What’s worth building in quantum computing?
In our quantum computing market deck, you will find everything you need to understand the market
SUMMARY
What’s worth building in quantum computing? The strongest opportunities are the infrastructure layers that useful quantum machines will be forced to buy: error correction, control and calibration, interconnects, specialized components, hybrid computing, and post-quantum migration.
Quantum is commercial now, but not evenly commercial. Revenue, bookings and customer activity are real, while broad application demand still sits behind a small number of hardware vendors, government programs and large enterprises.
The hardware race is still open technically and increasingly closed economically. A new architecture can still matter, but an incremental qubit improvement is unlikely to justify the capital required to compete with companies already raising nine-figure rounds.
The best startup wedges are moving one layer below the headline race for qubits. As processors scale, the ugly engineering problems around decoding, calibration, cryogenics, photonics, packaging, control and test become unavoidable purchases rather than optional research tools.
Error correction is especially attractive because it creates a large classical-computing problem inside every fault-tolerant machine. Fast decoding, error budgeting, orchestration and hardware-aware QEC can become independent businesses across several qubit architectures.
Control and calibration may be better businesses than building qubits for most founders. The market is architecture-agnostic, customers already exist, and larger machines make manual tuning less realistic every year.
Interconnect is becoming strategically important before a global quantum internet exists. The nearer-term market is inside the quantum data center: connecting processors, cryostats, racks and facilities so systems can scale modularly.
Post-quantum cryptography is the clearest software market with immediate demand because migration has to happen even if fault-tolerant quantum computing arrives late. The hard part is increasingly inventory, crypto-agility, PKI, code signing, testing and enforcement rather than inventing another algorithm.
Quantum sensing is a better near-term hardware bet than universal quantum computing when the customer problem already exists. GPS-denied navigation, timing, inertial sensing and magnetic measurement can justify purchases without waiting for millions of reliable quantum gates.
At the application layer, optimization has the strongest production evidence today, while chemistry, materials and drug discovery have the stronger long-term technical case. The safer company design is to create value with classical and hybrid methods now, then use quantum only where it earns its place.
Funding is still overwhelmingly going into hardware systems, but that concentration creates a second-order market. Every giant hardware round turns into spending on suppliers, control systems, error correction, fabs, lasers, cryogenics, photonics, software and test equipment.
The practical conclusion is narrow: build around problems that become harder as qubit counts rise, not around the hope that customers will suddenly need generic quantum software. The closer a product sits to an unavoidable scaling bottleneck, the stronger the startup case.
This market map, featured in our quantum computing market deck, highlights top companies and startups in the quantum computing market
What’s worth building in quantum computing?
Has quantum computing become commercial enough to build a startup around?
Quantum computing is commercial enough to build around today, but the money is still concentrated in a small number of companies, contracts and use cases.
McKinsey’s latest Quantum Technology Monitor found that quantum-computing companies generated more than $1 billion of revenue in 2025. More than 300 organizations were actively working with quantum companies, and among the large enterprises McKinsey studied, one-third were spending more than $10 million a year on quantum initiatives. Seven percent were spending more than $50 million. That is a much more serious customer base than the research-heavy market of a few years ago.
The latest public-company results make the shift even clearer, although they also show how uneven the market remains. IonQ generated $80.1 million in its latest quarter, up 287% year over year. Around 60% came from commercial customers. Infleqtion reported $13.5 million, up 157%. Rigetti generated $5.1 million, while D-Wave generated just $3.1 million in the quarter even though its first-half bookings jumped to $35.5 million.
We should be careful with IonQ’s growth figure because acquisitions contributed to it, and its SEC filing says specialized quantum-hardware contracts were another major driver. Still, the direction is clear: companies are buying machines, cloud access, integration work, sensors, control systems and quantum-related infrastructure.
Is it already too late to build another quantum computer?
Building a new quantum computer can still make sense today, but the bar has become brutal: a startup needs a genuine architectural breakthrough and access to enormous amounts of capital.
The hardware race remains technically open. DARPA currently has 11 companies in Stage B of its Quantum Benchmarking Initiative, covering neutral atoms, trapped ions, superconducting systems, silicon spin qubits, photonics and other approaches. DARPA recently went further and said it now considers a utility-scale quantum computer by 2033 likely, while remaining unsure which team will get there.
