UK

United Kingdom · Technology and data · Mid-Level (2-5 years)

Quantum Software Engineer

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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  • Experience bandMid-Level (2-5 years)
  • Direct reportsNo direct reports
  • Reports toSenior Quantum Software Engineer
  • UK framework levelUsually a coordinator, or early in a professional job

Also advertised as Quantum Programmer · Quantum Developer · Qubit Software Engineer

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

No confident skill match yet for Quantum Software Engineer. The closest we hold is below.

EAL Level 4 Diploma in Engineering and Advanced Manufacturing (Development Competence) Excellence, Achievement & Learning Limited · Level 4 · covers 1 of 1 standard

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Developing low level engineering software EAL Level 4 Diploma in Engineering and Advanced Manufacturing (Development Competence)
Performing Low Level Programming for Engineering Software ETCAL Level 3 NVQ Diploma in Engineering Technical Support

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
Frontier Fluency the next wave, beyond mainstream AI
Systems Fluency seeing the whole picture
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Your Plan Illustration

Built for Quantum Software Engineer

2 units that map to this job, from the qualifications that cover it.

  1. Developing low level engineering software Excellence, Achievement & Learning Limited · covers 1 of 1 standards Level 4
  2. Performing Low Level Programming for Engineering Software ETC Awards Limited · covers 1 of 1 standards Level 3
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Quantum Software Engineer?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skill this role is being asked for

  • Prompt Engineering & LLM Integration for Quantum Research
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Quantum Software Engineer plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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3What this role really is

A Quantum Software Engineer writes code that runs on quantum computers, or the simulators that stand in for them.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Qiskit (IBM Quantum SDK) Intermediate

You'll be writing, simulating, and running quantum circuits on IBM's quantum hardware and simulators. This includes using its transpiler, visualisers, and experiment services.

Cirq (Google Quantum SDK) Intermediate

Implementing quantum algorithms and running them on Google's quantum cloud or simulators. You should be comfortable with its circuit construction and simulation capabilities.

Python (NumPy, SciPy, Matplotlib) Advanced

This is your bread and butter. You'll be using Python for everything from controlling quantum SDKs to classical pre- and post-processing, data analysis, and plotting experimental results. You should be very comfortable with its scientific computing libraries.

Git (GitHub/GitLab) Intermediate

Version control is essential. You'll be using Git for branching, committing your code, creating pull requests, and participating in code reviews on GitHub or GitLab. You should understand standard team workflows.

Docker Basic

You'll use Docker to create and run reproducible research environments. This means you can run existing containers and make minor modifications to Dockerfiles to suit your experimental needs.

Jira & Confluence Basic

You'll use Jira to track your tasks and update progress on projects. Confluence will be where you document your findings, experimental setups, and contribute to the team's knowledge base.

Cloud Quantum Platforms (IBM Quantum Experience, Amazon Braket, Azure Quantum) Basic

You'll be submitting your quantum jobs to these platforms, retrieving results, and monitoring their execution. You should be able to navigate their interfaces and understand basic job management.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
Quantum Circuit Design Implements pre-defined circuits under close supervision. Independently designs and implements circuits for specific algorithmic sub-routines, consulting on overall architecture. Leads the design of novel quantum algorithms and circuit architectures for entire projects.
Tool/SDK Selection Uses specified SDKs and tools as directed. Chooses appropriate SDK features and libraries for tasks; can propose alternative tools for specific problems. Evaluates and recommends new quantum SDKs and platforms for team adoption.
Hardware Job Submission Submits jobs to cloud platforms with pre-configured settings. Configures job parameters (shots, backend selection, optimisation levels) independently, monitoring queue times. Manages resource allocation and prioritisation for multiple concurrent hardware experiments.
Project Timelines & Scope Follows assigned task deadlines; escalates any potential delays immediately. Manages personal task timelines within sprint; flags potential delays or scope creep to manager. Defines and commits to project timelines for workstreams, negotiating scope with stakeholders.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Circuit Fidelity on Hardware
How closely your quantum circuit's output on a real QPU matches the ideal, noise-free simulation.
Target · Achieve >90% fidelity between simulated and hardware outputs for routine experiments.

If your simulator predicts a 75% chance of measuring |0⟩, and the hardware gives you 68%, that's a 90.6% fidelity. We'd expect you to be consistently hitting this kind of target, or explaining why not.

Transpilation Efficiency
How well you can optimise quantum circuits to use fewer gates or shallower depths, which is crucial for noisy hardware.
Target · Reduce circuit depth by at least 10% through clever transpilation techniques for new algorithms.

You take a 50-gate circuit and, using a specific transpiler pass, get it down to 44 gates. That's a 12% reduction, which is a good result and saves precious coherence time on the QPU.

