McKinsey’s $600 Billion Quantum Finance Number Doesn’t Add Up. Literally.

Introduction

In late April 2026, McKinsey published its Quantum Technology Monitor 2026 with a projection that quantum computing could create 400 billion to 600 billion USD of value in financial services by 2035, an average impact of 3.0 to 4.5 percent across the sector. Executives are already repeating the number. McKinsey’s own banking article, updated in February 2026, presents the range as an established estimate, and its footnote cites the source as the June 2025 edition of the Monitor. The number has circulated for a year. Almost nobody who quotes it has examined the model behind it.

I examined the model. Four things are true of it. The arithmetic on McKinsey’s own slide cannot be reproduced from the assumptions displayed on it. Its impact estimates measure quantum computing and AI together, with no allocation between them. Depending on the line, the comparison baseline is either frozen at today’s technology or not disclosed at all. And nothing in the finance chapter engages the peer-reviewed resource estimates, including two papers from Goldman Sachs’ own researchers, that quantify what these applications would demand from hardware. Any one of these would be a caveat. Together they mean the 400 to 600 billion USD range is an expert-interview percentage multiplied by a revenue baseline, and no executive should treat it as more than that.

To be fair to McKinsey up front: the Monitor labels its value estimates approximate rather than definitive, and its industry-overview slide carries a caveat that quantum computing’s incremental impact overlaps with generative AI, so the totals are not fully additive. Those caveats are real. They are also printed in small type, while the 600 billion figure appears in headlines and client decks without them. I covered the Monitor’s market data when it launched, and much of that material is useful. The finance chapter deserves separate treatment because, measured against my own research in the Quantum Utility Map, finance is the sector where the distance between headline and evidence is widest, and because financial institutions are the audience most likely to misallocate real budgets in response.

How McKinsey builds the number

The model is top-down. McKinsey takes a projected 2035 revenue or spend baseline for each of seven financial sub-sectors: corporate banking at 3,500 billion USD, retail banking at 4,200 billion, payments at 1,600 billion, investment banking at 610 billion, asset and wealth management at 980 billion, risk and cybersecurity spend at 3,100 billion, and operations and finance at 70 billion. Each baseline gets an impact hypothesis, a percentage between 1 and 8 percent sourced to expert interviews and raw data from Panorama Solutions, which appears to be McKinsey’s own proprietary financial-services data platform. Each segment also carries a label stating what share of the division quantum computing affects: 50 percent of corporate banking, 70 percent of payments, 22 percent of investment banking, and so on.

No peer-reviewed resource estimate, hardware benchmark, or algorithmic paper appears anywhere in the finance chapter’s source lines.

The percentages that never enter the arithmetic

A careful reader hits the first problem within five minutes. Multiply each baseline by its impact hypothesis and you reproduce the chart exactly. Corporate banking: 3,500 billion times 3.5 to 5.5 percent gives 122.5 to 192.5 billion, against a charted 120 to 190. Risk and cybersecurity: 3,100 billion times 3 to 5 percent gives 93 to 155 billion, against a charted 95 to 155. Run all seven segments this way and the total comes to roughly 391 to 605 billion, matching the chart’s stated 390 to 600, which the headline rounds up to 400 to 600.

The affected-share percentages never participate. If half of corporate banking is affected, as the label on the chart states, the segment’s value should be roughly half of what appears. Apply every affected-share label as a multiplier and the sector total falls to roughly 175 to 270 billion USD, less than half the headline.

Three explanations are possible. The affected shares may already be embedded inside the impact hypotheses, in which case the labels are redundant and the hypotheses mean something different from what a reader would assume. The labels may be descriptive color with no computational role, in which case they should not sit in a column formatted like a model input. Or a step of the model is missing from the page. McKinsey does not say which. None is disclosed.

To put it bluntly: a headline figure whose published inputs produce a different figure is a graphic, and nobody should commit a nine-figure budget on the strength of a graphic. For a number that CISOs and CFOs are being asked to act on, the reproducibility failure alone should pause the conversation until McKinsey publishes the model.