That uncertainty has encouraged investors rather than scared them away. OQC recently raised £260 million, roughly $350 million, to expand its superconducting systems. Oratomic emerged this year and almost immediately raised a $300 million Series A around a neutral-atom architecture designed to reach fault tolerance with far fewer physical qubits than older estimates implied. QuantWare raised $178 million to scale its open superconducting processor architecture and build a dedicated quantum fab.
Those rounds show what a new hardware founder is signing up for. Even relatively young companies are raising nine-figure amounts before quantum computing has reached broad commercial usefulness.
We would still back a new quantum-computer company when the founding insight changes the scaling equation dramatically. Oratomic is a good example: its founders say recent work convinced them that a useful system could be built with roughly 10,000 to 20,000 neutral-atom qubits, far below older assumptions. A startup with that kind of discontinuity can still break in.
If you want more recent data on this point, please see our latest quantum computing market report.
As this chart shows, and as featured in our quantum computing market deck, search interest in quantum computing has grown significantly
Which quantum hardware architectures still have room for a startup?
Several quantum hardware architectures still have room for startups, but the best openings are increasingly specific engineering problems rather than entire computing stacks.
Superconducting qubits have a deep ecosystem around IBM, Google, Rigetti, OQC and several suppliers. Their speed is attractive, while scaling keeps creating hard problems around cryogenic wiring, control, packaging and modularity. Silicon-spin systems could eventually achieve extraordinary density because they borrow from semiconductor manufacturing, although uniformity, cryogenic control and readout remain difficult.
Neutral atoms have moved particularly fast lately. QuEra and its academic partners reported fault-tolerant architectures involving as many as 96 logical qubits and demonstrated several pieces needed for universal logical computation. Oratomic has entered the same broad architecture with a very different error-correction design. Large atomic arrays look increasingly feasible, which shifts part of the engineering challenge toward lasers, integrated photonics, automation and control.
Trapped-ion machines from Quantinuum, IonQ and newer companies can reach very high fidelities, but large systems require complex optical control and modular scaling. Photonic systems such as those pursued by PsiQuantum and Xanadu offer an appealing route through semiconductor manufacturing and optical networking, while photon loss, sources, detectors and packaging remain demanding.
| Quantum architecture | What already works well | Where startups can still break in |
|---|---|---|
| Superconducting | Fast gates, mature fabrication ecosystem | Cryogenic control, packaging, readout, modular interconnects |
| Neutral atoms | Large arrays, flexible connectivity | Lasers, photonics, automation, calibration, system integration |
| Trapped ions | Very high fidelity | Integrated optics, control, modular links |
| Silicon spin | Potential semiconductor-scale density | Cryogenic electronics, uniform fabrication, readout |
| Photonic | Networking and manufacturing potential | Sources, detectors, loss reduction, photonic packaging |
| Bosonic / cat qubits | Potentially lower error-correction overhead | Specialized control, decoding and system engineering |
Is quantum error correction one of the best startup opportunities right now?
Quantum error correction is one of the strongest things to build in quantum computing now because the industry has reached the point where error correction has to work continuously on real hardware.
The scientific question has moved forward quickly. Google’s Willow work showed below-threshold error correction, where increasing the size of the error-correcting code reduced the logical error rate. QuEra and its academic collaborators have demonstrated increasingly sophisticated logical operations on neutral atoms. Other approaches are testing different codes, qubit types and ways of reducing the physical-qubit overhead.
The bottleneck now includes a large classical-computing problem. Quantum hardware continuously produces error information that has to be decoded quickly enough for the computer to keep operating. A decoder that is accurate but too slow is useless.
Riverlane has built an entire company around that problem. Its Deltaflow 2 system performs real-time error correction on as many as 250 physical qubits, and the company says it works with more than 20 quantum-computer makers and national labs across the major qubit types. In a recent technical demonstration, Riverlane reported end-to-end QEC latency roughly ten times lower than results it compared against from Google.
The startup openings go beyond inventing new codes. We see room in real-time decoding hardware, error-budgeting software, code selection, hardware-aware compilers, QEC verification and simulation.
This chart, included in our quantum computing market deck, illustrates yearly VC funding for quantum computing startups
Are quantum control and calibration better businesses than building qubits?
