The claim by HSBC that noise in an IBM quantum computer helped deliver a 34% improvement in algorithmic trading performance has been disputed by experts, with Scott Aaronson, a leading academic in the field, saying that the finding had \u201cmore red flags than a Peoples Liberation Army parade\u201d, in an interview with The DESK\u2019s sister publication Global Trading.<\/p>\n
On 24 September, HSBC announced that in a project with IBM, it had delivered a 34% improvement in fill-rate modelling for bond request-for-quote (RFQ) using quantum-generated features and common data science algorithms.\u00a0<\/p>\n
Real-time estimation of fill probabilities of requests for quotes (RFQs) from a panel of dealers, which due to the complexity of datasets is typically best handled by machine learning, focused on the algorithmic trading of corporate bonds.<\/p>\n
Standard machine-learning algorithms work by identifying features from a training dataset in order to make predictions with test data. Working in conjunction with IBM, the HSBC team used a quantum computer to generate new machine learning features using a superposition of quantum states. Back-testing this output with an area-under-the-curve (AUC) metric, the quantum approach improved on non-quantum approaches by 34%, HSBC claimed.\u00a0\u00a0<\/p>\n
Yet HSBC conceded in its paper that the observed effect was purely empirical, caused by \u2018inherent noise\u2019 in the process with no theoretical foundation. That means the much-touted result is most likely a cherry-picked example of selection bias, according to Aaronson, who is chair of computer science and head of the Quantum Information Centre at the University of Texas.\u00a0\u00a0<\/p>\n
Aaronson said that quantum computing advances over the past 30 years were limited to two areas. \u201cSimulating quantum systems, which might help for example with designing new drugs and materials and industrial processes, and 2) breaking most of the public-key cryptography (such as RSA) that currently protects the Internet\u201d.\u00a0\u00a0<\/p>\n
Describing the HSBC paper as \u201cscientifically risible\u201d and \u201cfatally flawed\u201d, Aaronson added, \u201cthe \u2018advantage\u2019 is just a strange artifact of the particular methods that they decided to compare\u2014that it has nothing really to do with quantum mechanics in general, or with quantum computational speedup in particular\u201d.\u00a0<\/p>\n
Morten Hagen, a data science expert who built machine learning models for American Express before founding his own company, Context Technologies, told Global Trading that a well-known method known as \u201crandom resampling with shrinkage\u201d could potentially replicate the noise effect of IBM\u2019s quantum computer. Shrinkage has a long history in the quantitative finance community, particularly in the estimation of covariance matrices.\u00a0<\/p>\n
\u201cThis means that you create a much larger dataset, built on the original data. In each \u2018draw\u2019 or \u2018instance\u2019 you replace some of the feature values with a new \u2018sampled\u2019 value that is \u2018shrunk\u2019 towards zero\u201d, Hagen said. \u201cMuch the same as the quantum \u2018black-box\u2019, that would have the effect of normalizing the classical distributions.\u201d\u00a0<\/p>\n
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\u00a9Markets Media Europe 2025<\/p>\n