Every Quantum Salary Guide I’ve Seen Is Fake. Here’s How I Proved It.

Introduction

Somewhere in a recruitment agency’s office, someone opened a spreadsheet, typed a US salary range for “Quantum Hardware Engineer – Junior,” and then multiplied it by 0.79 for the UK column, 0.68 for France, 1.15 for the Bay Area, and 0.86 for Boulder. They repeated this for every role and seniority level, converted to local currencies using exchange rates they found on Google, and exported the result as a glossy PDF titled “The Ultimate 2026 Quantum Salary Benchmarking Report” (or something similar, I am not referring here to any particular salary guide).

I know this because I reverse-engineered the numbers. And over the past several months, I’ve done this with five separate quantum salary guides from five different agencies. Every single one was generated from a formula. Not one showed evidence of being derived from real market data.

This would be merely annoying if salary guides were just marketing collateral that nobody took seriously. They’re not. Companies use salary benchmarks to build hiring budgets, price professional services, decide where to locate engineering teams, and make the business case for headcount in board presentations. Candidates use them to evaluate offers, choose specializations, and decide whether to relocate across countries. Fabricating this data and presenting it as market intelligence is not a minor offense. People’s careers and companies’ strategies are downstream of these numbers.

I’m publishing the methodology here so you can run the same tests yourself.

The Market Is Real. The Data Is Not.

The quantum industry’s talent problem is genuine. As I covered in my guide to career opportunities in quantum technologies, the field is expanding well beyond PhD physicists into software engineering, systems integration, commercial roles, cybersecurity, and project management. A 2022 McKinsey analysis found only one qualified candidate for every three open quantum positions. By 2026, with NIST’s PQC standards finalized and regulatory deadlines accelerating migration timelines, that ratio has likely worsened.

Where there’s a talent shortage, recruitment agencies follow. And where recruitment agencies go, salary guides appear. The guides serve an obvious purpose: a well-designed PDF positioned as “market intelligence” captures email addresses from hiring managers and candidates, positioning the agency as a domain authority. As a lead generation strategy, that’s perfectly legitimate. The problem is that the data inside these guides is fabricated, and fabricated data presented as market intelligence is used to make real decisions about real people’s compensation and careers.

I started collecting these guides after noticing that several agencies were entering the quantum recruitment space and publishing (and sending me) salary benchmarks as their calling card. What I expected to find was varying quality, with some agencies drawing on genuine placement data and others padding thin datasets with estimates. What I actually found was worse: every guide I examined was generated from a single baseline with multipliers. If even that. The pattern was identical across all five, despite coming from different agencies in different countries.

The Forensic Method: Five Tests That Catch a Fake

The technique is straightforward. You need basic arithmetic and about 10 minutes with a spreadsheet. I encourage anyone who receives a quantum salary guide to try these tests before using the data for any decision.

Test 1: Check Whether Different Currencies Carry the Same Numbers

Real salary data collected independently in two cities would produce different numbers because the cities have different costs of living, different labor market dynamics, different employer mixes, and, crucially, different currencies.

In one guide I examined, which covered hardware, software, algorithms, and commercial roles across multiple European cities, the GBP figures for one major city were numerically identical to the EUR figures for another across every single level and function. Junior hardware: £45–53k and €45–53k. Mid software: £68–79k and €68–79k. Senior algorithms: £98–115k and €98–115k. The pattern held for all four role categories and all three seniority levels without a single exception. Someone built one “Europe” column and swapped the currency symbol.

A second guide committed the same error with two Scandinavian currencies. The high-end salary figures were identical across multiple roles — the same number appeared in both currency columns for Cryptographer, Security Architect, and Firmware Engineer. Since the two currencies in question trade at approximately 1:1.4, these numbers are mutually exclusive. They cannot both be correct.

Test 2: Compute the Ratio Between Any Two Cities Across All Roles

If an agency collected real data, the ratio between two cities’ salaries would vary by role because each role draws from a different talent pool with different competitive dynamics and supply constraints. The premium a city commands for quantum error correction specialists would differ from the premium for junior photonics engineers because the two labor markets have different structures.

