Quantum gravity in the lab
In the world of physics, ideas can lie dormant for decades before revealing their true power. What begins as a quiet paper in an academic journal can eventually reshape our understanding of the universe itself.
In 1993, nestled deep in the halls of Yale University, physicist Subir Sachdev and his graduate student Jinwu Ye stumbled upon such an idea. Their work, originally aimed at unraveling the mysteries of “spin fluids”, would go on to ignite one of the most surprising and profound connections in modern physics—a bridge between the strange behavior of quantum materials and the warped spacetime of black holes.
Two decades after the paper was published, it would be pulled into the orbit of a radically different domain: quantum gravity. Thanks to work by renowned physicist Alexei Kitaev in 2015, the model found new life as a testing ground for the mind-bending theory of holography—the idea that the universe we live in might be a projection, from a lower-dimensional reality.
Holography is an exotic approach to understanding reality where scientists use holograms to describe higher dimensional systems in one less dimension. So, if our world is 3+1 dimensional (3 spatial directions plus time), there exists a 2+1, or 3-dimensional description of it. In the words of Leonard Susskind, a pioneer in quantum holography, "the three-dimensional world of ordinary experience—the universe filled with galaxies, stars, planets, houses, boulders, and people—is a hologram, an image of reality coded on a distant two-dimensional surface."
The “SYK” model, as it is known today, is now considered a quintessential framework for studying strongly correlated quantum phenomena, which occur in everything from superconductors to strange metals—and even in black holes. In fact, The SYK model has also been used to study one of physics’ true final frontiers, quantum gravity, with the authors of the paper calling it “a paradigmatic model for quantum gravity in the lab.”
The SYK model involves Majorana fermions, a type of particle that is its own antiparticle. A key feature of the model is that these fermions are all-to-all connected, leading to strong correlations. This connectivity makes the model particularly challenging to simulate on classical computers, where such correlations are difficult to capture. Our quantum computers, however, natively support all-to-all connectivity making them a natural fit for studying the SYK model.
Now, 10 years after Kitaev’s watershed lectures, we’ve made new progress in studying the SYK model. In a new paper, we’ve completed the largest ever SYK study on a quantum computer. By exploiting our system’s native high fidelity and all-to-all connectivity, as well as our scientific team’s deep expertise across many disciplines, we were able to study the SYK model at a scale three times larger than the previous best experimental attempt.
While this work does not exceed classical techniques, it is very close to the classical state-of-the-art. The biggest ever classical study was done on 64 fermions, while our recent result, run on our smallest processor (System Model H1), included 24 fermions. Modelling 24 fermions costs us only 12 qubits (plus one ancilla) making it clear that we can quickly scale these studies: our System Model H2 supports 56 qubits (or ~100 fermions), and Helios, which is coming online this year, will have over 90 qubits (or ~180 fermions).
However, working with the SYK model takes more than just qubits. The SYK model has a complex Hamiltonian that is difficult to work with when encoded on a computer—quantum or classical. Studying the real-time dynamics of the SYK model means first representing the initial state on the qubits, then evolving it properly in time according to an intricate set of rules that determine the outcome. This means deep circuits (many circuit operations), which demand very high fidelity, or else an error will occur before the computation finishes.
Our cross-disciplinary team worked to ensure that we could pull off such a large simulation on a relatively small quantum processor, laying the groundwork for quantum advantage in this field.
First, the team adopted a randomized quantum algorithm called TETRIS to run the simulation. By using random sampling, among other methods, the TETRIS algorithm allows one to compute the time evolution of a system without the pernicious discretization errors or sizable overheads that plague other approaches. TETRIS is particularly suited to simulating the SYK model because with a high level of disorder in the material, simulating the SYK Hamiltonian means averaging over many random Hamiltonians. With TETRIS, one generates random circuits to compute evolution (even with a deterministic Hamiltonian). Therefore, when applying TETRIS on SYK, for every shot one can just generate a random instance of the Hamiltonain, and generate a random circuit on TETRIS at the same time. This simple approach enables less gate counts required per shot, meaning users can run more shots, naturally mitigating noise.