Quantum control and calibration currently look like better startup businesses than qubits for most founders because every hardware company needs them and the workload gets harder as machines become larger.
Quantum Machines is the clearest proof. The company raised $170 million in 2025, bringing its total funding to $280 million, and said its systems were already used by more than half of the companies developing quantum computers. Rather than choosing one qubit architecture, it sells the electronics and software needed to control several of them.
The company has kept moving deeper into the stack. In 2026 it launched an open acceleration architecture with Nvidia, AMD and Riverlane that connects quantum-control hardware directly to CPUs, GPUs, FPGAs and ASICs with microsecond-level latency.
Qblox is moving in the same direction from another part of the control market. It recently started manufacturing control systems in the United States and is working with HPE on integrating quantum processors into high-performance computing environments. Q-CTRL, meanwhile, has been pushing autonomous calibration. Its collaboration with Equal1 is designed so rack-mounted silicon quantum computers can tune and maintain themselves without a quantum physicist standing beside the machine.
A laboratory can survive with scientists manually recalibrating a small device. A data center containing thousands or millions of physical qubits cannot. Automated calibration, pulse optimization, drift detection, control orchestration and diagnostics therefore look increasingly valuable.
If you want more recent data on this point, please see our latest quantum computing market report.
Is quantum interconnect becoming an unavoidable market?
Quantum interconnect is becoming one of the more credible infrastructure markets because several leading roadmaps now assume that useful quantum computers will have to connect multiple processors.
Scaling a single processor forever becomes physically awkward. Wiring grows, cryogenic systems get crowded, optical systems become more complex, and yields become harder to manage. Modular architectures offer another route: build smaller high-quality processors and connect them.
IonQ acquired Lightsynq, a photonic-interconnect startup, for roughly $306 million in 2025. That is a large price for a company that had raised about $18 million in its Series A. IonQ described the technology as important to connecting quantum processors and scaling fault-tolerant systems.
QphoX is tackling a different piece. Superconducting qubits operate with microwave signals, while long-distance networking works far better with optical photons. QphoX is developing transducers that bridge those two domains. Rigetti and QphoX received a $5.8 million U.S. Air Force Research Laboratory contract to develop optical networking between superconducting systems.
memQ raised $10 million this year around quantum memory, network interfaces and distributed control. Shortly afterward, DARPA selected the company to work on a hardware- and network-aware compiler for distributed quantum computing.
For now, the stronger market is inside quantum data centers rather than a global “quantum internet”: links between processors, cryostats, racks and facilities.
This chart, included in our quantum computing market deck, looks at IonQ’s strategy in quantum computing
Can a startup make money selling quantum components before useful quantum computers arrive?
Quantum-component startups can already make money today because research labs, governments and hardware companies need physical infrastructure long before fault-tolerant quantum applications are widespread.
Cryogenics is an obvious example. Many superconducting and spin-based systems operate at temperatures close to absolute zero. Germany’s kiutra raised €13 million after developing magnetic refrigeration that avoids helium-3, a scarce isotope used in conventional dilution refrigerators. The company already had systems deployed with research institutions, startups and corporate customers when it raised the round.
Quantum processors themselves are also beginning to split from full systems. QuantWare says it has shipped processors to customers in 20 countries and powered quantum computers in more than five national markets. Its $178 million financing is funding a 20-fold increase in manufacturing capacity and a dedicated open-architecture fab.
Control electronics are becoming their own supply chain as well. Qblox now manufactures systems in the United States, while companies such as Quantum Machines sell hardware that can serve several different qubit types. The same separation is starting in photonics, packaging, microwave electronics and test equipment.
| Component | Who buys it now | Why demand should grow |
|---|---|---|
| Cryogenic systems | Quantum labs and hardware companies | More processors and denser systems need more cooling capacity |
| Control electronics | Most quantum-computer developers | Qubit count and feedback complexity keep rising |
| Integrated photonics | Neutral-atom, ion and networking companies | Optical systems become harder to manage manually at scale |
| Quantum processors | Labs, system integrators, national programs | Open architectures allow companies to buy rather than fabricate |
| Packaging and interconnects | Solid-state quantum developers | Modularity and higher density make packaging performance critical |
| Test and metrology tools | Fabs, labs and component makers | Quantum manufacturing needs repeatable characterization |
Is post-quantum cryptography the safest quantum startup market?
