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A quantum-computing startup is financially viable only when three conditions line up: its technology is progressing toward a differentiated, reliable product; identifiable customers are willing and able to pay for useful work; and realistic revenue, margins, and financing can fund the time and cost needed to reach repeatable delivery. A large market forecast or an impressive technical milestone is not enough. Test the company with evidence from contracts, customer deployments, delivery capacity, cash balances, financing terms, and independently measurable milestones.
What financial viability means for a quantum startup
For an early quantum company, viability is not the same as having a high valuation, winning a grant, raising a large round, or appearing in a market forecast. It means the business can finance its development and commercialization path while producing a product that customers repeatedly buy at economics that can eventually support the company.
The assessment has to connect technology, customers, and finance. A machine can perform well in a laboratory yet be too unreliable or expensive for a customer workflow. A startup can report a large booking while having little recognized revenue and substantial future delivery obligations. A company can also have enough cash for its current burn but not enough to reach the next technical milestone or survive a delayed procurement cycle.
The European Commission’s Quantum Grand Challenge financial screening is a useful checklist. It examines products or services, the business model, clients, competitive advantage, the business plan, corporate structure, governance, shareholders, and funding. Later-stage proposals are expected to demonstrate commercial potential, market-ready scalability, technical progress, and real-world applications. The program’s official language is explicit: “Projects should develop detailed financial viability assessments, identifying potential revenue models, commercialization pathways, and criteria for unlocking private investment during subsequent phases.” These criteria are a strong diligence starting point, not a guarantee that any company will succeed.
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Start with the three linked tests
1. Is the technology becoming a product?
Identify what the company can deliver today: hardware, cloud access, software, an application, professional services, or a combination. Then separate demonstrated capability from roadmap promises. Each milestone should have a measurable definition, an independent way to verify it, and a clear connection to a customer problem.
Useful evidence includes system availability, workload performance, error rates, coherence or error-correction progress where relevant, reproducibility, integration time, support requirements, and total cost for the intended workload. A headline hardware metric matters less than whether a customer can run a useful job reliably, obtain an economically meaningful result, and put that result into an existing process.
2. Will identifiable customers pay and keep using it?
Map every claimed customer relationship to its commercial stage. A conversation, unpaid research collaboration, funded proof of concept, paid pilot, production deployment, renewal, and expansion are different events. Ask who owns the budget, which workflow changes, what baseline or classical alternative is being measured, and what outcome justifies renewal.
Measure how much of each engagement is repeatable product revenue and how much is custom consulting or engineering. A pilot that requires a new bespoke integration for every customer may demonstrate interest without establishing a scalable business. Stronger evidence is a deployment that can be repeated with less implementation work, produces a measurable customer benefit, and has a contractual path to continued use.
3. Can revenue and financing cover the path to scale?
Build a company-specific model rather than applying a sector growth rate to a guessed market share. Forecast customer counts, contract values, utilization, procurement timing, renewals, delivery capacity, gross margin, working capital, and collections. Keep recognized revenue, bookings, grants, awards, pipeline, and remaining performance obligations in separate lines.
Then compare cash and committed financing with the time required to reach the next risk-reducing milestones. Include the possibility of a delayed roadmap, slower procurement, lower pilot conversion, a concentrated customer loss, weaker margins, or a financing round that closes later and on worse terms.
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Use market forecasts as context, not proof
Sector estimates can test whether a startup’s claimed market and growth assumptions are plausible, but they do not establish obtainable revenue, pricing power, delivery reliability, or margins. The major 2026 estimates use different definitions and methods, so they should not be combined into one audited time series.
