Overview
Next-generation computing is not one market. It is a set of attempts to push beyond the limits of conventional computing: quantum systems for problems classical computers struggle with, photonics for moving data faster and more efficiently, neuromorphic chips for low-power sensing and inference, and advanced semiconductor manufacturing for the AI era.
The investable problem is that many pure-play quantum and photonics companies are still very early in revenue terms. The most attractive research angle is therefore not simply “which quantum computer wins?” but “which bottlenecks become unavoidable if AI, quantum, photonics and advanced packaging scale?”
EarlyTheme maturity
Very HighCapital intensity
ExtremeValuation dispersion
HighCross-theme importance
Stock Table
Working watchlist. Pure quantum names are included because they define the category, but the higher-quality picks-and-shovels angle may sit in test, photonics, packaging, edge IP and post-quantum security.
| Rank | Company | Ticker | Role in compute stack | Category | Research view |
|---|
| 1 | IonQ | IONQ | Trapped-ion quantum platform, quantum networking, sensing and security | Quantum platform leader | Best public quantum revenue trajectory and balance sheet, but no longer a microcap and still expensive/speculative. |
| 2 | D-Wave Quantum | QBTS | Annealing and gate-model quantum systems, software and QCaaS | Quantum platform | More practical near-term enterprise optimisation story; still speculative but revenue/bookings momentum improved. |
| 3 | Rigetti Computing | RGTI | Superconducting quantum systems, chiplet-based QPUs, cloud and on-prem systems | Quantum hardware | Interesting technical roadmap and chiplet architecture; revenues remain small and losses high. |
| 4 | Quantum Computing Inc. | QUBT | Integrated photonics, quantum optics machines and TFLN photonic foundry services | Quantum photonics | Highly speculative but important photonics/foundry angle; financing gives runway but revenue validation is essential. |
| 5 | POET Technologies | POET / PTK.V | Photonic integrated circuits, optical engines and optical interposer for AI/data centres | Photonics / AI interconnect | Powerful AI interconnect thesis; production orders and cash improved, but current revenue base remains tiny. |
| 6 | Aehr Test Systems | AEHR | Semiconductor and photonics test/burn-in for AI processors, silicon photonics and power devices | Test / reliability | Cleaner picks-and-shovels angle than many quantum pure-plays; cyclical but tied to AI and photonics reliability. |
| 7 | QuickLogic | QUIK | eFPGA IP, rugged FPGAs, endpoint AI and sensor/voice processing | Programmable logic / edge AI | Small, lumpy and speculative; relevant to embedded next-gen compute and government programmes. |
| 8 | SEALSQ | LAES | Semiconductors, PKI and post-quantum security chips | Post-quantum security | Relevant quantum-security picks-and-shovels angle; needs evidence of sustained commercial transition. |
| 9 | BrainChip | BRN.AX | Akida neuromorphic edge-AI IP | Neuromorphic compute | Good thematic purity but revenue remains too early relative to valuation. |
| 10 | ACM Research | ACMR | Wafer cleaning, advanced packaging and semiconductor process equipment | Semiconductor infrastructure | Less speculative and more revenue-backed; geopolitical/export-control risk is the main complication. |
Value Chain Map
| Layer | What it supplies | Representative names | Investment note |
|---|
| Quantum hardware | Qubits, control systems, cryogenics, traps, superconducting circuits, annealers | IonQ, D-Wave, Rigetti, QCI | Highest upside but longest timeline and most valuation risk. |
| Quantum software / access | Cloud access, optimisation tools, hybrid workflows, algorithms, developer platforms | IonQ, D-Wave, Rigetti | Revenue is emerging, but quantum advantage is not broadly established. |
| Photonics and optical interconnect | Optical engines, TFLN, modulators, laser sources, optical interposers | POET, QCI, photonics supply chain | Important for AI scaling even if quantum timelines disappoint. |
| Semiconductor manufacturing | Advanced packaging, wafer processing, test, burn-in, reliability | Aehr, ACM Research | More picks-and-shovels; revenue may arrive before pure quantum monetisation. |
| Neuromorphic and edge compute | Brain-inspired chips, ultra-low-power inference, sensor-processing IP | BrainChip, QuickLogic, CEVA, Ambiq | Crosses into AI, robotics and human augmentation. |
| Post-quantum security | Secure chips, PKI, hardware roots of trust, quantum-safe cryptography | SEALSQ, cybersecurity ecosystem | Quantum creates a cybersecurity transition even before useful quantum computers are common. |
Sub-Themes
- Quantum computing: trapped-ion, superconducting, annealing, photonic and hybrid systems.