Post-quantum cryptography is probably the safest quantum-related startup market right now because companies have to migrate their security even if useful quantum computing takes longer than expected.
The standards question has largely been settled. NIST has finalized ML-KEM for key establishment and ML-DSA and SLH-DSA for digital signatures, and NIST is now explicitly telling organizations to begin migrating. Its current migration work focuses on cryptographic inventories, risk management, interoperability and benchmarking.
The policy pressure has also become much harder to ignore. A recent U.S. executive order requires high-value federal assets and high-impact systems to use post-quantum cryptography for key establishment by the end of 2030 and for digital signatures by the end of 2031. Federal procurement rules are also being prepared so covered contractors have to comply with relevant NIST standards by 2030.
A bank does not have to believe a fault-tolerant computer will arrive in five years before it starts looking for RSA and elliptic-curve cryptography buried across thousands of applications, certificates, devices and third-party products.
The strongest startup opportunities are therefore around migration: cryptographic discovery, inventories, crypto-agility, PKI upgrades, code-signing infrastructure, testing and policy enforcement. Long-lived industrial systems, connected devices and critical infrastructure look especially painful because many assets cannot be upgraded with a simple software patch.
If you want more recent data on this point, please see our latest quantum computing market report.
This chart, included in our quantum computing market deck, illustrates yearly funding for quantum computing startups
Is quantum sensing a better startup bet than quantum computing?
Quantum sensing is currently a better near-term startup bet than universal quantum computing for founders who want to sell useful hardware before fault tolerance arrives.
Quantum sensors can exploit atomic clocks, atom interferometry, magnetic effects and other quantum phenomena without performing millions of reliable logic operations. That opens applications in navigation, timing, gravity measurement, magnetic sensing, underground detection and scientific instrumentation.
Infleqtion is a useful example because we can see actual revenue. The company’s latest filing showed quarterly revenue of $13.5 million, up 157% year over year, with much of the increase coming from contracts involving NASA, the U.S. Army and the Department of Energy. Its portfolio includes quantum computing, atomic clocks, inertial sensing and RF sensing, so the revenue is not a clean sensing-only figure. It does show that quantum hardware can already be sold into mission-driven government programs.
Q-CTRL has gone even more directly after the navigation problem. The company is working with ANELLO Photonics on GPS-independent navigation for unmanned aircraft, combining quantum magnetic navigation with photonic inertial sensing.
The trade-off is straightforward: quantum sensing still requires difficult hardware engineering and slow procurement, but customers already have problems such as GPS denial, timing accuracy and navigation drift that they will pay to solve.
Is there still room for quantum software and hybrid quantum-classical infrastructure?
There is still room for quantum software today, especially where it connects quantum processors to classical computing, but another generic circuit-building SDK is a weak place to start.
Developers already have Qiskit, Cirq, PennyLane, CUDA-Q and software provided by major hardware and cloud companies. Basic access to quantum processors is increasingly standardized. A new startup has to solve something substantially harder than letting a developer write and submit a quantum circuit.
Classiq shows where investors think a bigger software layer might exist. The company raised $110 million in 2025 and later added strategic capital from investors including AMD Ventures, Qualcomm Ventures and IonQ, taking cumulative funding above $200 million. Classiq says its customer base and revenue have been tripling year over year, with users including BMW, Citi, Deloitte, Mizuho and Toshiba.
The hybrid-computing architecture is also becoming concrete. Japan’s ABCI-Q combines quantum processors with 2,020 Nvidia H100 GPUs across more than 500 nodes. Quantum Machines has opened its control stack so CPUs, GPUs, FPGAs and ASICs can interact with a quantum processor at microsecond latency. Qblox is working with HPE on quantum-HPC integration, while Infleqtion has demonstrated its neutral-atom system with Nvidia’s NVQLink architecture.
The valuable software problems are moving toward resource estimation, verification, hardware-aware compilation, workflow partitioning, runtime optimization, observability and deciding when a QPU call is worth making.
Cloud providers will handle basic QPU access. The more interesting companies can own the intelligence around that access.
This chart, included in our quantum computing market deck, compares the main business model options for quantum computing hardware startups
Which quantum applications are worth building a startup around?