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|---|---|---|
| QED-C, 2026 market forecast | USD 1.4 billion worldwide quantum-computing market in 2025; more than USD 3 billion projected by 2028; approximately 30% annual growth | Useful for testing a segment thesis. It is not a forecast of one startup’s obtainable revenue. |
| McKinsey & Company, 28 April 2026 | More than USD 1 billion in worldwide quantum-computing company revenue in 2025; up to USD 4.4 billion in 2028 | Based on McKinsey’s definitions and analysis. The report examined 162 companies in detail from a universe of more than 300 and says its coverage is not exhaustive. |
| ESMA, 13 May 2026 | USD 650–750 million in worldwide quantum-computing-company revenue in 2024; expected revenue above USD 1 billion by the end of 2025 | Retain the stated year and range. ESMA also notes that referenced funding datasets differ. |
QED-C says application focus is narrowing toward simulation use cases, on-premise systems are projected to become the largest segment by 2028, and partnerships are supporting commercialization. It also identifies continued dependence on government funding and possible investment contraction as risks. McKinsey reports that private companies account for 72% of quantum-computing use in its analysis, that one-third of a selected group of large companies allocated more than USD 10 million to quantum initiatives in 2025, and that 7% allocated more than USD 50 million. Those figures describe market activity, not a representative startup’s unit economics.
ESMA estimates that one referenced dataset put venture funding to quantum-technology companies at approximately USD 2.6 billion in 2024. It describes stronger public-funding reliance in Europe and a larger private and corporate funding role in the United States. Treat geography and funding source as part of the risk analysis rather than assuming that capital will be equally available everywhere.
Measure customer evidence by commercial stage
The OECD’s 2026 business-readiness paper characterizes quantum computing as still in a “pre-validation phase,” with several competing technology architectures. That does not mean there is no business activity; it means adoption and economics remain uncertain.
OECD reports a 2022 Capgemini survey of 800 large organizations in 12 countries. About 23% were already working on or planning applications in quantum computing, sensing, or communication. Among organizations pursuing quantum, close to 20% reported reaching an implementation phase, including research and development experiments, limited pilots or proofs of concept, or experimental results. OECD also cites a Boston Consulting Group tally of more than 100 active quantum proof-of-concept projects among Fortune 500 companies in 2024, representing about USD 300 million of cumulative investment.
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These figures show experimentation and interest, not sustained commercial deployment. For the startup under review, create a customer ledger with the following fields:
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- Named buyer, budget owner, and procurement route.
- Problem, current workflow, classical baseline, and measurable success metric.
- Commercial stage: discussion, collaboration, paid pilot, production, renewal, or expansion.
- Contract value, duration, cancellation rights, payment schedule, and implementation obligations.
- Internal resources the customer must provide and the startup must provide.
- Evidence that the work will recur after the initial project.
Pay special attention to whether the startup is selling a repeatable product or subsidizing customer-specific research. A high number of logos can conceal low conversion, unpaid access, or a concentration in one government program.
Translate the technical roadmap into business risk reduction
Define milestones that can be checked
A credible roadmap states what will be demonstrated, under what conditions, by what date, and how the result will be verified. “More powerful” is not a sufficient milestone. A useful definition could specify an error-correction capability, a reliability target, workload throughput, availability, integration time, or cost per useful result.
For every milestone, ask which commercial risk it removes. A coherence improvement that does not change the customer’s workload may have little near-term financial effect. Conversely, a less dramatic technical improvement that cuts deployment time or support cost may materially improve gross margin and adoption.
Check delivery, not just laboratory performance
Review fabrication and testing capacity, supplier dependencies, cloud or facility availability, security and regulatory requirements where applicable, customer support, and the people needed to install and operate the system. A roadmap that reaches a technical target but cannot be delivered, maintained, or integrated at customer sites is not a product plan.
Look for economic hypotheses
McKinsey reports that companies making faster progress combine technical experimentation with clear economic hypotheses and delivery roadmaps. Test whether management can state the expected customer value, price, cost to serve, sales cycle, and deployment burden for each major use case. If those assumptions are absent, the company may be optimizing a research program rather than building a financeable business.
Rebuild revenue from contracts and capacity
Separate every commercial metric
Recognized revenue is recorded under accounting rules. Bookings generally represent signed business that may be delivered later. Remaining performance obligations represent contracted work still to be performed. Grants and awards can be restricted, conditional, or tied to milestones. Pipeline is not contracted business. Keep these categories separate in the model and reconcile them to the financial statements.
