阿斯顿大学PhD position in Quantum Machine Learning申请条件要求-申请方
主办方
阿斯顿大学
PhD直招介绍
About the Project
Project Details
Financial crime, encompassing payment fraud, money laundering, identity theft, and insider trading, inflicts systemic damage on global economies. In the UK alone, fraud cost the banking industry $1.6 billion in 2024, and in April 2025 the UK government committed $161 million in quantum technology investment specifically to tackle crime, fraud and money laundering (World Economic Forum). Classical machine learning methods, whilst widely deployed, face structurally intractable challenges in this domain. Fraud datasets exhibit extreme class imbalance, with fraudulent transactions typically comprising less than 1% of records, non-stationary distributions, and high-dimensional feature spaces. These limitations reflect fundamental computational and statistical constraints that motivate exploration of different approaches.
Quantum Machine Learning (QML) offers a theoretically grounded alternative. Variational Quantum Circuits (VQCs) and quantum kernel methods embed classical data into exponentially large Hilbert spaces via parameterised quantum feature maps, enabling classifiers that operate in regions of function space inaccessible to polynomial-time classical algorithms. Quantum Generative Adversarial Networks (QGANs), and related quantum generative models such as Quantum Circuit Born Machines (QCBMs), utilise quantum superposition and entanglement to model complex, high-dimensional probability distributions, offering a principled mechanism for generating high-fidelity synthetic minority-class samples, directly addressing the class imbalance problem at its statistical root.
This PhD addresses three interconnected research challenges. First, it is not established which quantum feature maps and ansatz architectures are best suited to the distributional properties of financial crime data. Second, the question of whether QGAN-generated synthetic fraud data meaningfully improves quantum classifier performance, compared to standard quantum classifiers trained on imbalanced data alone, has not been rigorously answered on real hardware. Third, the adversarial threat surface of quantum financial crime detection systems remains uncharacterised: it is unknown whether VQCs and quantum kernel classifiers are inherently more or less robust than classical equivalents to adversarial perturbations crafted specifically in the financial crime domain.
The programme of work intends to proceed in three phases: (a) the design and execution of hardware-aware VQC and quantum kernel classifiers on IBM Quantum hardware, systematically investigating the role of data encoding strategies (amplitude, angle, and IQP-style encoding) and entanglement topology on classification performance for financial fraud data, (b) QGAN architectures will be trained and executed on quantum hardware, targeting the generation of statistically faithful synthetic fraudulent transaction data, and (c) a formal adversarial threat model will be developed for the quantum detection pipeline, covering input-space perturbation attacks, parameter-shift gradient leakage, and model extraction, with quantum-noise-aware defences evaluated on hardware.
The expected contributions include: (i) a hardware-validated, domain-specific framework for quantum feature map and ansatz co-design for financial crime data, (ii) the first systematic evaluation of QGAN/QCBM-augmented versus standard quantum classifiers for fraud detection on real quantum processors, (iii) a formal adversarial threat taxonomy and mitigation framework specific to quantum financial crime detection systems, and (iv) a cross-platform benchmarking spanning superconducting and trapped-ion architectures that provide empirically grounded guidance on hardware selection for financial QML applications.
This interdisciplinary project sits at the intersection of quantum computing, machine learning, cybersecurity, and financial technology, and offers the opportunity to contribute to an emerging field with significant scientific and societal impact.
阿斯顿大学 PhD position in Quantum Machine Learning项目有没有奖学金，是不是全奖Phd招生，下面我们一起看一下【大学名称】Phd的奖学金资助情况
项目资助情况
Funding Notes
This fully funded PhD covers 100% of tuition fees and provides an annual tax-free stipend at the UKRI rate (£21,805 for 2026/27), with increases in line with UKRI guidance for the duration of the studentship.
Please note that the successful candidate will be responsible for any costs relating to moving to Birmingham and/or visiting the Aston campus. International students must meet the financial requirements for the visa, flights, and NHS Surcharge. Applicants should be confident that they can meet these costs before applying.
Further information can be found here: Financial Requirements | Aston University( https://www.aston.ac.uk/current-students/support-services/international/prospective-students/financial-requirements#:~:text=The%20documents%20they%20generally%20accept,name%20or%20your%20parent%27s%20name )
阿斯顿大学Phd申请条件和要求都有哪些？PhD position in Quantum Machine Learning项目是不是全奖？有没有奖学金？下面我们一起看一下阿斯顿大学申请Phd直招需要具备哪些条件和要求，以及托福、雅思语言成绩要到多少才能申请。
申请要求
Person Specification
Candidates should have been awarded, or expect to achieve, EITHER:
a] a First or Upper Second Class award in their undergraduate degree, in a relevant subject.
OR
b] a First or Upper Second Class award in their undergraduate degree, and a Merit or Distinction in a Masters degree, both in a relevant subject.
Qualifications from overseas institutions will be considered, but performance must be equivalent to that described above, and the University reserves the right to ascertain this equivalence according to its own criteria.
Essential:
· Solid foundation in mathematics, particularly linear algebra, probability, and optimisation.
· Proficiency in Python programming.
· Good understanding of machine learning principles (e.g., supervised learning, model evaluation).
· Strong analytical and problem-solving skills, with the ability to work independently and conduct research.
Desirable:
· Familiarity with quantum computing frameworks/SDKs (e.g., Qiskit, PennyLane).
· Experience with deep learning libraries (e.g., PyTorch, TensorFlow).
· Understanding of cybersecurity concepts or adversarial machine learning.
· Interest in financial systems, fraud detection, or FinTech applications
报名方式
招生人信息
Dr M Khan, Prof J Alcaraz Calero
Email:
m.khan71@aston.ac.uk
j.alcarazcalero@aston.ac.uk
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