Quantum computing and DevOps

---- Rethinking DevOps for the Quantum Era

Quantum computing 101

What is Quantum computing

Quantum computing is a new approach that uses quantum physics principles. It employs qubits, which can exist in both 0 and 1 states at the same time—a property called superposition. This, along with entanglement (where qubits become linked), allows quantum computers to explore many possibilities simultaneously. While still in early stages, quantum computing holds promise for solving complex problems much faster than traditional computers.

The Origins and development of Quantum computing

Quantum computing traces its origins to quantum mechanics, developed in the early 20th century by scientists like Planck, Einstein, Bohr, and Schrödinger. In 1981, Richard Feynman proposed that quantum systems could outperform classical computers. Later, in 1994, Peter Shor introduced an algorithm for factoring large numbers exponentially faster, highlighting quantum computing’s cryptographic potential.

During the 2000s, IBM executed Shor’s algorithm on a 7-qubit quantum computer, and Google later claimed quantum supremacy in 2019. Recent advances in superconducting, trapped ion, and photonic qubits have driven progress, with IBM, Google, and IonQ leading the field. Cloud-based quantum computing is now accessible, accelerating research and applications in cryptography, optimization, and AI 

What problems Quantum Computing resolves

Quantum computing has the potential to revolutionize several industries by addressing complex problems that classical computers struggle with.

In cryptography, quantum computers can break traditional encryption methods like RSA using Shor’s Algorithm, pushing the need for quantum-resistant cryptography such as lattice-based encryption. Additionally, Quantum Key Distribution (QKD) offers unbreakable encryption, ensuring secure communication.

For optimization, quantum algorithms can explore multiple solutions simultaneously, significantly improving tasks like route planning and logistics (e.g., delivery and air traffic control), financial portfolio optimization (e.g., risk analysis and asset allocation), and manufacturing and scheduling (e.g., reducing waste and improving efficiency).

Quantum computers also excel in simulation and drug discovery, where they can naturally simulate molecules and quantum systems. This capability accelerates drug discovery for diseases like Alzheimer’s and cancer, as well as facilitates the development of new materials, such as high-performance batteries and superconductors.

In the realm of machine learning and AI, quantum computing enhances pattern recognition, data classification, and AI-driven solutions, making tasks like fraud detection and medical diagnostics faster and more efficient.

Moreover, quantum computing is instrumental in climate and weather modeling, improving hurricane predictions, disaster forecasting, and resource management by processing vast amounts of data more efficiently.

Finally, in national security and defense, quantum technologies provide advancements such as quantum-safe encryption, quantum radar, and enhanced cybersecurity measures, ensuring more robust protection against emerging threats.

While quantum computing is still in its early stages, its potential to transform industries and solve previously intractable problems is immense.

AI vs. Quantum Computing: Strengths in Different Tasks

Artificial Intelligence (AI) and Quantum Computing are two cutting-edge technologies, each excelling in different domains. AI is highly effective at pattern recognition, automation, and decision-making based on large datasets, making it ideal for applications like image recognition, language processing, and predictive analytics. On the other hand, Quantum Computing thrives in solving complex optimization problems, cryptography, and simulating quantum systems, which are infeasible for classical computers.

The table below highlights the key strengths of AI and Quantum Computing, showcasing the types of tasks each technology is best suited for.

From this table, we can see that:

• AI is powerful for automation, data analysis, and pattern recognition but requires large datasets and computational power.
• Quantum computing excels in optimization, cryptography, and complex simulations but is still in early stages with limited real-world applications.

Summary: 
AI is widely accessible and scalable, while quantum computing is highly specialized and not yet practical for most industries.

IBM Quantum computing: Leading the Future of Computation

IBM is a leader in quantum computing, pioneering research and development to bring quantum technology to practical applications. The IBM Quantum Platform provides cloud-based access to real quantum computers, enabling researchers, developers, and businesses to experiment with quantum algorithms without requiring specialized hardware. This platform is designed to accelerate the adoption of quantum computing by making it accessible to a broad audience, from academia to enterprise users.

