% for Naive Malicious Attack (NMA). Significant improvements in blockchain security, particularly in mitigating the impact of malicious nodes, were compared to previous schemes.
Performance predictability, ensuring low latency variability, is crucial for the reliability and efficiency of blockchain consensus. Byzantine Fault Tolerant Proof-of-Stake (BFT-PoS) consensus aims to achieve stable transaction processing latency by scheduling block generation at consistent intervals. However, BFT-PoS's incentive mechanisms grant all transaction tips to the block proposer, which can be exploited by delaying proposals to gain extra Maximal Extractable Value (MEV) rewards, thus undermining performance predictability. Existing solutions impose penalties for delays but lack a standard for measuring the extra rewards from delays or fail in malicious environments. This paper introduces InTime, a novel approach to safeguard performance predictability in BFT-PoS by economically motivating timely block proposals. We first introduce the untimely MEV ratio , a reliable metric to measure the extra rewards gained from proposal delays, facilitating our countermeasures against deliberate delays. Furthermore, we propose the arrival rate incentive (ARI), aligning rewards with transaction arrival timing among nodes to reduce potential MEV manipulation. To make ARI robust against malicious behaviors, we establish a committee time witness (CTW) workflow to accurately gather and verify transaction arrival times. Extensive experiments demonstrate that InTime can effectively reduce latency variability by up to 95.9%.
The rise of cryptocurrencies and decentralized fi- nance (DeFi) has highlighted the importance of secure and collaborative management systems for digital assets. Multi-party crypto vaults provide a way to ensure distributed control, privacy, and fault tolerance by involving multiple participants in the management and approval of transactions. This paper explores the two primary approachesâMulti-Signature (Multi-Sig) and Multi-Party Computation (MPC)âthat underpin these vaults. Multi-Sig schemes offer fast, scalable solutions for real-time applications, while MPC ensures strong privacy and security by allowing encrypted computations without exposing private keys. In addition key management models, such as split-key and blockchain-based methods, and fault tolerance mechanisms, including social recovery and time-locked protocols, which ensure that vault systems remain secure and operational even in cases of participant failure. This paper recommends MPC as the optimal approach for high-security, privacy-sensitive applications, such as institutional custody and financial systems, while outlining av- enues for future research, including post-quantum cryptography and latency reduction in MPC protocols. Index TermsâMulti-Party Computation (MPC), Multi- Signature (Multi-Sig), Crypto Vaults, Threshold Cryptography, Blockchain Key Management, Decentralized Finance (DeFi),, Threshold ECDSA, Social Recovery Mechanism, Time-Locked Withdrawal Protocol, Fault Tolerance in Cryptography, Split- Key Management, Smart Contracts, Privacy-Preserving Cryp- tography, Institutional Crypto Custody
Lukas Smirek, Jens Griesing, Tobias HĂśpfer, Daniel Stetter
Advances in electric vehicles and charging infrastructure technology have given the electrification of road traffic a positive momentum. Nowadays, it is becoming more and more evident that the related energy and financial processes of the current e-mobility ecosystem are reaching their limits. This leads to usability losses for end users as well as administrative and non-causation-based financial burdens on various energy system participants. In this article, use cases are inferred from the literature, the aforementioned challenges are discussed in more detail, and strategies for addressing them are presented. Furthermore, the information system architecture of the BANULA project, with its core elements of open communication standards, virtual balancing areas, and blockchain components, is explained. BANULA addresses the aforementioned challenges by holistically considering the needs of all participants. A special focus of the project is implementing and investigating the concept of virtual balancing areas. This concept has been available since 2020 but has not been implemented in the market yet. To the best of the authorsâ knowledge, BANULA is the first project that utilizes current legislation to transfer charging infrastructure to virtual balancing areas in conjunction with distributed ledger technology to support related processes. In the first step, the BANULA implementation prototype targets the German e-mobility ecosystem, but applicability to other states in the European Union is planned. Using an independent framework, the BANULA architecture and its prototypical implementation are evaluated. The authors show that the unique combination of virtual balancing areas and the related processes, enhanced through distributed ledger technology, has the potential to contribute to a user-centered, trustworthy, and grid-supportive e-mobility ecosystem.