Optimization is producing the clearest examples of quantum computing in production today, while chemistry and materials look like the strongest long-term application markets. Most standalone quantum-algorithm startups are still too early unless they can create value before fault-tolerant hardware arrives.
D-Wave gives us unusually concrete evidence on optimization. NTT DOCOMO recently put a second D-Wave-powered application into production for mobile-network planning. The system reduced peak location-registration signals by 65.3% and paging signals by 7% in the reported deployment. In one representative problem covering 333 base stations, the optimization ran in roughly five minutes.
D-Wave’s latest financial results also show that production usage is becoming a larger part of its cloud business. Production applications represented 37.3% of first-half quantum-computing-as-a-service revenue, compared with 9.8% a year earlier. Revenue itself remains small, so this is still an early commercial market rather than a mass one.
Chemistry and materials offer the strongest technical rationale for fault-tolerant quantum computing because molecules and materials are quantum systems. Infleqtion and Nvidia have already demonstrated a small materials-science workflow using logical qubits, with the encoded version cutting the reported error rate from around 15% to below 3%. Drug discovery could benefit from the same underlying capability.
Finance attracts serious experimentation, although institutions such as JPMorgan Chase already have excellent classical quantitative teams. That makes the benchmark particularly hard to beat.
The startup design matters as much as the application. A materials or drug-discovery company can use classical simulation, AI and high-performance computing today and add quantum methods where they genuinely improve the workflow. A company built around one quantum algorithm has much more exposure to somebody else’s hardware roadmap.
| Application | Evidence today | Our view for a new startup |
|---|---|---|
| Optimization and scheduling | Some production deployments already exist | Best near-term quantum application wedge |
| Chemistry and materials | Strong technical fit, early logical-qubit demonstrations | Best long-term application market |
| Drug discovery | Large customer budgets and direct link to molecular simulation | Attractive if paired with a useful classical product |
| Finance | Serious institutional research | High-value market, very hard classical benchmark |
| Industrial simulation | Growing interest in hybrid workflows | Good place for domain-specific infrastructure |
| Quantum machine learning | Lots of research, little proven commercial advantage | Too early for most standalone startups |
If you want more recent data on this point, please see our latest quantum computing market report.
Where is quantum startup funding going now?
Quantum startup funding is still flowing overwhelmingly toward hardware, and the latest rounds show how expensive the race has become.
In our review of 43 disclosed pure-play quantum-computing rounds across the latest 12-month window, companies raised about $7.4 billion. Hardware-system companies captured roughly $6.75 billion, or 91% of the total.
The pattern has continued lately. OQC raised about $350 million. Oratomic raised $300 million in its first major round. QuantWare raised $178 million. Quantum Motion raised $160 million. Quobly raised €115 million. Alice & Bob added Nvidia’s venture arm to a €100 million Series B. Several different qubit architectures are attracting large checks at the same time.
Government money is reinforcing the build-out. U.S. programs are putting substantial funding behind quantum processors, fabs, packaging, supply chains and manufacturing, while DARPA is funding independent technical validation of competing architectures.
The second-order opportunity is substantial: every large hardware round becomes spending on control systems, lasers, cryogenics, photonics, fabrication, test equipment, software and error correction.
This chart, featured in our quantum computing market deck, illustrates how revenue is divided among customer segments in the quantum computing market
What quantum computing startups should founders avoid?
Founders should currently avoid quantum startups whose entire business depends on being early to a market that customers still cannot use.
Another generic quantum SDK would be high on our avoid list. Open-source tools and platforms from IBM, Google, Nvidia, Xanadu, Amazon and hardware vendors already cover the basic programming layer. A new developer tool needs a difficult technical wedge such as verification, resource estimation or fault-tolerant compilation.
We would also be wary of launching a full-stack hardware company around an incremental improvement. The recent financing market shows what happens once a hardware architecture becomes credible: the company may need hundreds of millions of dollars to stay in the race.
Pure NISQ application companies face another problem. If a startup needs a quantum processor to beat a strong classical method before customers get value, the founding team has handed control of its product roadmap to somebody else.
Generic “quantum AI” pitches deserve similar skepticism unless there is a specific workload, measurable advantage and convincing classical comparison.
Quantum consulting can produce revenue, but we would want repeated projects to expose a product that customers eventually buy without a large services team.
So what is actually worth building in quantum computing?