Qiskit

A key component of IBM’s quantum efforts is Qiskit, an open-source quantum computing framework that allows developers to design, simulate, and execute quantum programs. By providing tools for quantum circuit design and algorithm development, Qiskit helps bridge the gap between classical and quantum computing. IBM has also developed increasingly powerful quantum processors, such as IBM Eagle, Osprey, and Condor, aiming to push the boundaries of computational power.

Channels

IBM provides multiple channels to access its quantum computing services, ensuring flexibility for different users and applications. The two primary channels are:

• IBM Quantum Platform – This is IBM’s dedicated cloud-based service that provides direct access to quantum processing units (QPUs). It allows users to run quantum experiments, develop quantum algorithms, and explore quantum computing research through tools like Qiskit and the IBM Quantum Lab.
• IBM Cloud® Channel – For businesses and developers already working within IBM Cloud, quantum computing services are integrated into the IBM Cloud environment. This enables organizations to leverage IBM’s quantum resources alongside classical computing services, streamlining hybrid quantum-classical workflows.

These channels allow researchers, developers, and enterprises to experiment with real quantum hardware, develop quantum applications, and integrate quantum computing into their existing workflows. Whether through the dedicated IBM Quantum Platform or the broader IBM Cloud infrastructure, IBM ensures that users have multiple pathways to explore and innovate with quantum technology.

DevOps & Quantum: Two Perspectives

DevOps & Quantum: A Two-Way Boost

As quantum computing advances, it presents new opportunities for DevOps to evolve. Quantum algorithms can optimize complex workflows, enhance security, and accelerate software testing. At the same time, DevOps practices help streamline quantum software development, making quantum technology more accessible and scalable.

• How Quantum computing Enhances DevOps
• How to  Accelerates Quantum computing with DevOps

Quantum computing enhances DevOps

Quantum computing represents a transformative advancement in technology that has the potential to significantly impact the DevOps landscape. By harnessing the power of quantum mechanics, organizations can streamline and optimize various aspects of the software development lifecycle. This innovative approach enables more efficient processes, improved performance, and enhanced decision-making capabilities, paving the way for higher-quality software and faster delivery. Here are five key ways quantum computing enhances DevOps:

• Optimizing Automated Testing
• Efficient Resource Scheduling and Allocation
• Automating Code Generation and Optimization
• Advanced Code Analysis and Bug Detection
• Identifying Performance Bottlenecks

Optimization of Automated Testing

Quantum computing can optimize software testing by automatically generating the most efficient test cases and identifying the most comprehensive test combinations. By rapidly searching through large solution spaces, quantum algorithms help select the most effective testing strategies, improving overall testing efficiency.

Tag: Optimization, Machine learning

Challenges:

• To fully utilize the benefits of quantum computing, a significant volume of testing activities is required for initial machine learning, or the client must have a highly complex system to test.

Approach: If a single client lacks sufficient testing activities, machine learning can be accelerated by aggregating testing data from multiple clients. This could lead to the development of industry-wide shared machine learning models, providing collective insights and benefits to all users.

• AI and Quantum has overlap on this same topic.

Approach: Quantum computing enhances machine learning by leveraging qubits, superposition, and entanglement to process data in ways that classical computers cannot. AI can address a wide range of applications and is accessible to most clients, making it suitable for general use. In contrast, Quantum computing, typically benefits a more specialized user base with specific needs that classical computing can't efficiently meet.

Resource Scheduling and Allocation

In large-scale software development, resource scheduling—such as assigning computing resources and developer tasks—is often an optimization challenge. Quantum computing can evaluate multiple solutions simultaneously to find the optimal or near-optimal allocation, enhancing project development efficiency.
Tag: Optimization

Challenges:

• There is an overlap with AI. Unlike AI, quantum computing requires significant computing power and hardware to reach its goals. Clients must provide substantial computing resources, making Quantum computing most suitable for large organizations with complex development tasks.

Approach and discussion:

If a client has a considerable number of development activities, they can best leverage the advantages of Quantum computing. However, for smaller teams, AI tends to offer more benefits.

Performance Bottleneck Analysis

With its robust parallel processing capabilities, quantum computing can assist developers in swiftly identifying performance bottlenecks in extensive codebases, optimizing execution paths, and enhancing overall software performance.
Tag: Optimization

Approach and discussion:

This capability is well-suited for quantum computing. Traditional methods of bottleneck analysis can become inefficient for highly complex circumstance due to the vast number of dependencies and execution paths. Quantum computing excels in this scenario by leveraging superposition and entanglement to analyze multiple execution pathways simultaneously, significantly reducing the time required to identify performance issues.

