Quantum Policy for National Security: Get It Right

Quantum is starting to look a lot like AI did a few years ago: huge strategic upside, fuzzy policy boundaries, and a real risk that the United States wins the research race but loses the production race. The awkward part is that government decisions made now—export controls, investment screening, workforce programs—will shape what gets built, where it gets built, and who’s allowed to touch it.

For leaders in defense, intelligence, and the public sector, the quantum economy isn’t a science fair. It’s an enabling layer for cybersecurity, sensing, timing, communications, and eventually compute—the same mission areas where AI is already reshaping requirements, budgets, and operational concepts. If you work in “AI in Government & Public Sector,” quantum policy is adjacent whether you like it or not.

Here’s the stance I’ll take: most U.S. quantum policy risk today isn’t underinvestment—it’s miscalibrated rules that slow trusted collaboration while failing to block adversarial capture. That’s fixable, but only if we treat quantum like we’re learning from the last decade of AI in defense.

Quantum and AI follow the same national security pattern

Quantum’s national security value shows up in a familiar three-step pattern—one that mirrors how AI moved from labs into operational systems.

First, the dual-use pipeline is unavoidable. The same companies building quantum sensors for medical imaging, navigation, or infrastructure monitoring will also be asked about submarine detection, GPS-denied operations, and clandestine surveillance. AI went through this. Quantum is next.

Second, the procurement reality hits: prototypes are easy; production is hard. Quantum hardware requires specialized components, stable manufacturing processes, and a workforce that understands both physics and production engineering. This maps cleanly to what government learned the hard way with AI: training a model is one thing; deploying it in secure environments with a supply chain you trust is another.

Third, the policy surface area expands quickly—export rules, foreign investment reviews, talent controls, cloud access, and standards. If those knobs are set wrong, you can get the worst of both worlds: slowed domestic innovation and continued leakage to competitors.

Snippet-worthy truth: Quantum advantage in national security won’t come from a single “breakthrough machine.” It will come from scalable manufacturing, trusted supply chains, and fast policy cycles—exactly the boring parts everyone wants to skip.

Export controls: ambiguity helps nobody (and hurts allies)

Export controls are supposed to protect national security. When they’re written with ambiguous technical thresholds, they do something else too: they create a compliance fog that scares off legitimate collaboration and lets sophisticated actors slip through.

The U.S. began tightening quantum export controls in 2024, aiming at advanced quantum computing systems above specified capability thresholds (for example, qubit counts and coherence-related metrics). The intent makes sense—certain quantum capabilities could, over time, threaten cryptography, intelligence operations, and advanced modeling.

The practical failure mode: over-compliance and under-compliance at once

When rules are unclear, two bad behaviors appear simultaneously:

• Over-compliance: companies shut down collaboration with allied universities and labs because counsel can’t confidently interpret what’s controlled.
• Under-compliance: other firms take permissive interpretations, betting that enforcement won’t catch nuanced technical distinctions.

Neither outcome improves security. Over-compliance erodes allied R&D velocity. Under-compliance leaves gaps adversaries can exploit.

A better export-control design for quantum (and AI)

If you want export controls to work in fast-moving deep tech, the model needs three features:

1. Clear controlled categories that map to how systems are actually built and delivered (hardware, control electronics, enabling software, and critical components).
2. Scheduled revisions (for example, every 6–12 months) because capability thresholds age quickly.
3. Allied coordination so restrictions don’t just push development offshore or create a “free rider” problem.

This is where AI offers a lesson: the controls that bite are the ones aligned to real supply chains and enforceable checkpoints, not vaguely defined performance descriptions.

The personnel reporting trap

A particularly strange dynamic emerges when reporting requirements apply only to new hires with access to controlled systems, while existing personnel remain unreported unless voluntarily disclosed. That structure can incentivize organizations to:

• tighten hiring channels to reduce reporting burden, and
• ignore risk visibility for current access.

A more rational approach is full-scope disclosure of all personnel with access, paired with case-by-case mitigations (segmentation of access, auditing, supervised environments) rather than a blanket “stop hiring” effect.

Investment screening: treat allies like allies, and block adversaries early

Quantum startups don’t have the luxury of slow capital. Hardware timelines are long, burn rates are real, and fundraising delays can kill companies that are technically sound.

U.S. investment screening has two competing objectives:

• protect critical technology from adversarial capture, and
• keep the innovation engine funded (including from trusted foreign capital).

The problem is that review processes often treat many foreign dollars with similar suspicion, which can turn routine rounds into months-long uncertainty.

What the numbers say about friction

In 2024, 55% of transactions referred for review were investigated, but only one was canceled. That gap is telling: a lot of cost and delay, relatively few true “no-go” decisions.

Meanwhile, adversarial access doesn’t rely on a single obvious acquisition. It can come through:

• minority stakes below thresholds,
• licensing arrangements instead of equity,
• partnerships routed through third-country intermediaries.

