We are seeking a Research Scientist to join our team in London, working on the development of quantum algorithms with a focus on solving optimization problems. This role offers the opportunity to contribute to cutting-edge research aimed at advancing the capabilities of quantum computers and exploring their potential advantages for real-world applications.
In this role, you will develop novel quantum algorithms and computational paradigms, and rigorously benchmark their performance against state-of-the-art classical methods. You will implement these algorithms, or key algorithmic primitives, on Quantinuum’s quantum hardware and optimize their performance under realistic hardware constraints, leveraging techniques such as error mitigation and quantum error correction.
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 for quantum optimization algorithms.
Researchers in this role are encouraged to publish their work in leading scientific journals and present their results at top international conferences.
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Key Responsibilities:
- Conduct research on quantum algorithms with a focus on optimization problems
- Design, implement, and optimize quantum algorithms on simulators and Quantinuum’s quantum hardware
- Contribute to end-to-end resource estimation of quantum algorithms under realistic hardware constraints
- Communicate scientific results internally across teams and externally through publications and conference presentations
Requirements:
- PhD (or equivalent experience) in physics, mathematics, computer science, or a related field
- Research experience in quantum algorithms
What We Value:
- Demonstrated research track record through publications in quantum algorithms for optimization or related areas
- Hands-on experience implementing quantum algorithms on quantum hardware
- Experience in mathematical optimization
- Excellent written and oral communication skills
- Programming experience in a high-level language such as Python and familiarity with quantum software frameworks such as pytket, Guppy, or Qiskit
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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 emergence of quantum optimization research scientists is a structural response to the critical transition from theoretical advantage to industrial utility within the deep-tech value chain. As global quantum hardware architectures move through Noisy Intermediate-Scale Quantum stages toward fault tolerance, the bottleneck has shifted toward the translation of abstract computational paradigms into hardware-efficient algorithmic primitives. These roles are essential for mitigating the Technology Readiness Level gaps that currently prevent broad commercial adoption of quantum-enhanced decision-making. By rigorously benchmarking quantum performance against state-of-the-art classical solvers, this role type provides the empirical validation required to justify large-scale capital deployment in quantum infrastructure. This functional necessity is driven by a sector-wide prioritization of hybrid quantum-classical workflows that can address immediate bottlenecks in logistics, materials science, and financial modeling.
The quantum algorithms sector currently operates at a pivotal juncture where the primary challenge is achieving a demonstrable "quantum utility" on available hardware. The role of an algorithm researcher is positioned within the "software and applications" layer of the value chain, acting as the critical feedback loop between hardware constraints and end-user requirements. Macro-level analysis indicates that while hardware scaling is progressing, a significant talent shortage persists in the "algorithmic translation" tier—specifically for experts who can navigate the nuances of error mitigation and hardware-native gate sets. This scarcity poses a risk to the scalability of the entire ecosystem, as hardware without efficient, benchmarked algorithms remains commercially inert.
Current industry focus lies on bridging classical and quantum capabilities at scale through the development of hybrid workflows. This involves integrating quantum processors as specialized accelerators within existing high-performance computing (HPC) environments. National quantum strategies globally emphasize this integration, recognizing that the first practical impacts of quantum optimization will likely occur in complex, multi-variable environments where classical methods hit exponential scaling walls. However, vendor fragmentation and a lack of standardized benchmarking protocols remain significant macro constraints, requiring researchers to develop portable software frameworks that can operate across disparate hardware modalities.
Furthermore, the sector-wide trend toward full-stack integration necessitates that algorithm development is no longer isolated from hardware engineering. The ability to optimize algorithmic performance under realistic constraints, such as limited qubit connectivity and coherence times, has become a primary determinant of a firm's competitive advantage. As the industry moves toward pilot production, the reproducibility and resource estimation of these algorithms will serve as the foundational metrics for investor confidence and sovereign technology roadmaps.
The capability architecture for this role type centers on the intersection of mathematical optimization, quantum information theory, and high-level software engineering. Mastery of the algorithmic stack is required to move beyond standard circuit models into domain-specific computational paradigms that leverage the unique physics of trapped-ion or superconducting systems. This technical coupling is critical for ensuring that theoretical breakthroughs in quantum speedup are not lost to the overhead of error correction or inefficient gate decomposition. By utilizing specialized quantum software frameworks like pytket or Qiskit, researchers enable a level of interoperability that allows for rapid iteration across different physical backends.
Structural throughput in this domain is further enhanced by expertise in classical benchmarking, which provides the baseline for determining true quantum advantage. These capabilities are essential for the end-to-end resource estimation of quantum algorithms, directly influencing the strategic planning of hardware roadmaps and the prioritization of near-term use cases. Beyond pure research, the role facilitates a cross-functional interface with hardware teams to inform the design of future QPU architectures based on algorithmic performance data. This feedback loop is a prerequisite for advancing the industry's collective technology readiness and ensuring the stability of hybrid-classical systems.
Accelerates the deterministic progression of quantum optimization from theoretical research to practical industrial application
Mitigates systemic risks in hardware development by providing algorithmic feedback on architectural constraints and gate fidelities
Facilitates the transition to hybrid quantum-classical computing architectures within existing high-performance computing ecosystems
Reduces iteration friction in the software stack by standardizing benchmarking protocols against classical state-of-the-art solvers
Strengthens the commercial viability of quantum hardware through the development of high-efficiency algorithmic primitives
Harmonizes global research efforts by publishing reproducible results in high-impact scientific journals and international conferences
Optimizes the allocation of computational resources by providing rigorous end-to-end resource estimations for scaled applications
Supports the scaling of quantum utility by implementing hardware-aware error mitigation and error correction techniques
Shortens the time-to-market for sector-specific solutions in logistics, finance, and materials science through targeted optimization research
Improves the interoperability of the quantum software ecosystem through the use of portable, high-level programming frameworks
Protects strategic investments in quantum infrastructure by validating the computational advantages of proprietary hardware backends
Enables the maturation of the quantum workforce by bridging the gap between academic physics and industrial software engineering
Industry Tags: Quantum Optimization, Algorithm Research, Benchmarking, Hybrid Quantum-Classical, Error Mitigation, Resource Estimation, Full-Stack Integration, Trapped-Ion Systems, Quantum Software Engineering
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