- Photonics: optical interconnect, co-packaged optics, TFLN, optical engines and photonic chip foundries.
- Neuromorphic computing: event-driven, low-power chips and IP for sensors and edge inference.
- Advanced packaging: chiplets, high-bandwidth memory, wafer-level processing and thermal bottlenecks.
- Semiconductor test: reliability, burn-in and validation for AI processors, photonics and power devices.
- Post-quantum security: hardware and cryptographic transition required before fault-tolerant quantum is widespread.
Market Forces
- AI scaling pressure: data movement, interconnect, memory bandwidth and power efficiency are becoming as important as raw compute.
- National security funding: quantum, photonics and secure semiconductors are strategic priorities.
- Technical uncertainty: no single quantum modality has fully won; error correction remains a bottleneck.
- Capital availability: speculative compute companies often need frequent financing before revenue maturity.
- Supply-chain sovereignty: semiconductor manufacturing, foundries and trusted supply chains matter more as computing becomes geopolitical.
- Cybersecurity transition: post-quantum cryptography creates demand before quantum computers become broadly commercial.
- Adoption urgency paradox: the sectors with the strongest theoretical problem-fit for quantum, including pharmaceuticals, chemicals and materials, are not necessarily moving fastest. Defence, financial services and telecommunications are leading in pilots and partnerships, driven more by competitive risk than by technical readiness.
- Geographic investment imbalance: as of 2024, US-based quantum companies captured around 57% of global private quantum investment, versus roughly 10% for the European Union, despite strong European public funding and a deep scientific talent pool. This shapes where commercial partnerships, scaling capital and listed equity exposure are concentrated.
Technology Deep Dive
Next-gen computing is best treated as a portfolio of bottlenecks rather than a single forecast. Quantum promises new computational capability; photonics solves data movement; neuromorphic chips aim at low-power sensing; advanced packaging keeps AI scaling alive; post-quantum security responds to the long-term threat that quantum creates.
| Bottleneck | Why it matters | Public-market angle |
|---|
| Qubit quality and error rates | Useful quantum computing requires qubits that can be scaled and corrected without overwhelming error. | IonQ, Rigetti, D-Wave, QCI. |
| Quantum control and manufacturing | Quantum systems need precision fabrication, control electronics, packaging and often cryogenic or optical infrastructure. | Rigetti Fab-1, IonQ/SkyWater strategy, QCI foundry approach. |
| Optical data movement | AI clusters are bottlenecked by data movement between chips, boards and racks. | POET, QCI, silicon photonics ecosystem. |
| Reliability and test | High-value AI chips, photonics and power semiconductors need burn-in and validation. | Aehr Test Systems. |
| Post-quantum migration | Organisations must secure data before cryptographically relevant quantum machines arrive. | SEALSQ and broader cybersecurity stack. |
| Low-power intelligence | Wearables, robots and sensors need compute that operates within severe power constraints. | BrainChip, QuickLogic, CEVA, Ambiq. |
Two-Stage Maturity Timeline
Quantum computing is most usefully framed as two overlapping horizons rather than a single forecast.
| Horizon | Timeframe | Defining mode | Where value is captured |
|---|
| Stage 1: Hybrid quantum-classical | 2 to 5 years | Noisy intermediate-scale quantum systems paired with classical high-performance computing and AI. | Molecular and materials simulation, portfolio optimisation, supply chain and energy-grid modelling, early heuristic optimisation. |
| Stage 2: Fault-tolerant quantum computing | 5 to 10 years | Error-corrected systems with stable qubits, expected from around 2030. | Large-scale biology, climate and materials simulation; mission-critical optimisation and risk engines; quantum machine learning at scale. |
Prime factorisation at scale, the capability that breaks current public-key cryptography, is generally placed at the far end of this curve, with broad availability not expected until the mid-2030s. The implication is that the post-quantum security transition has a longer runway than near-term hybrid use cases, but companies handling long-lived sensitive data must act earlier because of “harvest now, decrypt later” risk.
