Quantum kernel anomaly detection for credit card fraud
Detect anomalous, potentially fraudulent, transactions in a credit-card dataset, framed as an anomaly-detection task and compared against classical kernel-based benchmarks such as one-class support vector machines.
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Detect anomalous, potentially fraudulent, transactions in a credit-card dataset, framed as an anomaly-detection task and compared against classical kernel-based benchmarks such as one-class support vector machines. Kyriienko and Magnusson develop quantum protocols for anomaly detection and apply them to credit card fraud detection. They first establish classical benchmarks from supervised and unsupervised machine learning methods, choosing average precision as their metric for detecting anomalous data, and focus on kernel-based approaches — basing their unsupervised modelling on one-class support vector machines (OC-SVM) — for ease of direct comparison to the quantum protocols. They then employ quantum kernels of different type for the same anomaly-detection task, and report that quantum fraud detection can challenge equivalent classical protocols as the number of features grows, where the number of features equals the number of qubits used for data embedding. Running simulations with registers up to 20 qubits, they find that quantum kernels with a re-uploading structure give better average precision than the classical benchmarks, with the advantage increasing with system size, reaching a quantum-classical separation of 15% in average precision at 20 qubits. The authors discuss the prospects of fraud detection on near- and mid-term quantum hardware and describe possible future improvements.
Circuit & simulation
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What this takes and returns
TakesNothingWhat joins here
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ReturnsNothingWhat joins here
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How it works
Kyriienko and Magnusson develop quantum protocols for anomaly detection and apply them to credit card fraud detection. They first establish classical benchmarks from supervised and unsupervised machine learning methods, choosing average precision as their metric for detecting anomalous data, and focus on kernel-based approaches — basing their unsupervised modelling on one-class support vector machines (OC-SVM) — for ease of direct comparison to the quantum protocols. They then employ quantum kernels of different type for the same anomaly-detection task, and report that quantum fraud detection can challenge equivalent classical protocols as the number of features grows, where the number of features equals the number of qubits used for data embedding. Running simulations with registers up to 20 qubits, they find that quantum kernels with a re-uploading structure give better average precision than the classical benchmarks, with the advantage increasing with system size, reaching a quantum-classical separation of 15% in average precision at 20 qubits. The authors discuss the prospects of fraud detection on near- and mid-term quantum hardware and describe possible future improvements. The Classiq library carries this subject under applications · finance. The sources read state no complexity bound for this record (The abstract of arXiv:2208.01203, the only source read for this record, states no computational complexity bound anywhere in it — no query count, no gate count, no circuit depth, and no asymptotic scaling in the number of qubits or features. Its quantitative content is a simulated statistical-performance result, not a complexity bound: "Performing simulations with registers up to 20 qubits, we find that quantum kernels with re-uploading demonstrate better average precision, with the advantage increasing with system size." The same abstract adds, "Specifically, at 20 qubits we reach the quantum-classical separation of average precision being equal to 15%." Average precision is a classification-quality metric, not a running-time or resource bound, so it is not recorded in the complexity field. The abstract's only complexity-flavored remark is qualitative and unquantified: it observes that quantum FD "can challenge equivalent classical protocols at increasing number of features (equal to the number of qubits for data embedding)" without stating a rate, an exponent, or any other scaling expression. The Classiq index entry for applications/finance/credit_card_fraud gives a directory path and a file list (credit_card_fraud.ipynb, credit_card_fraud.qmod) and states no bound either. The complexity field is therefore left empty on purpose.).
Implementation
Unsupported
quantum-kernel-fraud-detection.txt
ALGORITHM: Quantum kernel anomaly detection for credit card fraud
PROBLEM: Detect anomalous, potentially fraudulent, transactions in a credit-card dataset, framed as an anomaly-detection task and compared against classical kernel-based benchmarks such as one-class support vector machines.
IDEA: Kyriienko and Magnusson develop quantum protocols for anomaly detection and apply them to credit card fraud detection. They first establish classical benchmarks from supervised and unsupervised machine learning methods, choosing average precision as their metric for detecting anomalous data, and focus on kernel-based approaches — basing their unsupervised modelling on one-class support vector machines (OC-SVM) — for ease of direct comparison to the quantum protocols. They then employ quantum kernels of different type for the same anomaly-detection task, and report that quantum fraud detection can challenge equivalent classical protocols as the number of features grows, where the number of features equals the number of qubits used for data embedding. Running simulations with registers up to 20 qubits, they find that quantum kernels with a re-uploading structure give better average precision than the classical benchmarks, with the advantage increasing with system size, reaching a quantum-classical separation of 15%in average precision at 20 qubits. The authors discuss the prospects of fraud detection on near- and mid-term quantum hardware and describe possible future improvements.
REPORTED COST: Not stated by the sources read
BASIS: The abstract of arXiv:2208.01203, the only source read for this record, states no computational complexity bound anywhere in it — no query count, no gate count, no circuit depth, and no asymptotic scaling in the number of qubits or features. Its quantitative content is a simulated statistical-performance result, not a complexity bound: "Performing simulations with registers up to 20 qubits, we find that quantum kernels with re-uploading demonstrate better average precision, with the advantage increasing with system size." The same abstract adds, "Specifically, at 20 qubits we reach the quantum-classical separation of average precision being equal to 15%." Average precision is a classification-quality metric, not a running-time or resource bound, so it is not recorded in the complexity field. The abstract's only complexity-flavored remark is qualitative and unquantified: it observes that quantum FD "can challenge equivalent classical protocols at increasing number of features (equal to the number of qubits for data embedding)" without stating a rate, an exponent, or any other scaling expression. The Classiq index entry for applications/finance/credit_card_fraud gives a directory path and a file list (credit_card_fraud.ipynb, credit_card_fraud.qmod) and states no bound either. The complexity field is therefore left empty on purpose.
DEMONSTRATED BY: the Classiq library entry applications/finance/credit_card_fraud
PRIMARY SOURCE: Oleksandr Kyriienko, Einar B. Magnusson (2022), Unsupervised quantum machine learning for fraud detection — https://arxiv.org/abs/2208.01203
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Quantum vs classical
Classical baseline
Compare Quantum machine learning with the strongest classical method for the same instance, input budget, and output metric.
Quantum claim
This reference exposes a quantum circuit pattern; it does not imply an application-level speedup without a matched benchmark.
How to compare
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Literature & references
Unsupervised quantum machine learning for fraud detection2022 · Oleksandr Kyriienko, Einar B. Magnusson
Primary source: it describes the classical benchmarks (supervised and unsupervised methods including OC-SVM), the quantum-kernel protocols including re-uploading, and reports the simulated average-precision separation of 15% at 20 qubits. Consult it for the dataset used, the specific quantum kernels compared, the circuit constructions, and any results beyond 20 simulated qubits, none of which the abstract states.