Automated Code Generation and Optimization

Quantum machine learning can accelerate code generation algorithms by learning from vast amounts of code, intelligently generating code structures, recommending code snippets, and even performing code optimization.

Tag: Machine learning

Approach and Discussion: AI is well-suited for most code generation and optimization tasks, handling standard patterns efficiently. However, when the logic and dependencies become highly complex, traditional AI approaches may struggle with optimization. Quantum computing offers an advantage in such cases by exploring multiple possibilities simultaneously, uncovering optimal solutions faster 

Code Analysis and Bug Fixing

Quantum computing can assist machine learning algorithms in code quality analysis by accelerating static code analysis and error detection. This reduces the workload on developers by identifying potential issues more efficiently.
Tag: Machine learning, Optimization

Approach and Discussion:
Traditional static code analysis tools, powered by AI, efficiently detect errors and code quality issues in most cases. However, for extremely large and complex codebases, AI alone may struggle with deep dependency analysis and intricate logic structures. Quantum computing enhances this process by rapidly evaluating multiple code paths simultaneously, allowing AI models to analyze patterns more effectively. This synergy between AI and quantum computing improves speed and accuracy in identifying bugs and vulnerabilities, making it particularly beneficial for large-scale software project

Accelerates Quantum computing with DevOps

In the rapidly evolving field of quantum computing, seamless deployment and iteration are crucial for maximizing its potential. Traditional development workflows can be slow and complex, making it challenging to integrate quantum solutions into real-world applications efficiently. By leveraging CI/CD automation, organizations can streamline the development, testing, and deployment of quantum algorithms and applications. This approach ensures faster iterations, higher code quality, and more reliable quantum solutions, ultimately bridging the gap between research and practical implementation.

In this section, we will discuss how to leverage the CI/CD tool in Quantum development, using IBM DevOps Automation platform as example.

Accelerating Quantum Computing Deployment with CI/CD

Traditional software delivery follows well-established CI/CD practices, where code is continuously integrated, tested, and deployed across standard computing environments. The infrastructure, compilers, and execution models are stable and widely supported, allowing for predictable automation.

In contrast, quantum computing delivery introduces several unique challenges:

• Hybrid Development Environments – Quantum applications often involve a mix of classical and quantum components, requiring seamless integration between conventional CI/CD pipelines and quantum-specific workflows.
• Probabilistic Outputs – Unlike deterministic classical programs, quantum algorithms often produce probabilistic results, requiring specialized validation and error mitigation strategies in CI/CD pipelines.
• Longer Compilation and Execution Times – Quantum programs must be compiled into quantum circuits before execution, and depending on the complexity, execution times may vary due to resource constraints on quantum hardware.

Value of IBM’s CI/CD Tools for Quantum Computing

To address the unique challenges in quantum computing, IBM’s DevOps automation is specifically designed to manage the complexities of developing quantum applications. This automation facilitates hybrid workflows that seamlessly integrate classical and quantum computing processes, enabling developers to work with both environments effectively.

IBM’s tools also leverage IBM Quantum cloud services, providing access to a range of quantum hardware and simulators. This integration allows for efficient resource management and job scheduling tailored for quantum tasks, ensuring that jobs are executed on the most suitable quantum devices based on their specific requirements.

Furthermore, IBM’s DevOps automation incorporates robust validation mechanisms that are essential for handling the probabilistic nature of quantum results. These mechanisms ensure that the outputs of quantum algorithms are accurately assessed, allowing developers to implement necessary adjustments and optimizations as needed. This capability is crucial in a landscape where results can vary due to inherent quantum uncertainties.

Overall, IBM’s CI/CD tools enhance the development lifecycle of quantum applications by providing the necessary frameworks for continuous integration, continuous testing, and continuous deployment, ultimately driving innovation and efficiency in quantum computing.

Testing and test automation in Quantum computing

Testing in quantum computing involves verifying the correctness, reliability, and performance of both quantum programs and quantum hardware. Unlike classical computing, quantum computers operate on qubits and leverage superposition and entanglement, making their testing fundamentally different from traditional software testing.