Company Profiles
1. IonQ · IONQ
Trapped-ion quantum platform · category leader but no longer microcap
IonQ is the clearest public-market quantum platform story, with exposure across quantum computing, networking, sensing and security. It has the strongest revenue profile in the public quantum group, but that quality is already reflected in a much larger valuation.
- Why it matters: public quantum leader with commercial revenue, large cash/investment balance and aggressive platform expansion.
- Recent evidence: FY2025 revenue was $130m, up 202%, with $3.3bn in cash, cash equivalents and investments at year-end. Q1 2026 revenue rose to $64.7m and management raised 2026 revenue guidance to $260m–$270m.
- Main risks: valuation, continued adjusted EBITDA losses, technology uncertainty and integration of acquisitions.
- Research rating: category anchor / watch for pullbacks rather than hidden microcap.
2. D-Wave Quantum · QBTS
Annealing and gate-model quantum · optimisation-focused quantum platform
D-Wave offers a more near-term optimisation angle through annealing while also working on gate-model systems. It is useful as a marker for whether quantum can solve practical enterprise optimisation problems before fault-tolerant universal systems arrive.
- Why it matters: dual-platform quantum company with enterprise optimisation use cases and improved liquidity.
- Recent evidence: FY2025 revenue increased 179% year-on-year, gross profit increased 265%, and the company ended 2025 with more than $884m of liquidity.
- Main risks: quantum advantage proof, customer adoption, burn rate and valuation volatility.
- Research rating: quantum platform watchlist.
3. Rigetti Computing · RGTI
Superconducting quantum systems and chiplet-based QPUs
Rigetti is a full-stack superconducting quantum company with in-house chip fabrication and a chiplet-based scaling strategy. The technical story is interesting, but the current financial base remains small relative to the investment required.
- Why it matters: chiplet approach, Fab-1 manufacturing and on-prem quantum systems give it a differentiated hardware roadmap.
- Recent evidence: Rigetti reported progress toward a 108-qubit chiplet-based system, 99.9% two-qubit gate fidelity on a prototype platform, and an $8.4m purchase order from India’s C-DAC for a 108-qubit system.
- Main risks: tiny revenue, high operating losses, customer concentration and long technical timeline.
- Research rating: speculative quantum hardware watchlist.
4. Quantum Computing Inc. · QUBT
Quantum photonics, TFLN foundry and quantum optics
QCI is a photonics-heavy quantum story. Its appeal is not only quantum machines, but the possibility that its foundry and TFLN photonics capabilities become useful in datacom, telecom, sensing, AI and cybersecurity.
- Why it matters: integrated photonics can be relevant across quantum and AI infrastructure.
- Recent evidence: QCI completed a $750m private placement, acquired Luminar Semiconductor for $110m after quarter-end, and reported that its Fab 1 facility had begun small-batch manufacturing and revenue contribution.
- Main risks: revenue remains early, dilution/financing history, execution and hype risk.
- Research rating: speculative photonics/quantum optionality.
5. POET Technologies · POET / PTK.V
Photonic integrated circuits and optical engines for AI/data-centres
POET is a high-upside optical interconnect story. Its optical interposer, optical engines and light-source products target AI systems and hyperscale data centres where interconnect bandwidth and power efficiency are major bottlenecks.
- Why it matters: AI scaling creates demand for faster, lower-power optical data movement.
- Recent evidence: POET reported more than $225m of financing in Q4 2025, an additional $150m in January 2026, $430m in cash, and a production order worth more than $5m for Infinity optical engines.
- Main risks: tiny revenue base, manufacturing scale-up, customer conversion and valuation volatility.
- Research rating: high-upside photonics watchlist; not a proven compounder yet.
6. Aehr Test Systems · AEHR
Semiconductor and photonics test/burn-in
Aehr supplies semiconductor test and burn-in systems. It is not a quantum computer company, but its relevance increases if AI processors, silicon photonics and power semiconductors require more reliability testing.
- Why it matters: test and burn-in are picks-and-shovels functions as compute hardware becomes more expensive and power dense.
- Recent evidence: Aehr reinstated guidance on improved visibility for AI processor and data-centre semiconductor test and burn-in, and later raised second-half FY2026 bookings expectations toward the high end of $60m–$80m.
- Main risks: cyclical order timing, lumpy revenue, customer concentration and recent losses.
- Research rating: practical infrastructure bottleneck watchlist.
