Decoupling quantum from cryogenics

McKinsey's Quantum Technology Monitor projects $1.3 to $2 trillion in economic value at stake across life sciences, chemicals, finance, and materials science through quantum optimization by 2035. But there's a problem: virtually all of that value is theoretical — because the quantum computers that could capture it are trapped inside $20 million dilution refrigerators that only function at 15 millikelvin.

The $2 trillion promise

McKinsey's analysis breaks the quantum value opportunity into four primary domains:

1. Pharmaceuticals and life sciences: $300–500 billion. Quantum simulation can model molecular interactions at fidelities impossible for classical computers. Drug discovery timelines could compress from 12–15 years to 3–5 years. Protein folding, molecular docking, and pharmacokinetic simulation all benefit from quantum advantage in exponentially scaling Hilbert spaces.

2. Chemicals and materials science: $200–400 billion. Catalyst design, polymer engineering, and battery chemistry optimization require simulating quantum mechanical interactions between atoms. Classical approximations (density functional theory) are imprecise. Quantum computers can simulate these systems natively, designing materials with specific properties from first principles.

3. Finance: $300–600 billion. Portfolio optimization, risk modeling, and derivative pricing are combinatorial problems that scale exponentially on classical hardware. Quantum optimization algorithms (QAOA, VQE) can find near-optimal solutions in polynomial time. High-frequency trading, fraud detection, and credit risk analysis all represent major deployment opportunities.

4. Mobility, logistics, and defense: $200–500 billion. Route optimization, supply chain management, sensor fusion for autonomous systems, and cryptanalysis are all quantum-ready workloads. The U.S. Department of Defense alone spends over $5 billion annually on quantum technology research and deployment.

The cryogenic trap

Despite these vast opportunities, the current state of quantum computing is defined by a single constraint: temperature.

The dominant quantum computing architectures — superconducting qubits (IBM, Google) and trapped ions (IonQ, Quantinuum) — require operating temperatures at or near absolute zero:

• Superconducting qubits: 10–15 millikelvin (0.015K). Achieved using dilution refrigerators that cost $1–5 million each and consume 25+ kW of power.
• Trapped ion systems: Near room temperature for the ions themselves, but require ultra-high vacuum chambers, complex laser arrays, and electromagnetic trapping infrastructure that costs $2–10 million per system.
• Neutral atom systems: Require laser cooling to microkelvin temperatures and magneto-optical traps.

The infrastructure required to maintain these temperatures is staggering. A single IBM Quantum System Two occupies approximately 22 square meters of floor space — not for the quantum processor (which is the size of a fingernail), but for the dilution refrigerator, control electronics, helium circulation system, and vibration isolation platform.

Helium-3, the isotope required for dilution refrigerators, costs $2,000+ per liter and is primarily produced as a byproduct of nuclear weapons maintenance. Global supply is constrained and geopolitically sensitive.

"The quantum computer isn't the chip. It's the building. And that building costs $20 million, requires specialized helium isotopes, takes days to cool down, and can't be moved. That's not computing. That's a laboratory."

Why cryogenics limits the TAM

The practical consequences of cryogenic dependence are devastating for market adoption:

Deployment is centralized. You cannot install a dilution refrigerator in a hospital, a bank trading floor, a pharmaceutical lab, or a defense installation. Quantum computing is available only via cloud access to a handful of facilities operated by IBM, Google, and a few startups. This limits quantum computing to use cases that can tolerate cloud latency and data sovereignty risks.

Scaling is linear. Each additional quantum processor requires its own dilution refrigerator. There is no equivalent of "adding a server to the rack." The cost per additional qubit system is measured in millions of dollars, not thousands.

Field deployment is impossible. The U.S. DoD cannot put a dilution refrigerator on a drone, a submarine, or a tactical vehicle. The entire $5B+ annual defense quantum budget is constrained to laboratory environments. Field-deployable quantum computing — for navigation, cryptanalysis, sensor fusion, and electronic warfare — remains science fiction under current architectures.

Uptime is limited. Dilution refrigerators take 24–72 hours to cool down from room temperature. Any maintenance event — a vacuum leak, a helium supply interruption, a power outage — results in days of downtime. This is incompatible with enterprise SLA requirements.

Room temperature changes everything

QLT's photonic quantum processor operates at 300K — room temperature. This is not an incremental improvement. It eliminates the fundamental infrastructure barrier that constrains the entire quantum computing industry.

Consider what becomes possible:

• Pharmaceutical deployment: A quantum processor installed in a drug discovery lab, running molecular simulations locally, with no cloud latency and no data sovereignty concerns. Pfizer, Roche, and Novartis can run quantum chemistry in-house.
• Financial deployment: Quantum optimization running on a trading floor for real-time portfolio rebalancing. Goldman Sachs and Citadel can deploy quantum advantage without routing sensitive financial data to a third-party cloud.
• Defense deployment: Quantum processors integrated into drones, submarines, and satellite systems for real-time signal intelligence, GPS-denied navigation, and cryptanalysis. The entire field-deployable defense market opens up.
• Edge deployment: Quantum-enhanced inference running on autonomous vehicles, surgical robots, and industrial control systems without thermal or infrastructure constraints.

How ODR enables 300K coherence

The reason quantum processors require cryogenic temperatures is decoherence — the loss of quantum information due to thermal noise in the environment. At room temperature, thermal vibrations in solid-state materials destroy quantum superposition states in nanoseconds.

QLT's Optical Distortion Reversal (ODR) takes a fundamentally different approach. Instead of trying to eliminate environmental noise by cooling to near-zero, ODR reverses the distortion that noise causes. The waveguide geometry is engineered so that phase distortions accumulated in one segment are structurally compensated in a subsequent segment — passively, without active error correction, and without cryogenic isolation.

This is not the same as classical error correction, which requires massive overhead (potentially 1,000+ physical qubits per logical qubit). ODR operates at the physical layer, maintaining coherence through geometry rather than software.

Unlocking the $2 trillion

McKinsey's $2 trillion projection assumes quantum computers eventually become deployable at scale across industries. Every existing roadmap to that deployment runs through solving the cryogenic problem.

QLT has already solved it. Not by building a better refrigerator, but by building a quantum processor that doesn't need one.

The $2 trillion is not a forecast of what quantum computing might be worth someday. It's a forecast of what becomes available the moment quantum processing can be deployed like any other computing technology — in a rack, in a facility, at the point of need, without specialized infrastructure.

That moment is room temperature. That technology is photonic. That company is QLT.

$2 trillion in quantum value is locked behind dilution refrigerators. QLT has the key: 300K.