In addition, the team “sparsified” the SYK model, which means “pruning” the fermion interactions to reduce the complexity while still maintaining its crucial features. By combining sparsification and the TETRIS algorithm, the team was able to significantly reduce the circuit complexity, allowing it to be run on our machine with high fidelity.
They didn’t stop there. The team also proposed two new noise mitigation techniques, ensuring that they could run circuits deep enough without devolving entirely into noise. The two techniques both worked quite well, and the team was able to show that their algorithm, combined with the noise mitigation, performed significantly better and delivered more accurate results. The perfect agreement between the circuit results and the true theoretical results is a remarkable feat coming from a co-design effort between algorithms and hardware.
As we scale to larger systems, we come closer than ever to realizing quantum gravity in the lab, and thus, answering some of science’s biggest questions.
About Quantinuum
Quantinuum, the world’s largest integrated quantum company, pioneers powerful quantum computers and advanced software solutions. Quantinuum’s technology drives breakthroughs in materials discovery, cybersecurity, and next-gen quantum AI. With over 500 employees, including 370+ scientists and engineers, Quantinuum leads the quantum computing revolution across continents.
Progressing your organization along the five levels of quantum maturity
Quantum computing has moved from a bet on the future to a race already underway. Early adopters are locking in strategic partnerships, building proprietary IP, and positioning themselves years ahead of competitors who are still watching from the sidelines. For executives, the question isn't whether to engage with quantum computing. It's how far along that journey your organization actually is, and what it takes to move forward.
That's a harder question to answer than it sounds. Quantum maturity isn't a single milestone you either hit or miss. It's a progression, built across talent, technology access, workflow integration, partnerships, and value realization, and most organizations aren't entirely sure where they currently stand and what to do next.
Our new paper, A Roadmap for Quantum Maturity, is built to answer exactly that.
A Framework for Where You Stand, and Where to Go Next
Drawing on extensive client experience, the paper lays out five distinct levels of quantum maturity, from early awareness through full transformation, along with the leadership actions that move an organization from one level to the next.
- Awareness — early conversations, but no clear ownership or use cases yet
- Exploration — exploring partnerships and prioritizing use cases with limited budget
- Experimentation — quantum roadmap established and guiding dedicated teams, funding, and partners to execute pilot use case projects
- Integration — quantum computing applications are being embedded into business unit workflows, and the quantum roadmap is integrated with broader digital technology strategy, including AI, HPC, and data
- Transformation — quantum capabilities are embedded in core products and decision-making, with differentiated, proprietary advantage
Most industry leaders today sit at the exploration or experimentation stages, with clear ambitions to reach transformation within the next several years. The paper breaks down what separates organizations that progress from those that stall out at proof-of-concept.
The Advantage Is Built, Not Bought
One of the paper's central takeaways is one many executives underestimate: investing in quantum technology alone isn't enough. Organizations that advance fastest pair that investment with a deliberate strategy, building quantum literacy across leadership and technical teams, honestly assessing capability gaps, focusing on a small number of high-impact use cases tied to real business metrics, and defining a clear roadmap that connects research to business advantage.
How Quantinuum Can Help
Achieving quantum maturity is a journey, not a single step, and most organizations don't need to make that journey alone. Quantinuum's consulting services are built to support every stage of it, from advisory and use-case identification, to capability building, technology access, and the co-development of scalable quantum solutions.
Whether your organization is just starting to build awareness or already scaling toward transformation, our team can help you identify exactly where you stand today, and what it takes to move to the next level.
- SoftBank Corp. and Quantinuum articulated a roadmap connecting quantum algorithms, hardware evolution, and commercial applications, helping organizations understand when quantum computing can address real-world challenges.
