The finance module of Qiskit, namely the (qiskit-finance) <\/strong>module, already contains everything you require, including a LogNormalDistribution<\/strong> circuit that encodes the probability distribution of the future price of the underlying asset, and an EuropeanCallPricingObjective<\/strong> circuit that encodes the payoff function into the amplitude of one of its qubits. By combining these components and then passing them through IterativeAmplitudeEstimation<\/strong>, you obtain the price of your option.<\/p>\n\n\n\n As many parts of the API were new to me, I used an AI tool (Manus) to write my first draft of the script, the same way I used AI for debugging and concept clarification in the previous project. It didn\u2019t give any error, came up with the price estimate almost equal to the $10.45 baseline estimated using Black Scholes model, and even showed the ef fect of \u201cqubit gap\u201d which I hoped it would, wherein the estimates are very poor for low qubit numbers but converge to the correct value for higher numbers.<\/p>\n\n\n\n Notable Bug I Encountered<\/strong><\/p>\n\n\n\n I Ran that exact same script a second time, no changes at all just to confirm the number. Got an entirely different outcome – not even remotely close to the last one so clearly something was wrong<\/p>\n\n\n\n What I discovered was that the culprit in this case was relatively easy to pin down: there wasn’t any seeding in the script. The randomness is entirely regenerated when running each iteration of the classical Monte Carlo simulation as well as the quantum sampling simulation, making a single pass of the qubit numbers simply a random noisy data point. What the original version had generated was essentially a fluke – lucky, unlucky, doesnt matter <\/p>\n\n\n\n This is a sort of continuation of the “1,000 samples landed above 500 samples” issue of the previous post.<\/p>\n<\/div> Fixing It<\/strong><\/p>\n\n\n\n This “fix” is the same as the classical baseline idea: do not rely on single runs anymore and use the average results of many repetitions instead. Now for each qubit size, I perform 8 repetitions of the price calculation circuit with different seeds and provide an average error and spread among those repetitions, rather than a single misleading value.<\/p>\n\n\n\n Here’s what that actually looks like after alot of debugging<\/p>\n\n\n\n This is the actual implementation of what I was talking about. From 2 qubits to 7 qubits, there’s a reduction of over 20 times in errors, and this time it’s a smooth, monotonically increasing function rather than a one-shot noisy result. I applied the same concepts I learned from the last project on quantum computing <\/p>\n\n\n\n The real lesson here isn’t about quantum computing at all; it is the lesson of being cautious about accepting an answer when the code works without any errors and gives you a sensible number. Being error-free doesn’t mean being right, and it’s always better to constantly change and improve your methodology to produce the optimal results. I had a similar experience last year when running the code for my EAQEC project, atleast 13 versions of the main code was created.<\/p>\n\n\n\n Next up I want to put the classical Monte Carlo convergence and this QAE convergence on the same chart<\/p>\n","protected":false},"excerpt":{"rendered":" (and a bug that taught me more than the code did) 7\/21\/2026 Since the last post I got back into VSCode and Qiskit properly, and this update is less about a result and more about a mistake I almost didn’t catch GETTING THE FIRST NUMBER The finance module of Qiskit, namely the (qiskit-finance) module, already […]<\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"class_list":["post-263","post","type-post","status-publish","format-standard","hentry","category-qubitoptions"],"jetpack_featured_media_url":"","_links":{"self":[{"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/posts\/263","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/comments?post=263"}],"version-history":[{"count":2,"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/posts\/263\/revisions"}],"predecessor-version":[{"id":269,"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/posts\/263\/revisions\/269"}],"wp:attachment":[{"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/media?parent=263"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/categories?post=263"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-json\/wp\/v2\/tags?post=263"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/wp.stgeorges.bc.ca\/vincentz\/wp-content\/uploads\/sites\/18\/2026\/07\/image-1-1024x399.png\")
<\/figure><\/div>\n\n\n\n# before \u2014 every run draws fresh randomness, nothing is reproducible<\/strong>\nsampler = StatevectorSampler(default_shots=4096)\n\n# after \u2014 seeded, and repeated across trials instead of trusting one run<\/strong>\nsampler = StatevectorSampler(seed=seed, default_shots=4096)<\/code><\/pre>\n\n\n\nQubits<\/th> Average error vs. exact price<\/th> Spread across trials<\/th><\/tr><\/thead> 2<\/td> $1.43<\/td> \u00b1 $0.07<\/td><\/tr> 3<\/td> $0.26<\/td> \u00b1 $0.07<\/td><\/tr> 4<\/td> $0.14<\/td> \u00b1 $0.05<\/td><\/tr> 5<\/td> $0.12<\/td> \u00b1 $0.06<\/td><\/tr> 6<\/td> $0.09<\/td> \u00b1 $0.04<\/td><\/tr> 7<\/td> $0.06<\/td> \u00b1 $0.06<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Lesson learned<\/strong><\/h3>\n\n\n\n