We are seeking a Research Scientist for our London location to work on quantum algorithms with a focus on solving differential equations.
In this role, you will design quantum algorithms and apply them to real-world problems. You will also contribute to resource estimation and implementation of relevant primitives on Quantinuum’s quantum hardware. The ideal candidate will have hands-on experience in at least one of our core research interests in quantum simulation, adiabatic quantum computing, quantum singular value transformation, Gibbs state preparation, variational quantum algorithms, partial differential equations. They will also have hands-on experience with at least one versatile classical approach to algorithm design, such as neural networks, tensor networks, quantum Monte Carlo, or exact diagonalization.
You will collaborate closely with researchers across the full quantum computing stack—including hardware, quantum error correction, and software teams—to translate theoretical advances into experiments on real quantum processors. The role also involves supporting collaborations with academic and industry partners to explore and develop promising use cases of quantum solvers for partial differential equations.
Researchers in this role are encouraged to publish their work in leading scientific journals and present their results at top international conferences.
Key responsibilities:
- Contribute to the team’s research & development (R&D) efforts in quantum algorithms for solving differential equations
- Collaborate with team members and other teams on quantum algorithm design and end-to-end resource estimation of quantum algorithms under realistic hardware constraints
- Contribute to external R&D collaborations
- Communicate scientific results via publications and presentations
Requirements:
- PhD in a relevant field (physics, mathematics, computer science etc.)
- Experience in quantum algorithms and their applications
What we value:
- Demonstrated research track record via publications in a relevant field
- Experience related to partial differential equations or quantum chemistry
- Familiarity with the implementation of quantum algorithms
- Programming skills in a high-level language such as Python and relevant tools
- An interest in fault tolerance and quantum error correction
- An interest in end-to-end resource estimates
- Excellent spoken and written communication skills
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What is in it for you?
Working alongside a highly talented team, with leading names in the quantum computing industry. We offer a highly competitive package, equity, 28 days of paid holiday (in addition to public holidays), a workplace pension, a positive approach to flexible working and enhanced parental and adoption benefits.
About Us:
Quantinuum is the world leader in quantum computing. The company’s quantum systems deliver the highest performance across all industry benchmarks. Quantinuum’s over 650 employees, including 400+ scientists and engineers, across the US, UK, Germany, and Japan, are driving the quantum computing revolution.
By uniting best-in-class software with high-fidelity hardware, our integrated full-stack approach is accelerating the path to practical quantum computing and scaling its impact across multiple industries.
By joining Quantinuum, you’ll be at the forefront of this transformative revolution, shaping the future of quantum computing, pushing the limits of technology, and making the impossible possible.
Visit our news pages to learn more about Quantinuum and our scientific breakthroughs and achievements: https://www.quantinuum.com/news
Quantinuum Intro Video: The Future of Quantum Computing
Please note that employment with us is subject to successfully passing our pre-employment screening checks. We are an inclusive equal opportunity employer. You will be considered without regard to age, race, creed, color, national origin, ancestry, marital status, affectional or sexual orientation, gender identity or expression, disability, nationality, sex, or veteran status.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The evolution of research and development functions specializing in algorithmic design represents a critical transition in the deep-tech sector from theoretical computational proofs to structured utility. As global quantum infrastructure matures, the structural necessity for engineering capabilities that bridge pure mathematical formulations and target physical architectures becomes paramount to realizing practical advantage. This specific role type serves as a primary translation pathway within the software layer, ensuring that complex mathematical systems are structurally compatible with physical constraints. Market data from national technology strategies and industry consortia indicate that advanced mathematical translation is essential for mitigating early-stage obsolescence across high-compute industries. By shifting abstract numerical solutions into deterministic execution primitives, this scientific function secures the groundwork for broader industrial adoption and multi-sector enterprise readiness.
The mathematical simulation landscape is experiencing a systematic pivot toward the integration of specialized computational kernels within high-performance workflows. While physical system development advances across multiple modalities, the primary barrier to industry adoption resides within the algorithmic architecture, particularly regarding the reproducibility and optimization of complex problem solvers. Current industry focus lies on bridging classical and quantum capabilities at scale, requiring sophisticated orchestration between theoretical complexity and physical hardware constraints to maintain processing throughput.
Workforce alignment remains a primary constraint at the intersection of domain-specific numerical analysis and quantum information science. As institutions progress toward fault-tolerant systems, the broader ecosystem requires specialized domain experts who can navigate structural software fragmentation and the absence of standardized algorithmic validation benchmarks. Current macroeconomic dynamics, driven by public-private capital initiatives and national research mandates, place a premium on expertise that establishes mathematical validation protocols across disparate execution environments.
Furthermore, integration with existing high-performance computing frameworks represents a major technical dependency for the global deep-tech value chain. The progression of industrial applications relies heavily on the ability to translate complex differential mathematical systems into native hardware routines without disrupting traditional high-compute delivery models. Consequently, the availability of specialized personnel capable of handling these structural cross-stack dependencies determines whether industrial organizations can successfully move from exploratory validation to field deployment.
The technical architecture for this role type centers on the synchronization of advanced mathematical modeling with the strict constraints of emerging hardware topologies. Deep capability within hardware-agnostic software layers is required to ensure that computational routines are optimized for specific physical parameters, such as limited coherence gates and system fidelities. This demands an explicit understanding of the interaction points between high-level application interfaces and the foundational compilers managing hybrid executions.
These distinct skill domains are vital to the strategic output of advanced technology organizations, enabling parallel exploration of novel software designs alongside scalable infrastructure planning. By establishing rigorous verification methods, this specialization provides the structural leverage needed to assess performance metrics prior to capital allocation. Navigating these multi-layered dependencies ensures that scientific results align directly with the rigid requirements of industrial deployment pipelines. - Accelerates the transition of advanced mathematical concepts into reproducible hardware execution pathways
- Reduces systemic development risk by aligning long-term numerical research with near-term hardware milestones
- Facilitates the deployment of complex computational solvers within high-performance computing frameworks
- Enhances the fidelity of organizational software strategies through rigorous mathematical benchmarking protocols
- Lowers iteration friction between pure scientific discoveries and scalable software application layers
- Optimizes the utilization of specialized technical talent across cross-functional research portfolios
- Stabilizes the emerging software value chain by providing clear requirements for system verification
- Supports the scaling of application layers by managing the parameters of hybrid compute workflows
- Improves technical transparency for external stakeholders reviewing long-term technology readiness lifecycles
- Enables the systematic replication of algorithmic outcomes through standardized implementation blueprints
- Protects capital allocations by ensuring scientific discoveries match industrial scaling realities
- Orchestrates the convergence of academic research frameworks with practical industry delivery mandatesIndustry Tags: Quantum Algorithms, Algorithmic Benchmarking, High-Performance Computing, Hybrid Classical-Quantum, Technology Readiness Pathways, Numerical Analysis, Software Optimization, Quantum Simulation
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