• Measurement Limitations – Quantum states collapse upon measurement, requiring multiple executions and statistical analysis to verify results. The No-Cloning Theorem makes debugging and regression testing difficult.
• Noise and Errors – Quantum hardware suffers from high error rates and noise, making it hard to distinguish software bugs from hardware issues.
• Lack of Standards – There is no unified testing framework or benchmarks, making performance comparison and verification challenging.
• Algorithm Complexity – Many quantum algorithms are probabilistic, and simulating quantum programs on classical hardware becomes infeasible as qubit numbers grow.
• Hybrid Testing Challenges – Quantum programs often interact with classical systems, requiring specialized hybrid testing approaches.
• Limited Test Automation – Existing automation tools are not well-suited for quantum computing, leading to manual-intensive testing processes.

Value of IBM’s Test Automation Tools for Quantum Computing

IBM’s test automation tools enhance the reliability and efficiency of quantum computing by addressing key challenges and streamlining the validation process.

1. Reliable Testing Despite Quantum Uncertainty
   • IBM’s tools use statistical validation to handle probabilistic outputs, ensuring accurate assessment of quantum algorithms.
2. Error Mitigation and Noise Handling
   
   • Built-in error correction and noise-aware testing help distinguish software issues from hardware noise, improving test reliability.
3. Standardized Testing Frameworks
   
   • IBM provides well-defined testing frameworks, enabling developers to validate quantum applications consistently across different hardware and simulators.
4. Hybrid Classical-Quantum Testing
   
   • Seamless integration with classical computing environments ensures smooth end-to-end testing of hybrid applications.
5. Scalability and Automation
   
   • Automated test execution and cloud-based quantum simulation allow developers to scale testing efficiently without manual intervention.
6. API-Driven Test Automation
   
   • IBM’s APIs enable automated testing of quantum circuits, providing access to real quantum hardware and simulators for validation.
   • APIs integrate with CI/CD pipelines, allowing developers to incorporate quantum tests into DevOps workflows.
   • Real-time execution logs, debugging insights, and error analysis help improve test efficiency and reduce troubleshooting time.
7. Service Virtualization for Quantum Workflows
   
   • IBM DevOps Automation's service virtualization allows developers to simulate quantum hardware responses, enabling early-stage testing without requiring real quantum machines.
   • This helps in testing error handling, performance, and integration with classical systems before deployment on actual quantum hardware.
   • Virtualized services provide consistent test environments, reducing dependency on physical quantum machines and improving test repeatability.

IBM’s test automation, especially the API-based test automation and service virtualization ensure scalable, repeatable, and efficient testing for quantum programs, making quantum development more accessible and reliable

Coding for quantum computing development

Quantum computing development requires a shift from classical programming, focusing on qubits, superposition, and entanglement. Qiskit, an open-source quantum computing framework from IBM, provides tools to design, simulate, and execute quantum algorithms on real quantum hardware. It enables developers to build quantum circuits, apply quantum gates, and perform measurements efficiently.

IBM’s Qiskit Code Assistant enhances this process by offering AI-powered code suggestions, debugging insights, and optimization tips within the Qiskit environment. It helps developers write more efficient quantum code, catch errors early, and refine their algorithms for better execution on quantum hardware.

Modelling for Quantum computing development

Modeling in quantum computing development involves designing and simulating quantum systems to understand their behavior before execution on real quantum hardware. Unlike classical models, quantum models leverage qubits, superposition, entanglement, and quantum gates to represent and process information.

Developers use frameworks like Qiskit, Cirq, and PennyLane to build quantum circuit models, optimize algorithms, and test their feasibility through quantum simulators. Effective modeling helps in refining quantum applications, improving algorithm efficiency, and mitigating hardware constraints such as noise and decoherence.

IBM’s DevOps Model Architect enhances this process by providing visual modeling, automation, and integration for quantum workflows. It enables teams to design quantum-classical hybrid systems and map dependencies across various components. By streamlining the transition from prototype to execution, Model Architect allows developers to visualize the entire workflow, making it easier to identify potential issues and ensure all elements are properly aligned. Additionally, it supports collaboration among team members by facilitating shared understanding and communication through visual representations of complex quantum architectures.