- The analysis demonstrates that quantum value will emerge in stages, with early opportunities in data analysis and longer-term breakthroughs in scientific computing as fault-tolerant systems mature.
- The work provides a practical planning framework for enterprises preparing to integrate quantum computing alongside AI and classical high-performance computing infrastructure in quantum-AI data centers.
Quantum computing is increasingly moving from exploratory discussion to structured enterprise planning. As organizations begin to assess where and when quantum technologies may deliver real business impact, a new class of work is emerging: integrated roadmaps that connect algorithmic feasibility, hardware development, and commercial opportunity.
A recent white paper from SoftBank Corp. and Quantinuum represents one of the most comprehensive examples of this approach to date. Rather than treating quantum computing as a distant, abstract capability, the study constructs a detailed, quantitative framework for understanding how real-world use cases evolve as hardware matures—and what this means for enterprise strategy.
A Comprehensive Roadmap
The SoftBank–Quantinuum white paper is broad in scope. It attempts to answer a fundamental question:
Which real-world problems can benefit from quantum computation, at what scale, with what accuracy requirements, and under what hardware conditions?
To address this, the study adopts a structured methodology that connects:
- Algorithmic resource estimates (logical qubits, circuit depth, gate counts)
- Explicit error correction assumptions and overhead models
- Hardware roadmaps across multiple generations
- Timelines for when specific classes of problems may become feasible
Two representative domains anchor the analysis:
- Quantum chemistry, particularly excited-state dynamics and photochemical processes
- Topological data analysis (TDA), including graph-based structure and anomaly detection
These domains were selected because they combine industrial relevance with computational structures that scale poorly on classical systems but map naturally onto quantum approaches.
Quantum chemistry is closely tied to materials science, energy systems, and the development of sustainable technologies. TDA, by contrast, offers tools for understanding complex data structures in networks, finance, and large-scale systems—where identifying structure and anomalies is increasingly critical.
Together, they illustrate how quantum computing may create value across both deep scientific domains and high-impact data applications.
An important caveat is that the resulting roadmap assumes a widely-studied but inefficient error correcting code. As more error correcting codes come online, the resources required to run algorithms will shrink. That means that the timelines detailed in this work can be thought of as “worst case” scenarios, which adds to the value by setting out a clear limit.
A distinguishing feature of the work is its emphasis on implementation over abstraction. Rather than relying solely on theoretical models, the study explicitly constructs quantum circuits and executes them on Quantinuum’s Helios and H2 system.
Parallel Paths to Value
A notable insight from the study is that quantum value creation will not follow a single linear path.
Instead, two complementary regimes are expected to emerge:
- In quantum chemistry, the focus is on fault-tolerant, logical-qubit-based computation, where error correction is essential for scaling toward meaningful scientific and industrial outcomes.
- In TDA, value can emerge earlier, where partial quantum advantage may already support useful computational tasks without full error correction.
This dual-track structure is important: it shows that quantum computing is not a single “threshold technology,” but a spectrum of capabilities that unlock value at different stages of maturity.
The Vision for Quantum Industrialization
The framework ultimately supports a broader strategic vision: the evolution of Quantum AI Data Centers—hybrid infrastructures where quantum processors operate alongside AI and classical HPC systems.
For enterprises, the implication is clear: quantum computing readiness is no longer about speculation. It is about structured preparation, disciplined modeling, and early engagement with the full stack of capabilities that will define the next generation of computational infrastructure.
Every year, The IEEE International Conference on Quantum Computing and Engineering – or IEEE Quantum Week – brings together engineers, scientists, researchers, students, and others to learn about advancements in quantum computing. This year’s conference, from September 13th - 18th in Toronto, Canada, will focus on translating research into real-world impact through the convergence of generative AI, distributed quantum systems, and quantum software engineering.
Throughout IEEE Quantum Week, our quantum experts will be on-site to share insights on upgrades to our hardware, enhancements to our software stack, our path to error correction, and more.