In one guide, the ratio between two US cities was 1.131 at junior, 1.132 at mid-level, and 1.133 at senior for hardware roles. A constant multiplier stable to three decimal places across seniority bands. Software roles showed a similar pattern. I checked every role category in the guide; the city-to-city ratio never deviated by more than half a percentage point.

Real salary distributions do not produce ratios stable to the thousandth across different seniority levels and different talent pools. Compensation dynamics at the junior level are fundamentally different from those at the principal level because senior quantum specialists have more geographic leverage and a thinner competitive set.

Test 3: Look for Arithmetic Progressions Across Roles

When real companies set compensation for different functions, those ranges reflect distinct market conditions for each talent pool. Hardware engineering draws from experimental physics and electrical engineering candidates. Software roles draw from a larger classical computing pool. Algorithms research draws from mathematics and theoretical CS. These talent pools have different sizes, different competing employers, and different geographic distributions. You would not expect the salary progression from one function to the next to follow a neat arithmetic step.

One guide’s APAC data showed exactly that. In one country, hardware junior topped at a certain figure, software was exactly 20,000 higher in local currency, and algorithms was exactly 20,000 higher again — a perfect staircase. With a round number. A second APAC market showed the same pattern at +5,000 steps. A third at +500,000 in its local currency. Three different countries, three different currencies, the same arithmetic staircase in each.

Test 4: Compute the Ratio Between Any Two Roles Across All Countries

This is the most damning test. If data were collected independently per country, the relationship between roles would vary because each country’s labor market prices different skills differently.

In one guide that covered 16 countries and nine role categories, the ratio of one technical role to another at the low end of the range was: 1.218 in the US, 1.218 in the UK, 1.220 in France, 1.221 in the Netherlands, 1.224 in Finland. I checked the second role pair: 1.408, 1.411, 1.396, 1.396, 1.400. Across all countries, both ratios were constant to two decimal places.

There is no plausible real-world data collection process that produces inter-role ratios this stable across twenty or so countries with different labor laws, different quantum ecosystem maturity, different employer concentrations, and different competing-industry salary structures. The methodology is transparent: they established a US baseline per role, applied a fixed country multiplier to every role, and rounded the results. Two dimensions, one spreadsheet.

Test 5: Check Whether Contractor Rates Are Independent or Derived

If an agency benchmarks permanent salaries and contractor day rates separately (as they should, since these are different markets with different pricing dynamics), the relationship between the two would be inconsistent across roles and countries. Some roles are easier to fill on contract, some countries have different contractor markups due to tax and employment law, and some specialisms have thicker or thinner freelance pools.

In one guide, dividing the permanent salary by 220 working days produced a baseline from which the contractor day rate could be derived with a consistent markup factor across all roles and all countries. The contractor data wasn’t independently collected; it was calculated from the permanent salary table using a single formula.

A Spectrum of Fabrication, Not Just One Pattern

The five guides I examined weren’t all equally crude. They fell along a spectrum.

At the worst end sat guides with the currency duplication errors such as identical numbers in GBP and EUR, or identical numbers in two Scandinavian currencies. These are guides where the fabrication is visible to anyone who looks at two adjacent columns.

In the middle sat guides that avoided the most obvious currency mistakes but still showed constant cross-role and cross-country ratios. Some of these were more carefully constructed: one split Europe into “Tier 1” and “Tier 2” regions rather than listing individual countries, which reduced the number of cells that could betray the formula. The Tier 2 figures looked like Tier 1 multiplied by 0.65. That’s a more sophisticated version of the same approach, but the underlying method was identical.