Quantum computing gets credit for AI’s work

The second problem is a five-word footnote. Slide 23, the qualitative companion to the value chart, marks its impact levers with a note stating that they measure the combined impact of quantum computing and AI. The quantitative chart beside it attributes the entire 400 to 600 billion to quantum computing in its headline.

(I have spent three decades reading vendor and consultant fine print. Rarely have I seen five words asked to carry this much weight.)

Read the use cases with that footnote in mind. Corporate banking value comes partly from improving default risk prediction for real-time product decisions. Retail banking value comes from faster credit scoring at scale and from personalized finance recommendations. Asset and wealth management value includes personalized pricing and recommendations that the page itself ties to AI. Every one of those is a workload banks run today on classical machine learning. Quantum machine learning research exists, and I follow it closely, but no peer-reviewed result establishes a scalable quantum advantage for any of these tasks. McKinsey provides no allocation of the projected value between quantum computing, AI, and ordinary technology modernization. The quantum-specific share of the 400 to 600 billion is therefore unknown, and from the report alone, unknowable.

McKinsey concedes the entanglement, so to speak, in its industry-overview caveat about overlap with generative AI and totals that are not fully additive. The caveat is accurate. The finance headline ignores it.

Compared with what

Footnote 1 on the value chart reads: “Savings are vs situation today, not vs future technology alternatives.” That is the only statement in the finance chapter about the classical comparator, and it covers only the savings lines. On this chart, a single segment carries the savings label: operations and finance, worth 1 to 2 billion USD. The other six segments, more than 99 percent of the total, are labeled as revenue increases, and for revenue increases McKinsey discloses no comparator at all.

Both halves of that are problems. Where the baseline is stated, it is frozen: a 2035 benefit measured against 2026 technology, with nine years of GPU generations, AI surrogate models, and variance-reduction advances assumed away. I described this as the moving classical baseline in my finance analysis, and it erodes quadratic quantum speedups faster than any other factor; a modest advantage over today’s Monte Carlo stack can be no advantage at all over the 2035 stack. Where the baseline is unstated, which is nearly everywhere, the reader cannot even evaluate the claim. An impact percentage with no counterfactual is an opinion with a decimal point.

What is being valued in risk and cybersecurity

The largest single line item is risk and cybersecurity at 95 to 155 billion USD, and it contains the model’s strangest arithmetic. The impact hypothesis is a 3 to 5 percent increase in revenue. The baseline it multiplies is 3,100 billion USD of total spend on risk and cybersecurity. That is an increase in revenue computed as a percentage of cost. Whose revenue? A bank does not book revenue on its own security spending; its vendors do. McKinsey does not say.

A second confusion comes from the qualitative page, where McKinsey lists post-quantum cryptography and quantum key distribution as a new business opportunity for this segment, on the grounds that they will be mandatory for securing transactions. Mandatory is the right word and the wrong category. PQC migration is a defensive expenditure that the quantum threat imposes on every financial institution, and regulators, insurers, and counterparties have already put dates on it. For a bank, PQC is cost, compliance, and avoided loss. To the extent it becomes revenue anywhere, it is revenue for security vendors, booked against bank budgets. Folding a mandated defensive migration into a chart about the value of quantum use cases is like counting flood barriers as value created by sea level rise. On the quantitative side, McKinsey attributes the 95 to 155 billion to fraud detection accuracy and the replacement of classical risk models, and fraud detection is once again a workload where classical machine learning is both the incumbent and the benchmark.

The biggest number in McKinsey’s finance chapter could therefore contain improved fraud models, vendor revenue, avoided breach losses, compliance spending, quantum-driven risk simulation, or some blend of all five. Nothing on either page distinguishes revenue from savings from avoided loss from mandated expenditure. At its largest line item, McKinsey does not say what is being counted.

Quantum money as a 2035 revenue line

Payments receives 50 to 75 billion USD, and its headline mechanism, per the qualitative description, involves replacing current blockchain technology and eliminating fraud and anti-money-laundering checks through quantum money.