7. SEALSQ · LAES
Post-quantum security semiconductors and PKI
SEALSQ sits at the security edge of next-generation computing. The thesis is that post-quantum cryptography and hardware roots of trust become necessary as quantum threats move from theoretical to strategic.
- Why it matters: post-quantum security can commercialise before useful universal quantum computing arrives.
- Recent evidence: FY2025 revenue reached $18.3m, up 66%, with first production revenues from next-generation post-quantum technology products expected in the latter part of 2026.
- Main risks: small scale, commercial transition risk, competition and speculative valuation.
- Research rating: post-quantum security watchlist.
Future Scenarios
Bull case: quantum systems secure specialised commercial niches, photonics becomes essential for AI scaling, and semiconductor test/packaging suppliers see sustained demand from AI and advanced compute.
Base case: AI-related photonics, packaging and test monetise before broad quantum advantage. Pure quantum names remain volatile but continue to receive strategic funding.
Bear case: quantum timelines stretch, commercial orders disappoint, speculative valuations reset, and only the more conventional semiconductor infrastructure names hold up.
Signals to Watch
- IonQ revenue guidance, backlog and acquisition integration.
- D-Wave enterprise bookings and evidence of repeatable optimisation use cases.
- Rigetti deployment of its 108-qubit chiplet-based systems and fidelity progress.
- QCI foundry revenue, Luminar Semiconductor integration and photonics customer validation.
- POET production orders and conversion from customer interest to revenue.
- Aehr bookings from AI processors, silicon photonics and data-centre semiconductor customers.
- SEALSQ QS7001 and post-quantum chip commercial production revenue.
- Evidence of fault-tolerant quantum milestones from leading providers, including IBM, Google, IonQ, Quantinuum and PsiQuantum, particularly logical-qubit demonstrations and error-correction breakthroughs.
- Outcome-based commercial models in quantum-as-a-service, especially in pharmaceuticals where customers may pay per validated drug candidate rather than per compute hour.
- Cross-sector adoption signals from defence, finance and telecommunications, which are currently the fastest movers on quantum pilots.
Metrics That Matter
- Quantum revenue quality: commercial customers versus government or milestone contracts.
- Qubit count and fidelity: both matter; headline qubit counts alone are not enough.
- Error correction progress: essential for durable quantum advantage.
- Bookings / backlog: especially important for quantum systems and semiconductor equipment.
- Revenue versus cash burn: many names are long-duration R&D stories.
- Photonics production orders: the key test for POET and QCI-style stories.
- Manufacturing scale-up: whether foundries and module production can move beyond prototypes.
Risk Map
- Timeline risk: quantum utility may take longer than public markets want.
- Valuation risk: several companies trade far ahead of current revenue.
- Dilution risk: early-stage quantum and photonics companies need large capital pools.
- Technology risk: qubit modalities, photonic platforms and neuromorphic architectures may not scale as expected.
- Customer concentration: early commercial orders can depend on one or two large customers.
- Geopolitical risk: semiconductors, quantum and cryptography are national-security sensitive.
- Hype risk: quantum is capable of enormous share-price moves before fundamentals catch up.
Convergence
- Computing + AI: photonics, advanced packaging, semiconductor test and edge AI keep AI scaling alive.
- Computing + Cybersecurity: quantum creates post-quantum cryptography demand.
- Computing + Biotech: quantum and AI target drug discovery, protein design and molecular simulation.
- Computing + Energy: AI and quantum data centres need power, cooling and grid infrastructure.
- Computing + Space: secure communications, sensing and radiation-hardened compute.
- Computing + Robotics: neuromorphic chips and edge AI reduce latency and power use in physical systems.
Research Library
Source links used for this first filled-out version:
Summary
Next-Gen Computing is one of the most important sections of the hub, but also one of the easiest places to overpay for narrative. Pure quantum names such as IonQ, D-Wave, Rigetti and QCI define the frontier, but the more investable near-term bottlenecks may sit in photonics, semiconductor test, advanced packaging, embedded edge compute and post-quantum security.
Current working conclusion: IonQ is the category anchor but not a hidden microcap; D-Wave and Rigetti remain important but speculative; QCI and POET are high-upside photonics bets; Aehr is the clearest practical test/burn-in picks-and-shovels name; SEALSQ is a post-quantum security watchlist name; ACM Research belongs in the broader advanced semiconductor infrastructure basket.