Meet our team at Booth #501 and join the below sessions to discover how Quantinuum is forging the path to fault-tolerant quantum computing with our integrated full-stack.
Keynote with Quantinuum's CEO, Dr. Rajeeb Hazra
September 14th
5:00 – 6:30pm | 800 Hall G
Quantum computing has passed the point where error correction is theoretical. What comes next depends on systems that hold logical performance steady and do real work at scale. On September 14th, join Quantinuum’s CEO Dr. Rajeeb Hazra for his keynote session on “Logically Speaking: The Next Era of Error Correction” where he will explore what the next era of quantum computing requires: shared definitions of logical performance, and benchmarks built on real workloads.
Speaking Sessions
September 13th
11:00 – 11:12am | Location: 601A/B
Workshop: QGenAI: Synergies between Quantum Computing and Generative Artificial Intelligence
Finding Compatible Datasets for Quantum Generative Modeling
Presenting Author: Chen-Yu Liu
September 14th
1:00 – 1:20pm | Location: 701B
Workshop: Q3-Control: Integrated Systems for Quantum Computing, Sensing, and Networking
Cryo-ASICs for Scalable Control
Speaker: Dr. Patty Lee
September 15th
2:30 – 3:30pm | Exhibit Hall (informal event)
Workshop: Q3-Control: Integrated Systems for Quantum Computing, Sensing, and Networking
The Quantum Spectrum
Speaker: Dr. Patty Lee
3:00 – 4:30pm | Location: 801A
Where Quantum-HPC Integration Actually Stands in 2026
Panelist: Neal Erickson
3:00 – 4:30pm | Location: 718A
From Research to Commercialization: Defining the Quantum Workforce for the Next Five Years
Panelist: Kortny Rolston-Duce
September 16th
10:00 – 11:30am | Location: 801B
Who Will Turn Quantum Computing into Value? Specialists, Domain Experts, and the Workforce Gap
Panelist: Enrico Rinaldi
September 17th
10:00 – 11:30am | Location: 701B
International Workshop on Quantum Computing for Power Systems: From Optimization Algorithms to Grid-Scale Applications | Session 1
Organizer: Kortny Rolston-Duce
10:00 – 11:30am | Location: 714A
openQSE: Co-Designing the Quantum-HPC Software Stack from Applications to Control Systems | Session 1
Speaker: Neal Erickson
10:00 – 11:30am
The Impact of Qubit Connectivity on Quantum Advantage in Noisy IQP Circuits
Presenting Author: Leonardo Placidi
10:15 – 11:00 am | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Automated near-term quantum algorithm discovery
Speaker: Konstantinos Meichanetzidis
11:00 – 11:15am | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Fast Stabilizer State Preparation via AI-Optimized Graph Decimation
Presenting Author: Jasmine Brewer
1:00 – 1:15pm | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes
Speaker: Gabriel Matos
1:00 – 1:20am | 718B
Quantum Software 2.6: current challenges and headways in quantum software | Session 1
Quantum compilation and hybrid compilation
Speaker: Ross Duncan
1:00 – 2:30pm | Location: 714A
openQSE: Co-Designing the Quantum-HPC Software Stack from Applications to Control Systems | Session 2
Speaker: Phillipp Seitz
1:45 – 2:00pm | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Graph-Theoretic Quantum Circuit Optimization with the ZX-Calculus and Gumbel AlphaZero
Speaker: Alexander Koziell-Pipe
2:00 – 2:15pm | Location: 601A/B
Workshop: AI for Circuit Synthesis, Optimization, and Discovery
Reusable Equivariant Neural Compilers for Matrix-Group Quantum Circuit Synthesis
Speaker: Richie Yeung
3:30 - 4:00pm | Location: 715B
Workshop: 4th Workshop on Quantum Computing for Natural Sciences: Technology and Applications
Quantum Algorithms
Speaker: Setso Metodi
September 18th
10:00 – 11:30am | Location: 801B
Scalable QPU architectures
Panelist: Setso Metodi