Stephen Wiesner conceived quantum money around 1970 and could not get the paper published until 1983. In fairness to the concept, it is no longer purely theoretical. Researchers demonstrated quantum-digital payments over metropolitan fiber in Vienna in 2023, and token schemes now exist that avoid the long-lived quantum memory Wiesner’s original design required. I find that research elegant.

None of it supports a 2035 revenue forecast. Between a physics demonstration and tens of billions of dollars of payments value sit an infrastructure build-out, a regulatory framework for quantum-secured settlement, standards work, interoperability, bank adoption cycles, and unit economics. None of these exists even in draft. In nine years.

The anti-money-laundering claim is worse than optimistic; it is mis-specified. Quantum unforgeability addresses counterfeiting and double-spending. It does nothing about what AML programs actually police: identity fraud, account takeover, sanctions screening, source-of-funds verification, mule networks. A bank running quantum money in 2035 would still run its full AML stack, because criminals do not launder money by counterfeiting tokens; they launder it by moving real value through real accounts. The value driver McKinsey names for this segment would not deliver the value even if the technology arrived on schedule.

The resource estimates McKinsey never engages

Nothing above requires a physics argument; the disclosure problems are visible from the slides alone. The physics makes everything worse.

Two peer-reviewed papers define what quantum derivative pricing would demand from hardware, and both come from researchers with every incentive to make quantum finance work. Chakrabarti, Krishnakumar, Mazzola, Stamatopoulos, Woerner, and Zeng, a Goldman Sachs and IBM team writing in Quantum in 2021, produced the first complete resource estimate for useful quantum pricing of autocallable and TARF derivatives: roughly 8,000 logical qubits running a T-depth of 54 million, with the program executing on the order of a second to beat classical pricing. The authors present the requirements as beyond current systems and offer them, in their own hopeful framing, as a roadmap for algorithm and hardware designers.

Three years later, Stamatopoulos and Zeng improved on their own numbers, using quantum signal processing to encode derivative payoffs directly into quantum amplitudes. The technique cut T-gates by roughly 16 times, logical qubits by roughly 4 times, and the required logical clock rate by roughly 5 times against their updated arithmetic baseline. Their bottom line: quantum advantage in derivative pricing needs about 4,700 logical qubits on a device executing a billion T-gates at a rate of 45 MHz.

Now set those figures beside McKinsey’s own technology chapter. On slide 57 of the same Monitor, McKinsey surveys published logical-qubit roadmaps, finds that most players target roughly 100 to 200 logical qubits by around 2030, and cautions that the goals are neither comparable across modalities nor dependable predictors of delivery. IBM’s Starling system, the machine behind the 2029 fault-tolerance milestone the Monitor cites elsewhere, targets 200 logical qubits and 100 million operations; its successor Blue Jay targets 2,000 logical qubits and a billion operations sometime after 2033. Hold the Goldman Sachs specification against that: a single competitive pricing run needs ten times more T-gates than the total operation count IBM plans for its first fault-tolerant machine, on more than twenty times the logical qubits, at sustained gate rates no vendor publishes at all. IonQ’s roadmap, the 80,000-logical-qubit outlier McKinsey features, is aspirational by McKinsey’s own caution and, like every roadmap on that page, is silent on sustained logical T-gate throughput, which is the binding constraint here.

I want to be precise about what this shows, because precision is the point of this article. The Goldman Sachs papers are optimists’ documents. They do not prove quantum finance impossible by 2035, and the 2021-to-2024 improvements are evidence of real algorithmic progress. Ryan Babbush and colleagues at Google argued on general grounds in 2021 that quadratic speedups are unlikely to yield error-corrected quantum advantage at all, and I have made the mathematical case against quantum finance at length. My charge against McKinsey is narrower and harder to escape: a report that projects 400 to 600 billion USD from these applications by 2035, while its own hardware chapter shows one to two orders of magnitude fewer logical qubits than the published requirements on that timeline, owed its readers a bridge between the two chapters. No bridge exists. The finance chapter’s sources are expert interviews and a market-data feed. The only empirical demonstration McKinsey cites for finance is HSBC and IBM’s bond-trading experiment, a preprint result whose own authors describe it as dataset-specific and decline to generalize. That is the entire evidentiary basis for the largest sector-level quantum value claim in the report.