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Review contract duration, renewal language, acceptance criteria, payment timing, cancellation rights, deferred revenue, receivables, customer concentration, and gross margin by offering. A one-time system sale can make a period look strong while recurring software or production-use revenue remains small.
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D-Wave’s reported first-half 2026 results show why metric definitions matter. For the six months ended 30 June 2026, it reported USD 5.9 million of revenue, down 67% from USD 18.1 million in the comparable 2025 period. The 2025 comparison included USD 13.7 million from the company’s first sale of an annealing quantum-computing system. D-Wave also reported USD 35.5 million of first-half 2026 bookings, including a USD 20 million system sale whose revenue was to be recognized in subsequent quarters, and USD 40.7 million of remaining performance obligations at 30 June 2026. First-half QCaaS revenue from production applications was USD 1.3 million.
This example is about one issuer and one reporting period; it is not a benchmark for every startup. It demonstrates that bookings, future obligations, recognized revenue, and production usage can move in different directions. Ask management to bridge each metric to cash collected and delivery obligations.
Compare cloud, on-premise, and hybrid delivery
Deployment architecture changes capital intensity, customer control, utilization, support burden, and the timing of cash receipts.
| Delivery model | Questions for diligence | Financial implications to model |
|---|---|---|
| Hosted cloud access | Who pays for infrastructure, how utilization is measured, what availability and data controls customers require, and whether workloads recur? | Potentially recurring usage revenue, but continuing cloud and support costs; utilization and service reliability drive margins. |
| On-premise system sale | Who funds procurement, installation, acceptance testing, maintenance, and upgrades? | Larger, lumpy bookings; fabrication, installation, warranty, and working-capital requirements can be substantial. |
| Hybrid or application layer | Which components are standardized, which are customized, and who owns integration with the customer’s existing stack? | Can broaden access, but professional-services burden and integration cost must be tracked per deployment. |
McKinsey reports that private firms are more likely to access hosted applications through cloud providers, while public entities are more likely to purchase on-premise hardware. QED-C projects on-premise systems to become the largest segment by 2028. These are market patterns, not substitutes for evidence from the startup’s target buyers.
Build a cash runway model that reaches milestones
Include the full cost base
- Research and development salaries, benefits, and specialized technical recruitment.
- Engineering, fabrication, testing, cryogenics or other facility requirements relevant to the architecture.
- Cloud, data-center, laboratory, equipment, insurance, and maintenance costs.
- Sales, marketing, customer integration, training, support, and travel.
- Security, compliance, export controls, regulatory work, and legal costs where relevant.
- Working capital caused by long procurement cycles, milestone billing, inventory, receivables, and warranty obligations.
Do not calculate runway by dividing current cash by the latest quarter’s burn without adjustment. A company approaching a hardware delivery, major hiring plan, facility expansion, or customer integration phase may have a materially different forward burn.
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Model base and downside cases
At minimum, stress a delayed technical milestone, a longer sales cycle, lower conversion of pilots, loss of the largest customer, lower gross margin, an installation delay, and a financing round that arrives later or at a lower valuation. Show the month in which cash falls below the level needed to operate and the milestone that must be reached before then.
Classify grants separately from unrestricted cash. Record eligibility conditions, matching requirements, reimbursement timing, intellectual-property provisions, and milestones that could delay payment or trigger repayment. A grant can extend runway without being available to fund every expense.
Test financing resilience
Review the cap table, liquidation preferences, debt covenants, warrant or option obligations, investor concentration, and the realistic sources of the next round. Ask whether a missed milestone would merely reduce valuation or make financing unavailable. ESMA’s regional funding observations and QED-C’s warning about investment contraction support treating capital availability as a core operating risk, not an afterthought.
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For each offering, calculate the cost and time required to acquire, deploy, serve, and renew a customer. Separate hardware gross margin from software, cloud, and services margin. Track engineering hours per implementation, support tickets, uptime commitments, replacement parts, warranty exposure, and the proportion of work performed by senior researchers.
Then test whether the next ten customers are economically different from the first two. If every deployment requires founder-level involvement, a new custom algorithm, or a unique facility configuration, forecast the resulting headcount and schedule rather than assuming scale. A startup may have valuable intellectual property while still lacking a repeatable delivery engine.