Jira Task Completion Rate
Delivering your assigned quantum software development tasks and research experiments on time.
Target · Complete 90% of assigned Jira tickets within the agreed sprint cycle.

If you're assigned 10 tickets in a sprint (e.g., 'Implement VQE for H2 molecule', 'Debug Qiskit transpilation issue'), you'd ideally close 9 or 10 of them. We understand things can get tricky with quantum, but consistent delivery matters.

Code Review Feedback Incorporation
How quickly and effectively you address feedback from code reviews, improving your code quality.
Target · Resolve 95% of code review comments within 48 hours of receiving them.

You submit a pull request, and a Senior Engineer leaves 5 comments. You address 4 of them within two days and discuss the fifth, leading to a quick resolution. This shows you're taking feedback seriously and learning.

Problem-Solving & Debugging
Your ability to independently diagnose and fix issues, especially when dealing with the unique challenges of quantum systems.
  • You're often the first to spot a subtle bug in a quantum circuit. You can differentiate between a code error and a hardware noise issue without needing constant hand-holding. You'll propose multiple solutions when something isn't working, not just give up.
Collaboration & Knowledge Sharing
How well you work with others, sharing your findings and helping the team move forward.
  • You actively participate in team discussions, offering insights and asking thoughtful questions. You'll proactively share interesting papers or experimental results with the team. You're happy to pair-program on a tricky problem or explain a concept to a new joiner.
Documentation & Reproducibility
The clarity and completeness of your documentation, making your work understandable and repeatable for others.
  • Your code comments are clear and concise. Your experimental notebooks are well-organised and easy to follow. Someone else could pick up your project and rerun your experiments without needing to ask you a dozen questions.
Initiative & Learning
Your drive to learn new quantum concepts and techniques, applying them to your work.
  • You'll often bring up new research papers you've read, suggesting how we might apply them. You're always trying out new features in the SDKs or exploring different cloud platforms. You don't wait to be told what to learn next.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Solving Genuinely Hard Problems

You'll be tackling challenges that have no known classical solutions, or where the 'right' answer isn't immediately obvious. This could mean optimising a complex circuit or trying to mitigate a new type of hardware noise.

Spending an entire afternoon trying to figure out why a specific quantum gate sequence always gives slightly off results, digging into the hardware specs and physics papers, and finally finding a workaround.

Continuous Learning & Exploration

The quantum field is constantly evolving. You'll be expected to keep up with the latest research, new SDK features, and emerging hardware. This role offers endless opportunities to learn new physics, maths, and programming techniques.

You're excited to spend your 'learning time' diving into a new quantum machine learning paper or experimenting with a different cloud quantum platform, just to see what it can do.

Building the Future of Technology

You'll be contributing directly to a nascent but potentially revolutionary technology. Your code could be a small piece of a much larger puzzle that changes industries.

The satisfaction of seeing your quantum circuit successfully run on a real QPU, even if it's just a small demonstration, knowing you're part of something bigger.

What frustrates people
  • The Simulator-to-Hardware Gap: You'll spend ages perfecting an algorithm in a noise-free simulator, only for it to produce complete garbage on the actual, noisy QPU. It's soul-crushing.
  • The Hardware Queue: You'll have a brilliant idea ready to test, but then have to wait hours, sometimes half a day, for your 15-minute slot on the quantum computer. Patience is a virtue here.
  • 'Is it a bug or is it physics?': This is the classic, maddening debugging problem. You can't tell if your code is wrong, the hardware is malfunctioning, or if you're just seeing a weird but valid quantum mechanical effect. It's a real head-scratcher.
  • The Hype Translator: You'll sometimes have to manage expectations, explaining to non-technical folks why our 127-qubit machine can't break RSA encryption today, despite what the latest press release might imply. It's a constant battle against unrealistic expectations.
  • Non-Deterministic Debugging: Trying to find a bug when the output of your program is inherently probabilistic. You can run the exact same code twice and get two different, valid answers, which makes finding a consistent error incredibly difficult.
What this role does not give you
  • A predictable, stable codebase: The quantum SDKs and hardware are constantly evolving, so expect breaking changes and new features regularly.
  • Immediate, tangible business impact: While the long-term potential is huge, most of what you'll build today is experimental, not production-ready.
  • A clear, well-trodden career path: This field is so new that you're helping to define what a 'quantum software engineer' even means.

8Who you work with

Your work directly impacts our ability to experiment with and develop new quantum applications. Good code means we learn faster and build better. Poor code means wasted resources and missed opportunities to push the boundaries of what's possible in quantum computing.

