Quantum annealing in 2026: overview
Quantum annealing encodes an optimization problem in an energy landscape and uses a quantum system to seek low-energy solutions. It is particularly suited to problems that can be expressed as Ising models or QUBO formulations. The approach is drawing interest for hard combinatorial tasks, but its practical value depends on how well problems map to hardware and how results compare with strong classical methods.
Recent work is extending the field beyond standard optimization while confronting hardware limits. In October 2026, Keio University researchers examined quantum annealing correction with additional inter-replica interactions; a September 2026 study from Goethe University Frankfurt and Forschungszentrum Jülich investigated reverse annealing for harder problems. August 2026 research explored machine learning with annealer dynamics and QUBO-based material topology optimization. At the same time, a Flatiron Institute study reported in August 2026 found classical tensor-network simulation more accurate in many cases, keeping credible benchmarking and scaling advantage central open questions.
- 1Beyond optimization: Recent work applies annealer dynamics to machine learning and explores QUBO formulations for material design, widening the potential use cases.
- 2Hardware remains a constraint: Noise, limited energy gaps and problem-mapping demands continue to restrict solution quality as optimization problems grow more complex.
- 3Benchmarking matters: Classical simulation results reinforce the need to demonstrate where annealers provide useful performance, rather than assume an advantage.
Top organizations working on quantum annealing
Ranked by Linknovate activity score · last 24 monthsThe activity score adds up the relevance of every patent, publication, grant and news item linking an organization to quantum annealing. Showing 7 of 333 organizations.
| # | Organization | Approach | Signals (24 mo) | Momentum | Why it matters |
|---|---|---|---|---|---|
| 1 | 
D WAVE
Corporate · Canada |
Transverse-field annealing | NEWS 107 | -35% | D-Wave builds quantum annealing processors and hybrid solvers for optimization and sampling, and has reported using its processors for physics simulations. |
| 2 | 
Tohoku University
Research · Japan |
General | PAT 1 · PUB 35 NEWS 2 |
+24% | Tohoku researchers apply quantum annealing to optimization and machine learning. Recently, they explored multi-FPGA acceleration of simulated annealing for automated-guided-vehicle route optimization. |
| 3 | 
Google
Corporate · United States |
General | PAT 1 · NEWS 120 | -54% | Google has patented hardware designs for robust quantum annealing, including quantum units and couplers configured to implement a quantum Hamiltonian. |
| 4 | 
Keio University
Research · Japan |
General | PUB 22 · NEWS 1 | -36% | Keio researchers study ways to improve quantum annealing on constrained and noisy hardware, including spin-fixing methods and quantum annealing correction. |
| 5 | 
Kipu Quantum
Startup · Germany |
Hybrid quantum-classical solvers | PAT 6 · PUB 1 NEWS 46 |
-11% | Kipu Quantum is developing optimization methods for analog quantum computers, including a variational coherent quantum annealing algorithm and a PINN-based approach. |
| 6 | 
NEC Corp
Corporate · Japan |
General | PAT 8 · PUB 4 NEWS 12 |
-59% | NEC patents systems for adjusting quantum-annealing parameters, including annealing time and variable symmetry, to support combinatorial optimization. |
| 7 | 
Jülich Research Center
Research · Germany |
Reverse annealing | PUB 14 · NEWS 4 | -62% | Jülich researchers investigate quantum annealing performance and sampling, including how annealing schedules and reversal parameters affect D-Wave reverse annealing. |
| 8 | ··Hidden organizationCompany | Approach | PAT 00 · PUB 00 GRANTS 0 · NEWS 00 | +00% | Available to registered users. |
| 9 | ··Hidden organizationCompany | Approach | PAT 00 · PUB 00 GRANTS 0 · NEWS 00 | +00% | Available to registered users. |
| 10 | ··Hidden organizationCompany | Approach | PAT 00 · PUB 00 GRANTS 0 · NEWS 00 | +00% | Available to registered users. |
Startups to watch and leading research centers
Startups to watch 10 total
Multiverse ComputingSpainMultiverse Computing patents methods for securing communications during quantum-annealing and QUBO routines, and for generating random numbers using quantum annealers.
Qilimanjaro Quantum TechSpainQilimanjaro develops analog quantum computers and has patented a method for performing quantum annealing.
QuEra ComputingUnited StatesQuEra develops neutral-atom quantum computers and participates in research on hybrid quantum-classical algorithms for combinatorial optimization.- ··Hidden startupCountryAvailable to registered users, with funding and founding year.
- ··Hidden startupCountryAvailable to registered users, with funding and founding year.
Leading research institutions 267 total
- Tohoku UniversityJapanTohoku researchers apply quantum annealing to optimization and machine learning. Recently, they explored multi-FPGA acceleration of simulated annealing for automated-guided-vehicle route optimization.
- Keio UniversityJapanKeio researchers study ways to improve quantum annealing on constrained and noisy hardware, including spin-fixing methods and quantum annealing correction.
- Jülich Research CenterGermanyJülich researchers investigate quantum annealing performance and sampling, including how annealing schedules and reversal parameters affect D-Wave reverse annealing.
- ··Hidden research institutionCountryAvailable to registered users, with their key researchers.
- ··Hidden research institutionCountryAvailable to registered users, with their key researchers.
Where quantum annealing innovation happens
Share of documents with at least one organization from each country.