Ulysse Pavloff, Yackolley Amoussou-Guenou, Sara Tucci-Piergiovanni
Ethereum has undergone a recent change called the Merge , which made Ethereum a Proof-of-Stake blockchain shifting closer to BFT consensus. Ethereum, which wishes to keep the best of the two protocol designs (BFT and Nakomoto-style), now has a convoluted consensus protocol as its core. The result is a blockchain possibly being produced in a tree-like form while participants try to finalize blocks. We categorize different attacks jeopardizing the liveness of the protocol. The Ethereum community has responded by creating patches against some of them. We discovered a new attack on the patched protocol. To support our analysis, we propose a new high-level formalization of the properties of liveness and availability of the Ethereum blockchain, and we provide pseudo-code. We believe this formalization to be useful for other analyses as well. Our results yield that the Ethereum Proof-of-Stake has safety but only probabilistic liveness. The probability of the liveness is influenced by the parameter describing the time frame allowed for validators to change their mind about the current main chain.
This thesis primarily explores efficient zero-knowledge range arguments as first part and privacy-preserving applications within distributed systems as second part. The first part focuses on zero-knowledge range arguments, a fundamental cryptographic primitive that enables a prover to convince a verifier that a secret value lies within a predefined range without disclosing any unnecessary information. However, deploying range arguments in practice faces significant challenges due to high gas costs and computational overhead. This part contributes to optimizing the {\em verification efficiency} of range arguments to reduce deployment costs on blockchains and other decentralized platforms. We introduce four new zero-knowledge range arguments in the discrete logarithm setting that require only $c \sqrt{\tfrac{N}{\log N}}$ group exponentiations in verification, where $N$ is the number of bits to represent a range and $c$ is a small constant. These improvements make the arguments concretely efficient for blockchain deployment with minimal gas costs. The second part proposes two privacy-enhancing secure distributed systems and applications. First, we introduce a novel paradigm for decentralized privacy-preserving group purchasing for energy plans. Leveraging privacy-preserving blockchain technology and secure multi-party computation, this approach enables users to form coalitions for coordinated switch decisions in a decentralized manner without relying on a trusted third party. We develop an effective solution to support decentralized privacy-preserving group purchasing, which includes a competitive online algorithm for decision-making, secure multi-party computation for enhancing privacy, and zero-knowledge proofs on the blockchain for verifying the private input data used in our online algorithm. Second, we propose a novel scheme zk-qrcode based on anonymous credentials and zk-SNARK. Our scheme leverages the following features: Blockchain-based credential issuance, we eliminate the need for credential issuers to hold signing keys by allowing them to issue credentials to a smart contract on the blockchain; Flexible and composable identity statements, enables users to prove complex statements about their credentials without revealing unnecessary information; QR code based verification: enables user interaction with service providers through QR codes displayed or scanned on mobile phones, incorporating identity proof and access control requests. We implement and evaluate our zk-qrcode scheme in practical use cases for entering bar anonymously. Our results demonstrate that the scheme is efficient and practical, with access control proof generation and verification taking less than 650 milliseconds.
The Federated Unified Secure Exchange (FUSE) algorithm represents a breakthrough in computer science and cybersecurity, building upon three foundational elements: Homomorphic Encryption, Zero-Knowledge Proofs, and Federated Learning. By deeply integrating these three pillars, FUSE not only guarantees data integrity and privacy in decentralized environments but also maintains high performance and remarkable scalability. This paper details the architecture, mathematical principles, and real-world applications of FUSE across Internet of Things (IoT), healthcare, finance, and supply chains. In addition, it compares FUSE with several âclassicâ algorithms that have dramatically changed the worldâsuch as QuickSort, RSA, Dijkstra, PageRank, FFT, Backpropagation, SHA, Monte Carlo, Gradient Descent, and Blockchainâhighlighting how FUSE addresses existing limitations in data protection and distributed computation.