Where the Monitor is on firmer ground

McKinsey’s chemistry estimate of 450 to 800 billion USD and its pharmaceutical estimate of 80 to 400 billion point at the sectors where, in my own research, the case for quantum advantage is genuine. My Quantum Utility Map series reached that conclusion from the opposite direction, by cataloging peer-reviewed resource estimates: molecular simulation, catalysis, and battery chemistry involve exponential classical hardness that quantum computers evade, the required resources are falling fast, and the honest ledger for chemistry supports real, if narrower than advertised, advantage.

Consistency requires the next sentence too. The chemistry and pharma dollar figures come out of the same top-down machinery as the finance figure: expert-interview percentages, combined QC-and-AI levers, opaque baselines. I find the direction credible because the underlying science supports it. The dollar ranges are as unvalidated as the finance range. What separates the sectors is that in chemistry the feasibility question has peer-reviewed support, while in finance the same literature cuts against the claim on the stated timeline. A methodology I reject in finance does not become sound in chemistry because I happen to like the conclusion better.

What financial institutions should do with this

My recommendations have not changed since Why Quantum Won’t Save Wall Street, and nothing in the Monitor gives me a reason to revise them.

Put the quantum budget into post-quantum cryptography migration first. Its return arrives as risk reduction, compliance, resilience, and avoided loss rather than as new revenue, which is why it will never headline a value-at-stake chart and why it matters anyway. The harvest-now-decrypt-later threat operates today, regulators, insurers, and counterparties have already set the schedule, and migration takes years.

Keep a lean quantum research capability. Algorithmic surprises happen, the 2021-to-2024 resource reductions show real movement, and the institution with expertise in place will exploit a breakthrough years before the one that starts hiring after the breakthrough is published. Track the three signals I laid out: a super-polynomial speedup for a financial problem class, logical clock rate demonstrations above 1 MHz, and pricing estimates below 1,000 logical qubits.

And build the analytical capability to understand quantum computing’s effect on the industries it will transform. A bank that can read the Quantum Utility Ladder prices quantum-exposed sectors, evaluates quantum companies, and advises clients better than one working from a consultant’s percentage. That edge is available now and costs a fraction of a quantum lab.

What no finance leader should do is walk the 600 billion figure into a board meeting as if someone measured something.

Five questions for McKinsey

Five questions remain open, all specific, and all answerable by the team that built the model.

1. Are the affected-share percentages already embedded in the impact hypotheses, and if so, how, and what do the hypotheses then represent?
2. What share of each segment’s value is attributable to quantum computing rather than to AI or ordinary modernization?
3. Does the 95 to 155 billion USD risk and cybersecurity line include PQC or QKD, and is that value revenue, savings, avoided loss, or mandated expenditure, and whose?
4. What logical-qubit counts, sustained logical gate rates, and error-correction assumptions support a 2035 finance timeline, and how do they reconcile with slide 57 of the same report?
5. Can the Panorama dataset and the finance model be shared in enough detail for independent reproduction?

If McKinsey answers, I will publish the answers and update this analysis. [EDITOR: Recommend sending these five questions to the Monitor team for comment before publication; adjust this line based on the outcome.]

A percentage is not a forecast

There is a version of the Monitor’s finance chapter that would have earned its headline. It would have separated quantum value from AI value, stated its classical comparator, shown arithmetic that reproduces, and confronted the resource-estimate literature, then defended whatever number remained, probably a much smaller one. McKinsey has the technical bench to write that chapter. It published this one instead.

My Quantum Utility Map series asked what quantum computers will be able to do, sector by sector, against the published evidence. The answer for finance was uncomfortable for the industry and for parts of my own business: the advantage is structurally weak on any timeline that matters for planning. The Monitor asked a different question, what the value would be if the applications worked, and then printed the answer without the conditional. McKinsey keeps the caveats in the footnotes and the conditional out of the headline. And a 600 billion USD figure stripped of its conditional is how a bank ends up funding the wrong quantum program.

Do the multiplication before you carry the number into a boardroom. It took me an afternoon, and the model is not on the page.