Examine intellectual-property ownership, open-source obligations, university licenses, key-person dependence, supplier concentration, and the right to use customer data. These issues can affect financing, gross margin, and the ability to deliver the promised roadmap.
Use a structured comparison when several startups are under consideration
Score companies only on evidence that is comparable and dated. Keep qualitative judgments separate from verified figures, and document the source and conditions for every technical or commercial claim.
| Evaluation axis | Evidence to compare |
|---|---|
| Use-case fit | Named buyer, measurable value, classical baseline, and workflow integration. |
| Technical maturity | Independently evidenced performance, reliability, error-correction progress where relevant, and roadmap completion. |
| Customer adoption | Paid pilots, production use, renewals, expansion, contract terms, and customer concentration. |
| Product repeatability | Standardized implementation, engineering hours per deployment, support burden, and share of custom work. |
| Go-to-market | Sales cycle, procurement route, channel or partnership dependence, and conversion from pilot to production. |
| Capital intensity | Fabrication, facilities, cloud, inventory, installation, and working-capital requirements. |
| Revenue quality | Recognized revenue, recurring production usage, bookings, grants, remaining obligations, gross margin, and cash collection. |
| Concentration risk | Largest customers, investors, suppliers, government programs, and individual system sales. |
| Runway | Unrestricted cash and committed financing measured against base and downside time to the next milestone. |
Follow a repeatable diligence sequence
- Define the actual business. List what is sold today, the buyer, contract form, delivery method, current price, and roadmap-only features.
- Validate the use case. Identify the budget owner, existing workflow, baseline method, measurable outcome, and reason the quantum approach is preferred.
- Classify customer evidence. Label every relationship as discussion, collaboration, paid pilot, production, renewal, or expansion, and verify the associated cash and obligations.
- Check technical and delivery milestones. Request definitions, test conditions, independent verification, schedule, dependencies, and the customer risk each milestone removes.
- Rebuild revenue. Tie forecasts to customer counts, contract values, utilization, procurement timing, renewals, delivery capacity, and collections. Keep bookings, grants, pipeline, and recognized revenue separate.
- Rebuild costs and cash. Include development, fabrication, facilities, cloud, selling, integration, support, compliance, and working capital. Run base and downside cases.
- Test financing resilience. Review cash, committed funding, grant conditions, cap table, debt, dilution needs, investor concentration, and plausible follow-on sources.
- Set a conditional decision. Write the milestones that would improve the case, the misses that would invalidate it, and the evidence date for reassessment.
Red flags that require stronger evidence
- Market-size claims are presented as the startup’s forecast without a buyer, price, procurement path, or capacity calculation.
- Bookings, letters of intent, grants, or awards are described as recurring revenue.
- Technical milestones have no test conditions, independent verification, or customer outcome.
- A large customer list contains mostly unpaid collaborations or pilots with no renewal path.
- Revenue depends on one system sale, one government program, one investor, or one customer.
- Gross margin excludes founder, researcher, integration, cloud, facility, warranty, or support costs.
- Runway ends before the next financing-relevant technical or commercial milestone.
- Management cannot explain how implementation cost falls as deployments increase.
- Forecasts assume uninterrupted access to public funding or a favorable financing market.
Make the investment or planning decision conditional
End the assessment with an explicit decision rule rather than a vague view that the sector is promising. State what is already proven, what remains an assumption, and which evidence would change the conclusion.
- Supportable case: paid and repeatable customer use, measurable technical progress, credible delivery capacity, improving unit economics, and runway through the next milestones.
- Conditional case: credible technology and customer interest, but insufficient proof of repeatability, margin, financing access, or production use. Specify the exact milestones and date for reassessment.
- Unsupportable case: no verifiable path from technical work to a paying workflow, revenue quality that cannot be reconciled, or cash that cannot reach the next risk-reducing milestone without highly uncertain financing.
The OECD’s “pre-validation” characterization means uncertainty should be expected, not ignored. The practical question is whether this particular startup has enough technical, customer, and financial evidence to survive that uncertainty and convert progress into repeatable cash generation.
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