Inside the business
  • Your Manager (Senior Quantum Software Engineer)
  • Other Quantum Software Engineers (your peers)
  • Quantum Physicists (who dream up the algorithms)
  • Hardware Engineers (who build the machines)
  • Product Managers (who try to find real-world uses for quantum tech)
  • Research & Development team
Outside the business
  • Cloud Quantum Platform Providers (e.g., IBM, Amazon, Azure)
  • Academic Collaborators (sometimes we work with universities on specific projects)

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • A degree in Physics, Computer Science, Mathematics, or a related quantitative field (or equivalent practical experience).
  • At least 2 years of hands-on experience in quantum software development, either in academia or industry.
  • Demonstrable experience with at least one major quantum SDK (Qiskit or Cirq) and a strong command of Python.
  • A portfolio of quantum projects (e.g., GitHub repo, research papers) showcasing your ability to implement and analyse quantum algorithms.
  • A solid understanding of linear algebra, probability, and basic quantum mechanics.

10What to practise next

Where the job is going, and what to do about it starting this week.

Advanced Noise Mitigation & Error Suppression

As quantum hardware gets slightly better, the focus shifts from just 'running a circuit' to 'running a circuit reliably'. We'll need to extract meaningful results from increasingly noisy systems, and that means going beyond basic mitigation techniques.

Dynamical decoupling sequences · Zero-noise extrapolation (ZNE) · Error-aware compilation · Measurement error mitigation

  • This month: Read up on the latest papers on ZNE and error-aware compilation. Try to implement a basic ZNE routine in Qiskit or Cirq.
  • Next quarter: Experiment with different dynamical decoupling sequences on a real QPU, comparing their effectiveness.
  • Next 6 months: Propose an experiment to characterise a specific type of noise on our target hardware and suggest mitigation strategies.
  • Next 9 months: Present your findings on advanced noise mitigation to the team, highlighting practical applications.

Quick win: Start by understanding the noise models available in your preferred SDK. Run a simple benchmark circuit and try to apply one basic noise mitigation technique, observing the difference.

Quantum Machine Learning (QML) Applications

QML is one of the most promising near-term applications of quantum computing. As classical ML models hit their limits, quantum approaches might offer breakthroughs in areas like pattern recognition, optimisation, and generative modelling. You'll need to understand how to build and train these hybrid models.

Variational Quantum Classifiers (VQC) · Quantum Neural Networks (QNNs) · Data encoding strategies · Optimisation of QML models

  • This month: Complete an online course or tutorial on QML, focusing on VQC or QNNs.
  • Next quarter: Implement a simple quantum classifier for a small dataset using PennyLane or Qiskit's QML module.
  • Next 6 months: Explore different data encoding strategies and compare their performance on a specific QML task.
  • Next 9 months: Propose a small-scale QML project that could demonstrate a potential advantage over classical methods for a specific business problem.

Quick win: Read a foundational paper on QML (e.g., 'Quantum Machine Learning' by Schuld and Petruccione) and try to implement one of the basic examples in a simulator.

11Staying current once you are in

What people here do to keep up
  • Regularly read and engage with new research papers on arXiv.org, especially in your areas of interest (e.g., QML, optimisation, error correction).
  • Actively participate in quantum computing online forums, communities, and open-source projects (e.g., contributing to Qiskit, Cirq, or PennyLane).
  • Attend quantum computing conferences, workshops, and webinars to stay abreast of the latest developments and network with peers.
  • Take advanced online courses or specialisations in specific quantum computing sub-fields (e.g., quantum algorithms, quantum error correction).
  • Experiment with new quantum SDKs or cloud platforms as they become available, even if they're not part of our primary stack.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration for Quantum Research

Honestly, LLMs are changing how we do research and even how we write code. Competitors are already using tools like ChatGPT and Claude to draft research summaries, generate code snippets for classical pre-processing, and even brainstorm new algorithmic approaches in minutes. If you don't keep up, you'll be left behind.

  • Context windows and token limits
  • Temperature settings for different tasks
  • RAG (Retrieval Augmented Generation) architectures
  • Output validation and hallucination detection
  • Prompt chaining for complex analysis

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Quantum Algorithm Design & Implementation
  • Quantum Circuit Compilation & Transpilation
  • Noise Modelling & Mitigation
  • Hybrid Quantum-Classical Methods
  • Hamiltonian Simulation

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Associate Quantum Software Engineer (L1)

    1-2 years

    Skills to master

    • Mastering a primary quantum SDK (e.g., Qiskit), understanding basic quantum algorithms, strong Python programming, and effective debugging techniques.

    You're ready to move on when

    • Consistently delivering assigned quantum software tasks with minimal supervision.
    • Successfully implementing and testing common quantum algorithms on simulators and basic hardware.
    • Proactively identifying and proposing solutions for minor technical issues.
    • Receiving positive feedback on code quality and documentation.
  2. 2

    Classical Software Engineer with Quantum Interest

    2-3 years (with self-study/training)

    Skills to master

    • Deep dive into quantum mechanics fundamentals, linear algebra, and quantum information theory. Hands-on experience with quantum SDKs and building small quantum projects.