Open the interactive map with all 333 organizationsMain technical approaches
Last 24 months| Approach | What it is | Documents | Leading organizations |
|---|---|---|---|
| Transverse-field annealing | Uses a transverse field to drive transitions between states; performance depends on the schedule and the hardware’s ability to preserve useful quantum dynamics. | 87 |
Multiverse Computing, Michigan State University, University College London |
| Reverse annealing | Starts from a candidate solution and partially reverses the annealing schedule to refine it; results depend on schedule design and parameter choices. | 41 |
Jülich Research Center, Caidas, Rheinisch-Westfaelische Technische Hochschule Aachen |
| Non-stoquastic annealing | Introduces interactions beyond conventional stoquastic models that may help address hard instances, but requires specialized hardware and careful validation. | 7 |
University College London, University of Southern California, Tel Aviv University |
| Digital annealing | Uses digital or simulated hardware to reproduce annealing-style optimization; it offers implementation flexibility but does not necessarily provide quantum dynamics. | 207 |
D WAVE, Quantum Wave Pty Ltd, Microsoft |
| Hybrid quantum-classical solvers | Combines annealing hardware with classical routines to handle problem preparation and refinement; performance depends on effective decomposition and coordination. | 233 |
IBM, Google, Microsoft |
Emerging trends
Last 12 months vs previous 36 monthsTerms whose frequency in the last 12 months grew the most compared with the previous 36 months.
Funding and investment
Recent funding rounds
FACTS records $46,500,985 across 50 grants since 2020. Funding-round entries include unrelated activity, so the grant total is the clearer indicator of tracked research support.
Patent landscape
Top patent holders · last 24 monthsRecent developments
Latest news, duplicates removed- Aug 2026Researchers at the Flatiron Institute in New York City have used a new tensor network (TN) scheme to simulate how Ising spin glasses evolve in time. In many cases, their classical method is more accurate than the latest…Physics World
- Aug 2026D-Wave is a bit of an oddity in the quantum computing space, having been founded back in the last century. And its initial offering wasn’t a quantum computer like those being developed by IBM or Google. Instead, the…Ars Technica
- Jul 2026How even small but frequent disruptions can cause quantum computers to fail. Quantum computers are expected to tackle difficult tasks more quickly and with less energy than current supercomputers, including molecular…SciTechDaily
- Jul 2026Jul 24, 2026 When quantum computers freeze: How even minor, yet frequent, disruptions can cause quantum computing to fail As adiabatic quantum computers scale, environmental disturbances may trigger the quantum Zeno…Nanowerk Nanotechnology and Emerging Technologies News
- Month 20XXHidden developmentAvailable to registered users with alerts enabled.
Frequently asked questions about quantum annealing
What companies are working on quantum annealing?
D WAVE, Google, NEC Corp, Fujitsu Limited and Kipu Quantum are among the organizations active in quantum annealing. D WAVE builds annealing processors and hybrid solvers for optimization and sampling. NEC patents parameter-adjustment systems, while Kipu Quantum develops optimization methods for analog quantum computers, including hybrid approaches.
Which quantum annealing startups are the most promising in 2026?
Multiverse Computing, Qilimanjaro Quantum Tech and QuEra Computing are identified as startups active in this area. Multiverse patents methods related to quantum-annealing routines and random-number generation; Qilimanjaro develops analog quantum computers and has patented an annealing method. QuEra works on neutral-atom systems and hybrid algorithms. These examples reflect different parts of the technology stack, not a ranking.
Which universities and research centers lead quantum annealing research?
Tohoku University, Keio University and Jülich Research Center are active research organizations in quantum annealing. Tohoku researchers work on optimization and machine learning applications; Keio studies error correction and constrained, noisy hardware; Jülich investigates reverse annealing schedules and sampling performance. Other active groups include Goethe University Frankfurt and University of Southern California.
How much funding has quantum annealing received?
FACTS records $46,500,985 across 50 grants since 2020. This figure represents grants associated with the topic, not a complete measure of investment: funding-round records may include organizations or activity unrelated to quantum annealing. The available data does not provide a clean, topic-specific total for private investment.
Who holds the most quantum annealing patents?
NEC Corp leads the listed patent holders with 8 patents, followed by Multiverse Computing with 7 and Kipu Quantum with 6. Unisys Corporation has 5, while the French National Center for Scientific Research and Sorbonne Polytechnic University each have 4. These counts describe the patent-holder data in FACTS.
Is quantum annealing patent activity growing?
No: FACTS records 79 patents in the 24-month window and a patent-growth change of -68.3%. This contrasts with 446 publications in the last year, up 8.5%. The figures suggest that recent activity is stronger in research publications than in patenting, though they do not by themselves explain why the trends differ.
What are the latest developments in quantum annealing?
Recent work includes Keio University’s October 2026 study of quantum annealing correction with additional inter-replica interactions and a September 2026 study of reverse annealing by Goethe University Frankfurt and Forschungszentrum Jülich. August 2026 publications also explored annealer dynamics for machine learning and QUBO-based multi-material topology optimization.
Which countries lead quantum annealing?
The United States leads the country data with 1,730 documents, or 42.4% of the total. Japan follows with 786 documents, then Canada with 658 and the United Kingdom with 524. These figures indicate research and innovation activity in the dataset; they do not measure the commercial performance of each country’s quantum annealing sector.
What are the applications of quantum annealing?
FACTS identifies logistics and scheduling, finance, chemistry and materials, machine learning, and telecommunications and networks as application areas. Logistics and scheduling has 138 documents, chemistry and materials 75, and machine learning 85. Recent examples include automated guided vehicle routing, multi-material topology optimization and wireless sensing optimization.
What are the main approaches in quantum annealing and how do they differ?
The main approaches in FACTS are transverse-field annealing, reverse annealing, non-stoquastic annealing, digital annealing and hybrid quantum-classical solvers. Transverse-field methods drive transitions through a changing field; reverse annealing refines a starting candidate; non-stoquastic methods use different interactions. Digital annealing is simulated or digital, while hybrid solvers combine quantum and classical processing.