A policy pattern that works: fast lanes and hard stops

The strongest recommendation from recent policy debate is differentiation:

• Fast-track reviews (e.g., 30 days) for trusted investors from allied blocs (Five Eyes, EU, Japan, South Korea) or previously vetted entities.
• Bright-line restrictions on Chinese-affiliated capital in quantum companies—implemented early, not after months of review.
• Time-bounded, targeted mitigation instead of open-ended compliance obligations that become permanent overhead.
• Expanded authority for greenfield facilities where the strategic asset is talent concentration and know-how, not just IP on paper.

This is exactly how AI in defense is evolving too: agencies are increasingly comfortable with trusted supplier frameworks, cleared environments, and tiered access—because the alternative is paralysis.

Federal and state action: build the ecosystem, remove the sandbags

The United States’ innovation model has always been a layered system: basic research, university talent, private commercialization, and mission-driven federal demand. Quantum fits that model, but there’s a policy gap: the National Quantum Initiative Act powered early momentum and then expired, leaving reauthorization and continuity as an open question.

At the same time, Congress is pushing quantum-related efforts that matter directly to defense and cybersecurity, including a post-quantum cryptography migration strategy and defense-focused quantum testbeds and sandboxes.

The lesson from industrial policy misfires

Programs that inject demand without fixing supply constraints often disappoint. If incentives pour money into a sector while permitting, environmental review, workforce rules, and facility timelines remain misaligned, you get delays and cost overruns.

The better approach—especially for quantum manufacturing—is straightforward:

• fund regional capacity where it already has anchors (labs, universities, industrial base),
• waive or streamline specific regulatory bottlenecks for strategically designated facilities,
• measure success in throughput: parts produced, yields achieved, technicians certified.

States are moving faster than Washington

Two state-level examples show what “ecosystem building” looks like:

• New Mexico is organizing national labs, universities, and private partners around a quantum initiative backed by federal collaboration, alongside targeted investment vehicles.
• Maryland is branding itself as a “Capital of Quantum,” aiming to attract more than $1 billion in investment tied to local research and industry.

For public sector leaders, these state strategies matter because they create natural landing zones for:

• defense pilot programs,
• federally funded test ranges and evaluation centers,
• secure manufacturing clusters.

Workforce: the real bottleneck that’s hiding in plain sight

Quantum doesn’t scale because you wrote a strategy memo. It scales because you can staff production lines, test labs, calibration benches, and field deployments.

The industry projects 250,000 quantum jobs by 2030, yet job postings were up only 4.4% year-over-year as of April 2025 and down 13.9% month-over-month. Those are not “moonshot boom” numbers. They’re early warning signs.

Two details should change how government programs are designed:

1. Over 50% of quantum jobs don’t require advanced degrees. The shortage is technicians, manufacturing specialists, and field engineers.
2. 50–70% of quantum-relevant U.S. PhDs are awarded to foreign nationals, predominantly from China. That’s a talent pipeline risk and a security challenge at the same time.

What a practical quantum workforce plan looks like

If you want quantum to become an American jobs story (and a defense readiness story), build multiple on-ramps:

• Community college and certificate tracks for vacuum systems, cryogenics safety, RF electronics, precision optics, and metrology.
• Advanced manufacturing apprenticeships aligned to quantum hardware assembly and test.
• Security-cleared technician pathways so programs don’t stall when systems move into classified environments.
• Cross-training with AI-enabled manufacturing (automation, computer vision inspection, predictive maintenance). This is where the campaign connection gets real: AI helps produce and sustain quantum systems at scale.

Snippet-worthy truth: The U.S. doesn’t have a “quantum talent” problem; it has a “quantum technician and production” problem.

What government leaders should do in the next 12 months

If you’re in government, defense, or a public-sector-adjacent contractor, you don’t need to predict the entire quantum future. You need to reduce regret.

Here’s a pragmatic checklist that aligns quantum with the way AI programs mature.

1. Demand clear export-control guidance for cloud access, collaboration with allies, and performance thresholds that can be audited.
2. Build a “trusted capital” lane for allied investment so domestic startups aren’t forced to choose between delay and risky money.
3. Stand up testbeds that look like operations (security boundaries, ruggedization, integration with existing command-and-control and cyber tooling).
4. Treat post-quantum cryptography as a migration program, not a procurement. Inventory, prioritize, stage deployment, and test rollback.
5. Fund workforce capacity where manufacturing is real: certifications, labs, and equipment that mirror production conditions.

If this sounds familiar, it should. It’s the same playbook AI in defense is converging on: trusted inputs, secure deployment, measurable readiness, and human capacity.

Quantum becomes a security advantage only if policy matches the pace

Quantum policy is easiest to get wrong when it’s written as if the market will patiently adapt. It won’t. Capital reroutes. Talent chooses less regulated industries. Allies build their own stacks.

The United States still has a strong position—research depth, private investment momentum, and a real chance to make quantum manufacturing a domestic strength. But the difference between “research lead” and “strategic advantage” is the stuff policymakers tend to underrate: clear controls, differentiated investment screening, and workforce programs that produce technicians, not just white papers.

If your organization is building or buying AI capabilities for defense and national security, you’re already living inside this pattern. Quantum is simply the next enabling layer. The teams that align quantum policy with operational needs—security, supply chain, and scalable delivery—will shape the next decade of public sector technology.

Where do you see the biggest risk today: policy that slows trusted collaboration, or adversaries that keep finding new paths around the rules?