Exploring Potential Improvements for Quantum Support

In considering how IBM’s DevOps Model Architect could better support quantum computing development, several possibilities arise:

• Quantum-Specific Modeling Features – The introduction of pre-built templates for common quantum algorithms and error mitigation strategies might simplify the modeling process for developers, allowing them to focus on higher-level design rather than low-level implementation details.
• Enhanced Hybrid Workflow Integration – Strengthening the connection between quantum and classical DevOps pipelines could facilitate smoother transitions between the two environments, improving overall workflow efficiency and reducing friction in development processes.
• Improved Visualizations for Quantum Circuits – Enhancing the visualization capabilities within Model Architect could help developers better understand the complex interactions and dependencies in quantum circuits, leading to more effective debugging and optimization.
• User-Friendly Interface Enhancements – Improving the user interface to support intuitive drag-and-drop modeling features could lower the entry barrier for developers new to quantum computing, making it easier for them to visualize and manipulate quantum circuits.

Exploring these potential improvements could contribute to making IBM’s DevOps Model Architect an even more effective tool for quantum computing development, ultimately benefiting developers in their quest to build and deploy quantum applications.   

Demonstration of Quantum computing 

Overview

This demonstration will utilize IBM Qiskit, IBM DevOps Automation - DevOps Deploy, and the IBM Quantum Platform to showcase how Qiskit operates within the Quantum Platform and how DevOps Deploy enhances the development and deployment processes in quantum computing.

Introducing IBM DevOps Deploy

IBM DevOps Deploy is a key component of IBM's DevOps Automation suite, designed to streamline the deployment of applications across various environments. It automates the deployment process, ensuring consistent and reliable releases while reducing the risk of human error. This tool facilitates continuous integration and continuous deployment (CI/CD) practices, enabling teams to deliver high-quality software quickly and efficiently.

Relationship with DevOps Automation

IBM DevOps Deploy works in tandem with other DevOps Automation tools to create a comprehensive pipeline for software development. By integrating with IBM's suite of DevOps tools, it provides seamless connectivity between development, testing, and production environments. This integration allows for automated testing, validation, and deployment of quantum applications built using Qiskit. As a result, developers can focus on writing and optimizing quantum algorithms while relying on DevOps Deploy to manage the complexities of deployment and scaling on the IBM Quantum Platform.

Demo topology

This demonstration will highlight how the combination of Qiskit and IBM DevOps Deploy can enhance the development lifecycle for quantum computing applications, making it easier for teams to leverage the power of quantum technology.

We have 3 servers:

Quantum Server – Executes computations on quantum hardware.

Qiskit Linux Server – Runs the Qiskit toolkit and handles non-quantum logic, pre-processing, circuit mapping, hardware optimization, and post-processing.

IBM DevOps Deploy Server – Manages automation, release processes, environment segregation, governance, and test integration when needed.

Scenario of demo quantum computing

Quantum Gate 101

Hadamard Gate (H)

The Hadamard gate (H gate) is a fundamental quantum gate that creates superposition. When applied to a qubit, it transforms the basis states as follows:

This means a qubit initially in |0⟩ or |1⟩ will end up in an equal superposition of both states. The Hadamard gate is widely used in quantum algorithms, including Grover’s search and quantum Fourier transform, as it enables quantum parallelism.

Controlled-X (CNOT) gate

The Controlled-X (CNOT) gate is a two-qubit quantum gate that flips (applies an X or NOT operation to) the target qubit only if the control qubit is in state |1⟩. It is defined as follows:

If the control qubit is |0⟩, the target qubit remains unchanged.
If the control qubit is |1⟩, the target qubit flips: |0⟩ → |1⟩ and |1⟩ → |0⟩.

The CNOT gate is essential for quantum entanglement, playing a key role in quantum algorithms and error correction. It is often used to create Bell states, which are fundamental in quantum communication and computation

Demonstration scenario

This demo simple quantum circuit with two qubits (q0 and q1) and two quantum gates, Hadamard Gate (H) and Controlled-X (CNOT) gate.

Transform the logic into primitives quantum hardware understands 

Explanation of primitives logic steps:

Demo Video