I also found a separate category of salary content that works differently: job boards publishing long SEO blog posts with salary ranges pulled from public aggregators like Glassdoor and ZipRecruiter. These aren’t gated PDF benchmarking reports; they’re content marketing with ranges so broad ($80k–$300k+) that they tell you almost nothing. One such site appeared to have quotes from named individuals at major quantum companies — I could not verify whether those people exist or consented to being quoted. These job board guides aren’t fabricating granular data with the same false precision as the agency PDFs, but they contribute to the same problem: a flood of authoritative-looking quantum salary information that isn’t grounded in actual placement data.

What the Agencies Say When Confronted

When I raised these issues directly with agencies whose reports showed these patterns, I got three categories of response. Some agencies blocked me and cut off communication entirely after I pointed out the constant ratios. Others doubled down, claiming their data was “aggregated from hundreds of placements across the globe” or drawn from “proprietary internal datasets” accumulated over years of quantum recruiting. A third group simply didn’t respond.

The math tells a different story. If you had genuinely collected hundreds of real salary data points across twenty countries, three dozen cities, ten roles, multiple specializations, and multiple seniority levels, you would see noise. Real market data is messy. Some countries would show Role A paying more than Role B, contrary to the global pattern, because local conditions differ. Some cities would break the expected rank order. The Netherlands might pay disproportionately well for certain roles because the QuTech cluster in Delft concentrates demand. Finland might show unusual patterns because Bluefors and IQM create localized competition for cryogenic and hardware talent. The complete absence of any such irregularity across any role, any country, any seniority level is itself the proof of fabrication.

Why This Matters: Real Decisions From Fake Data

Salary data isn’t trivia. It’s infrastructure. It sits underneath a chain of decisions that affect companies, candidates, and entire market segments. When that infrastructure is fabricated, every decision built on top of it is compromised.

How Companies Use Salary Data

A quantum startup raising a Series A needs to tell investors how many engineers the round will fund. That calculation starts with salary benchmarks. If the benchmarks are inflated by 15% because they were generated from a formula rather than collected from actual placements, the startup either hires fewer people than it could have, or it burns through its runway faster than projected. Either outcome damages the company.

Larger organizations use salary data to build the business case for new quantum initiatives. When a bank’s CTO proposes a quantum-safe migration team, the CFO asks what it will cost. The answer comes from salary benchmarks for PQC engineers, program managers, and cryptographic architects in the relevant geography. If those benchmarks are fiction, the budget is wrong from day one. The project either gets approved at an artificially high cost (and gets scrutinized more aggressively than it should), or it gets rejected as too expensive when a realistic budget might have gotten the green light.

Professional services firms, including mine, price engagements partly on the basis of what it costs to staff them. If I’m pricing a quantum systems integration project and I rely on a salary guide that overstates European hardware engineer compensation by 20%, I either eat the margin difference or I pass the inflated cost to the client, making quantum consulting look more expensive than it needs to be. In a market that’s still educating buyers about the value of quantum services, artificial cost inflation is the last thing we need.

Companies also use salary data to decide where to build. A quantum hardware company choosing between Delft, Munich, and Boulder for its engineering lab will factor in talent cost alongside talent availability. If the salary guide shows all three cities at roughly the same cost because the same European multiplier was applied to all of them, the company is making a location decision on false inputs. The real cost differences between these cities, driven by local employer density, tax regimes, and housing costs, are exactly the information a salary guide should provide, and exactly what a formula-generated guide erases.

How Candidates Use Salary Data

A mid-career cybersecurity professional considering a move into quantum security might use one of these guides to assess whether the transition makes financial sense. If the numbers are fabricated, that person is making a career decision on fiction. They might pass up a genuinely well-compensated opportunity because the guide made it look average, or they might accept a below-market offer because the guide suggested it was competitive.

Candidates also use salary data to choose specializations. Someone deciding between deepening their expertise in PQC migration versus QKD photonics engineering will reasonably consider which path pays better and in which geographies. If both roles show identical compensation across all markets because the guide applied the same multiplier to both, the candidate gets no useful signal. The real differences in supply-demand dynamics between these specialisms, which should inform career strategy, are invisible.