    You're ready to move on when

    • Demonstrable personal projects in quantum computing (e.g., GitHub repo).
    • Completion of advanced quantum computing courses or certifications.
    • Strong classical software engineering skills (e.g., Python, C++) applied to scientific computing.
    • Ability to articulate the challenges and opportunities in quantum computing.
  3. 3

    Academic Researcher (Postdoc/PhD)

    0-2 years (direct entry)

    Skills to master

    • Translating theoretical research into practical code, understanding software engineering best practices (version control, testing), and adapting to industry pace.

    You're ready to move on when

    • Published research in quantum computing or related fields.
    • Experience with scientific programming and data analysis.
    • Ability to work effectively in a team-oriented, product-focused environment.
    • Strong problem-solving and analytical skills from research background.

15Where this role leads

The long view: This work can lead to a staff quantum software engineer role, or on to principal quantum engineer, guiding research into areas such as quantum machine learning or optimisation.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Quantum Software Engineer is actually changing.

Where it leads

Roles this leads to

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Developing low level engineering software, worked against Quantum Software Engineer.

Developing low level engineering software Level 4

Applied to your work in Quantum Software Engineer

The objective of this unit is to enable learners to interpret requirements, design, develop, test, and document low level engineering software components, ensuring functionality and adherence to coding standards.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

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See Your Progress Grow Illustration
Quantum Software Engineer
  • Quantum Algorithm Design & Implementation
  • Quantum Circuit Compilation & Transpilation
  • Noise Modelling & Mitigation
  • Hybrid Quantum-Classical Methods
  • Hamiltonian Simulation
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Quantum Software Engineer is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Senior Quantum Software Engineer (L3)

    3-5 years in this role

    You'll move from owning components to leading entire workstreams and designing novel algorithms. You'll also start mentoring junior engineers.

    • Designing novel quantum algorithms from scratch for specific problems.
    • Deep expertise in quantum error correction or advanced noise mitigation techniques.
    • Architecting complex hybrid quantum-classical workflows.
    • Contributing to open-source quantum SDKs or publishing internal technical papers.
    • Benchmarking and evaluating new quantum hardware and platforms.
Working with AI on the job

Working with AI

Where AI is starting to help

Imagine spending less time on the mundane bits of coding and more on the truly mind-bending quantum challenges. That's the reality we're building here, using AI to supercharge your productivity.

In quantum software, the classical 'wrapper' code—data handling, plotting, basic infrastructure—can eat up a lot of your day. Plus, keeping up with the torrent of new research is a job in itself. AI tools are becoming incredibly good at handling these tasks, letting you focus on the qubits and the physics.

Classical Code Scaffolding

Use AI code assistants like GitHub Copilot to auto-generate the Python boilerplate for data loading, pre-processing, and results visualisation. This means you can focus almost entirely on the core quantum algorithm, not the surrounding plumbing. It's like having a coding assistant who never sleeps.

Noise Pattern Analysis

Train simple machine learning models to analyse the massive amounts of experimental data coming off a QPU. These models can spot subtle, recurring error patterns or correlations in qubit crosstalk that would be practically impossible for a human to find. This helps us understand the hardware better and design more robust circuits.

arXiv Research Summariser

Set up an LLM-based agent to constantly monitor arXiv.org for new papers matching your specific keywords (e.g., 'quantum error correction codes', 'VQE optimisations'). It'll then provide daily one-paragraph summaries directly to your inbox, saving you hours of sifting through academic literature.

Documentation & Translation

After you've developed a new quantum algorithm or experimental setup, use an AI tool to generate the initial draft of your technical documentation for Confluence. You can then ask it to create a simplified, high-level explanation for a presentation to the product or business team, making complex ideas accessible.

Common questions

Common questions

How do you become a Quantum Software Engineer?

Common routes in include Associate Quantum Software Engineer (1-2 years), Classical Software Engineer with Quantum Interest (2-3 years (with self-study/training)) and Academic Researcher (Postdoc/PhD) (0-2 years (direct entry)). Times vary with prior experience.

Where can a Quantum Software Engineer progress to?

This role can lead on to Senior Quantum Software Engineer (3-5 years in this role), depending on the skills you build.

What new skills matter most for a Quantum Software Engineer?

Increasingly, Prompt Engineering & LLM Integration for Quantum Research. These are the skills our record of this role lists as growing.

Free tools

Want a read on where you stand?

Three free tools, no account needed. Each one ends with a role, a level and a way in.

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17Where to go from here

Other roles at Level 3

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in technology and data

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills apply across the quantum technology sector, including hardware manufacturers, quantum software companies, and large enterprises exploring quantum applications in finance, pharma and materials science. They can also lead into deep tech research roles in academia or government labs.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my Quantum Software Engineer plan, free What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.