A funded 4-year joint PhD between King’s College London and the National Physical Laboratory is available for methodological developments into quantum algorithms for realistic electronic structure. Quantum computers are on their way, and (as originally envisaged by Feynman) one of the most impactful application areas they are expected to disrupt is in the simulation of electronic structure for molecular and materials modelling. In a collaboration between Prof. George Booth at King’s College London (KCL), and Dr. Yannic Rath at the National Physical Laboratory (NPL), this ambitious PhD project will work at the intersection of classical and quantum algorithm development, to devise scalable and robust quantum algorithms for the simulation of chemistry and physical processes over reactive timescales.
In particular, we will consider a grand challenge of simulating photo-induced reactivity of molecular processes, leveraging quantum devices and data-driven inference for the end-to-end simulation of non-adiabatic wave function dynamics over realistic timescales. This will build on recent advances in a novel data-efficient classical interpolation scheme which acts directly on quantum variables, to allow for noise-resilience and boosting of the accessible time scales of the quantum simulation. By interpolating smooth electronic features from sparse quantum data, we aim to leverage both quantum and classical resources to reduce quantum computational demands by many orders of magnitude.
The candidate will be embedded in an active research environment dedicated to developing novel numerical approaches bridging the gap between the promise and practical deployment of quantum computing for simulating quantum many-body systems at both KCL, and as part of NPL’s Quantum Software and Modelling team at its Teddington campus. The role will involve regular periods working at both London locations. Excellent opportunities for interaction exist within the UK quantum computing ecosystem, including the QCi3 hub, as well as London’s established materials modelling community, such as the Thomas Young Centre and the Materials and Molecular Modelling Hub. The collaboration also benefits from strong industrial ties with leading quantum computing and data-driven chemistry companies. The closing date may be brought forward once the position has been filled.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The core necessity for this research-oriented role stems from the need to close the technical readiness gap between Noisy Intermediate-Scale Quantum (NISQ) hardware and industrial application viability, particularly in computational chemistry. The fundamental challenge remains pushing quantum simulation timescales beyond current hardware coherence limits, a capability crucial for realizing quantum advantage in materials and molecular modeling. This position translates foundational algorithmic theory into scalable, hybrid computational schemes, directly addressing the noise sensitivity and resource demands that currently constrain end-user adoption of quantum methods for complex electronic structure problems. The focus on data-driven inference integrated with quantum algorithms provides a pathway to de-risk commercialization by accelerating the convergence of quantum and classical resources.-----Industry analysis consistently identifies quantum algorithm design, especially within the applications layer, as a critical bottleneck in the overall quantum value chain. Roles focused on mitigating the noise and resource demands inherent in current quantum systems are structurally essential. While quantum hardware development is progressing, the fidelity and qubit count limitations necessitate a heavy reliance on hybrid quantum-classical computing models to achieve meaningful computational results. This PhD research sits squarely within the "applications enablement" domain, focusing on quantum chemistry and materials science—one of the earliest and most promising application sectors projected to yield disruptive commercial value.
Global quantum strategies emphasize the development of sophisticated software stacks that efficiently partition computational labor between classical high-performance computing (HPC) resources and nascent Quantum Processing Units (QPUs). The integration challenge is compounded by the scarcity of talent possessing dual expertise in advanced quantum mechanics and robust software engineering practices required to bridge these architectures. The goal of devising scalable and robust algorithms for non-adiabatic wave function dynamics directly supports the translation pathway from foundational academic research to commercially viable simulations, a key objective for governmental and industrial quantum investment. Furthermore, leveraging data-efficient interpolation techniques reflects a sector-wide trend toward maximizing the utility of noisy quantum outputs and reducing the exponential scaling of quantum circuit depth, which is currently a major impediment to achieving real-world quantum advantage.-----The foundational skill architecture for this role centers on mastering the interface between electronic structure theory and low-level quantum circuit implementation. Core capabilities include advanced proficiency in quantum algorithms suchiversional variational methods (e.g., VQE, QAOA) and phase estimation, coupled with deep understanding of classical simulation methods (e.g., density functional theory, coupled cluster). Technical leverage is derived from developing hybrid workflows that dynamically allocate tasks across heterogeneous computing environments. This requires fluency in quantum programming environments (such as Qiskit or Cirq) alongside classical computational chemistry packages. The integration of data-driven interpolation and machine learning techniques is crucial for noise mitigation and achieving non-linear compression of quantum data, thereby improving the efficiency of post-processing and boosting the effective simulation timescale beyond the physical limits of current quantum coherence. This cross-domain expertise is necessary to build robust, reproducible quantum computational protocols.-----Develops novel hybrid classical-quantum computational paradigms.
Accelerates the time-to-solution for complex molecular reaction simulations.
Quantifies the resource requirements for achieving verified quantum advantage in chemistry.
Establishes new metrics for noise-resilient quantum algorithm performance.
Enhances the scalability of non-adiabatic dynamics simulations on existing QPUs.
Reduces the circuit depth necessary for accurate quantum expectation value calculation.
Creates data-efficient protocols for extracting smooth features from sparse quantum output.
Drives the maturation of quantum software tooling and application development kits.
Validates methodological approaches against established metrology standards set by NPL Careers.
Improves the accessibility of quantum chemical simulations for industrial end-users.
Translates academic quantum physics innovations into high-impact chemical engineering applications.
Fosters critical interoperability between quantum hardware providers and classical simulation platforms.-----Industry Tags: Quantum Algorithms, Computational Chemistry, Electronic Structure, Hybrid Quantum-Classical Computing, Materials Modelling, Non-Adiabatic Dynamics, Quantum Software, NISQ
Keywords:
NAVIGATIONAL: King's College London quantum research opportunity, NPL Careers PhD position quantum computing, Quantum algorithms for chemical reactions PhD, Hybrid quantum classical computing workflows, Simulation of molecular processes research, Quantum advantage recalibration electronic structure, Non-adiabatic wave function dynamics simulation
TRANSACTIONAL: Scalable quantum algorithms for noisy computers, Optimize quantum simulation of chemical reactions, Quantum computing application development chemistry, Advanced electronic structure quantum algorithms, Data-driven inference quantum simulations, Develop robust quantum algorithms for chemistry, Noise resilience techniques quantum computing applications
INFORMATIONAL: What is hybrid quantum-classical computing in chemistry, Future applications of quantum simulation in materials, Challenges in scaling quantum algorithms for chemistry, Explaining quantum advantage for chemical reactions, Integrating classical interpolation with quantum data, Role of quantum metrology in algorithm development, Understanding non-adiabatic quantum dynamics simulation
COMMERCIAL INVESTIGATION: Quantum software roadmap for industrial chemistry, Comparing VQE performance in chemical simulation, Commercial viability quantum algorithms molecular modelling, Investment trends in hybrid quantum computing, Accelerating drug discovery with quantum simulation, Quantum simulation of reactive timescales feasibility
Authority Tag: QED-C Workforce and Education Technical Advisory Committee Reports