Relocation decisions are perhaps the most consequential. A quantum engineer in Helsinki evaluating an offer in Zurich needs to know whether the salary premium justifies the higher cost of living. If the guide’s Helsinki and Zurich numbers were both derived from a generic “Tier 1 Europe” multiplier applied to a US base, the comparison is meaningless. The engineer might relocate for a premium that doesn’t exist, or might stay put and miss a genuine opportunity.

The Compounding Effect

None of these decisions happen in isolation. A company that overpays because of inflated benchmarks eventually corrects by freezing salaries or reducing headcount. A candidate who accepted below-market because of understated benchmarks discovers the gap and leaves, creating turnover costs. A startup that located in the wrong city based on false cost comparisons spends years paying the premium before it can afford to move. These aren’t hypothetical scenarios; they’re the predictable consequences of building decisions on bad data.

For the PQC migration market specifically, the timing is terrible. As I’ve argued throughout this site, the urgency of post-quantum migration is driven by regulatory deadlines that are already set, not by distant Q-Day predictions. Organizations need to hire PQC engineers, migration program managers, and cryptographic architects now. If the salary data they’re using to budget those hires is fiction, the migration gets delayed, and the HNDL exposure window extends. Every month of delay is a month of additional risk.

The Credibility Tax

There’s also a cost that’s harder to quantify but no less real. One of the things I spend significant effort on at PostQuantum.com is fighting Q-FUD — exaggerated quantum claims designed to sell products and services. Fake salary guides are Q-FUD for the talent market: authoritative packaging over invented content, designed to sell recruitment services. When a CISO or CTO picks up one of these guides and later discovers the data is fabricated, the damage extends beyond the agency’s reputation. It erodes confidence in the quantum industry’s maturity as a whole. An industry that can’t produce honest salary data doesn’t look ready for enterprise-grade partnerships.

I want to be direct about this: publishing fabricated salary data that you know will be used for hiring budgets, compensation negotiations, location decisions, and career choices is unethical. It is not a gray area. The agencies producing these guides know that their placement volume cannot support the granularity they claim. They publish anyway because the commercial upside of looking authoritative outweighs, in their calculation, the harm done to companies and candidates who trust the numbers. That calculation is wrong, and the agencies that make it should not be trusted with the recruiting relationships they’re seeking to build.

Why No Agency Can Produce a Real Global Quantum Salary Guide (Yet)

The underlying problem is structural, not just ethical. The quantum talent market may be too small, too fragmented, and too young for any single agency to produce a credible global salary guide with the granularity these reports claim.

The entire quantum computing workforce worldwide is estimated at perhaps 30,000–50,000 people, depending on where you draw the boundaries. PQC is a subset of that. QKD photonics engineering is a smaller subset still. Now slice that population by role (hardware, software, algorithms, commercial, security), then by seniority (junior, mid, senior, lead), then by geography (twenty countries in one guide I reviewed), then by employer type (startup vs. big tech vs. national lab vs. consultancy). You’re left with cells containing a handful of data points at best, and zero at worst.

The agencies I examined fall into two categories. The first is generalist recruitment firms, for example life sciences staffing companies, broad tech recruiters, that bolted quantum onto their portfolio as a growth vertical. Their core business is pharma or enterprise IT; quantum is a new desk. The second is newer, quantum-branded agencies that position themselves as sector specialists but have only operated in quantum for a few years with small teams.

Neither category appears to have sufficient placement volume to produce statistically meaningful salary data across the geographic and role coverage their guides claim. An agency with a quantum practice of 5 recruiters operating for two years cannot plausibly have completed enough placements to benchmark nine roles across three seniority levels across 16 countries. The volume doesn’t exist. Rather than acknowledging this limitation, they fill the gap with a formula.

That’s the core ethical failure. It would be entirely respectable to publish a guide that said: “Based on 40 placements in the US and UK, with estimates for other markets.” The dishonesty is in presenting formula-generated numbers as market-benchmarked data, defending the fiction when challenged, and knowing that companies and candidates are making consequential decisions based on it.