Atoms and photons, two things so different but yet so alike. The former, the building block of matter, something we learn about in school and imagine it as some tiny marbles encircled by other tinier marbles. The latter, an electromagnetic wave, a light particle or an excitation of the electromagnetic field. Quantum mechanics tells us about the properties of these two entities. And even if it sounds, looks and writes counter-intuitive, it has proven right for over a century now. In this work, I elaborate on how we tested the laws of quantum mechanics and how we used them learn more about the tiny building blocks of nature and the fields they use to talk to each other. The atoms we use, are artificial. Superconducting qubits, small electrical circuits with quantized energy levels behave like electrons that transition between different orbitals in an atom. One of the qubits' advantages, is also a big disadvantage. We design the circuits' energy levels and fabricate them in a cleanroom. This allows for arbitrary spaced energy levels but in contrast to real atoms, prevents two superconducting qubits from being alike. Still, this qubit platform is one of the frontrunners for future quantum computing technology and testing fundamental physics due to their scalability. We interface superconducting qubits, which operate in the GHz regime, with microwave photons. We use 3D aluminum cavities as mediators between qubits and photons. The cavities allow for non-destructive readout of the qubit state, they shield the qubits from noise at the qubit frequency and they give us an easy way to frequency-tune these joint systems. We need to operate superconducting qubits and their cavities at millikelvin temperatures in dilution refrigerators. At higher temperatures, superconductivity suffers and even worse, the environment is filled with thermal noise photons. This poses a fundamental limitation on the scalability of superconducting qubit devices. Also connecting multiple devices in different fridges does not work over room temperature links because the microwave photons used for this purpose will be covered in noise and the quantum information they carry, will be unusable. Infrared photons do not suffer from this noise problem since there are close to zero thermal noise photons at their frequencies at room temperature. We cannot simply interface superconducting devices with optical photons due their frequency mismatch and the destructive effect of optical photons on superconductors. Therefore, we use microwave-to-optics transducers that allow to convert microwave photons into optical ones and vice-versa. The transducers that we use are macroscopic electro-optic transducers using the Pockels effect in a disk-shaped Lithium Niobate whispering gallery mode resonator. By using a strong optical pump, photons from the two frequency domains experience a beam-splitter interaction and get converted from one to the other. We measure the generated optical photons using elaborate optical setups, optical heterodyning and single photon detectors to gain knowledge about the qubit state or the converted microwave photons. Bridging the microwave and the optical world allows us to take advantage of both of their strengths but it also requires deep knowledge about both of their working principles. In this work, we describe two experiments that our group conducted to showcase the opportunities that arise from interfacing superconducting qubits with optical photons but also the pitfalls, one may encounter on the way. In the first experiment, we managed to all-optically read out a superconducting qubit. We show that the assignment fidelity, the probability that a measurement of the qubit state matches the prepared state, is close to equal for all-optical, microwave-to-optics and conventional microwave readout. We show T1 and T2 measurements for all three readout types and give an analysis of the noise caused by the optics. Finally, we show that the infrared light does not affect the qubit performance in a negative way but that the heating it causes does. This is an important insight that we used in the next experiment. The second experiment is the upconversion of itinerant single microwave photons to the optical domain. We show that we can generate single microwave photons from a qubit-cavity system. We upconvert these single photons, measure them with a single photon detector and reconstruct their shape. By conducting a single photon Rabi measurement, we show correlations between the microwave and the optical domain. And by thorough signal-to-noise measurements and noise analysis, we find that we can generate single infrared photons with high signal-to-noise ratio 5.1 and low transducer added noise (<0.012 quanta). We show that this measurement creates a path towards entanglement of a superconducting qubit and an optical photon and what parameters need to be improved to achieve it. Additionally, this experiment is a proof of principle for an on-demand infrared single photon source. More generally, it allows to link microwave quantum technology in general to the optical domain.