What is most worth building in quantum computing today is the infrastructure that every credible path to useful quantum machines keeps running into: error correction, control, automation, interconnects, specialized components and hybrid computing.
Quantum error correction sits near the top for us. Fault-tolerant computers cannot scale without fast decoding, error budgeting, orchestration and hardware-aware QEC. The requirement exists across several competing qubit architectures.
Control and calibration are similarly attractive. Quantum Machines, Qblox and Q-CTRL are already showing that independent companies can own valuable parts of this layer, and larger machines make automated operation more important.
Interconnects and quantum components deserve more attention. IonQ paying roughly $306 million for Lightsynq is a strong indication that modular connectivity can become strategically important. Activity around cryogenics, photonics, processors, packaging and test systems suggests a broader supply chain is forming around quantum computing.
Post-quantum cryptography is the clearest software market with immediate demand. Standards exist, migration deadlines are appearing and companies have large cryptographic estates to discover and replace.
Quantum sensing is probably the best adjacent hardware opportunity. Navigation, timing, gravity and magnetic sensing solve problems that customers already have, especially in defense and critical infrastructure.
At the application layer, we would focus on chemistry, materials, drug discovery and selected optimization problems, preferably through companies that can already create value with classical and hybrid computing.
A new full-stack quantum computer still belongs on the list in exceptional cases. The recent arrival of Oratomic shows that a genuinely different error-correction or scaling insight can reopen the hardware race. Without something that fundamental, the economics are increasingly hostile to a new entrant.
Our answer is therefore fairly concentrated: build what thousands of reliable qubits will force the industry to buy.
If you want more recent data on this point, please see our latest quantum computing market report.
This chart, included in our quantum computing market deck, shows how cloud quantum computing access technology has evolved over time
OUR METHODOLOGY
This analysis asks what is actually worth building in quantum computing today. We broke the market into the dimensions that most directly shape startup attractiveness: current commercialization, technical bottlenecks, architecture maturity, capital intensity, infrastructure requirements, application readiness, customer pull and financing activity.
We prioritized recent, observable evidence: reported revenue and bookings, customer deployments, government contracts, technical demonstrations, standards and migration requirements, acquisitions, financing rounds and changes in major hardware roadmaps. Older research was used where it remained technically relevant, but newer evidence carried more weight when judging where the market stands now.
We did not treat any single announcement as decisive. A large funding round shows investor conviction, not customer demand. A laboratory breakthrough can remove an important technical barrier without creating an immediate business. A commercial deployment can prove willingness to pay without proving a large market. The strongest conclusions come from several kinds of evidence pointing in the same direction.
We also separated commercial evidence today from technical necessity tomorrow. Post-quantum migration and parts of quantum sensing can already be justified by existing customer needs. Error correction, automated control and interconnects are attractive for a different reason: increasingly credible paths to large-scale quantum computing keep running into the same engineering requirements.
At the application layer, we looked separately at where quantum systems are already entering production workflows and where the underlying physics gives quantum computing a particularly strong long-term reason to matter. That keeps early commercial activity from being confused with long-term technical potential.
For the financing analysis, we separately reviewed 43 disclosed pure-play quantum-computing funding rounds across the latest 12-month window. We used announced round values rather than estimates for undisclosed transactions and classified companies according to the primary product being financed. The goal was to understand where capital intensity and investor commitment are concentrated, not to rank opportunities mechanically by dollars raised.
We did not use a mechanical scoring model. Instead, we assessed the evidence point by point and gave the greatest weight to opportunities where technical necessity, market demand and startup defensibility increasingly reinforce one another.
Key sources include McKinsey’s Quantum Technology Monitor 2026 on commercialization and enterprise spending; DARPA’s Quantum Benchmarking Initiative and 2026 QBI update on architecture diversity and utility-scale timelines; Google Quantum AI’s Willow work and Riverlane’s Deltaflow 2 results on quantum error correction; Quantum Machines and Q-CTRL on control and autonomous calibration; IonQ’s Lightsynq acquisition and Rigetti and QphoX’s AFRL contract on interconnects; QuantWare on processor manufacturing; NIST, NCCoE and the White House on post-quantum migration; Classiq on higher-level quantum software; Infleqtion’s Q2 2026 filing on revenue and government programs; and Oratomic on the economics and technical ambition of a new hardware entrant.
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