My Position

I run Applied Quantum, a quantum professional services and systems integration firm, and I sit on boards of several other quantum companies. Between these organizations and PostQuantum.com’s network, I interact regularly with recruitment agencies seeking to place quantum talent or partner on hiring engagements. My position is simple: if I catch an agency publishing fabricated salary data, I will not work with them or recommend them to anyone.

That’s not because I expect perfection. Salary data is inherently imprecise, ranges shift, and thin markets produce noisy estimates. If an agency published a guide that was approximately right but disclosed its limitations honestly, I would respect the effort. The issue is presenting invented data as market intelligence and defending the invention when the arithmetic exposes it.

How to Evaluate Future Salary Guides

I’m aware that by publishing this analysis, I’m giving agencies a roadmap to fake more convincingly. The next generation of guides will probably add noise to the ratios, break the arithmetic progressions, and avoid the most obvious currency duplication. So here are checks that remain useful even after agencies improve their spreadsheet formulas.

Ask about methodology. A credible salary guide should disclose its data source: how many data points, from what geographies, collected over what time period, and whether the data represents completed placements, accepted offers, advertised salaries, or candidate expectations. These are four very different things. If the guide has no methodology section, treat the data as marketing material, not market intelligence.

Check whether the guide differentiates between employer types. A quantum hardware role at Google pays differently from the same title at a 20-person startup. A PQC consultant at a Big 4 firm earns differently from one at a boutique. If the guide lumps all employers together into one range, the data is too coarse to be useful even if it’s real.

Look for asymmetries and surprises. Genuine data contains outliers. Maybe one European country pays disproportionately well for cryogenic engineers because a specific employer cluster drives up local competition. Maybe PQC salaries in the UK run higher than continental Europe because NCSC’s aggressive migration timeline created early demand. If every country’s data follows the same smooth progression with no outliers and no surprises, the data was generated, not collected.

Cross-reference with public sources. Job boards, government salary databases (like the US Bureau of Labor Statistics), and platforms like Glassdoor and Levels.fyi carry compensation data for related roles — cybersecurity architects, photonics engineers, software engineers at known quantum companies. These won’t give you “quantum salary” data directly, but they establish floors and ceilings that a credible guide should be consistent with. If a guide claims senior PQC engineers in a tax-free jurisdiction earn less than conventional cybersecurity professionals in the same market, the numbers are suspect.

Check the agency’s quantum track record. How many quantum placements has the agency actually completed? How long have they operated in quantum specifically? Do they have published case studies from quantum companies? An agency that entered quantum two years ago and covers twenty countries across ten specialisms is almost certainly extrapolating beyond its data.

Be especially skeptical of broad geographic coverage. Producing accurate salary data for the US quantum market alone would require substantial placement volume. Covering the US plus seven European markets plus a number of APAC and GCC markets in a single guide requires either an enormous global operation or a formula. Most quantum recruitment agencies are small teams.

What Would a Real Quantum Salary Guide Look Like?

I haven’t found one yet. But I would love to see one – so if you are aware of a good one, send them my way. The honest answer is that the market may not yet be large enough for any single agency to produce one. A useful approach would be for an industry consortium, perhaps something like QED-C or a collaboration between multiple agencies, quantum companies, and industry bodies, to pool anonymized compensation data across employers. That’s how mature industries produce salary benchmarks: through aggregation at a scale no single recruiter can achieve.

Until something like that exists for quantum, every single-agency salary guide should be treated with the skepticism it deserves. Run the tests. If the ratios are constant, the currencies are copied, and the progressions are arithmetic, you’re not looking at market data. You’re looking at a spreadsheet someone built in an afternoon to capture your email address.

The quantum industry deserves better data than this. Until it gets it, don’t trust the guides.

And if you catch any agency doing this, don’t work with them. Ever. If they’re willing to fabricate data to win your business, they’ll cut corners on everything else in the relationship – candidate vetting, reference checks, market advice. Dishonesty at the front door doesn’t get more honest further inside.