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Every year, APS Global Physics Summit brings together scientific community members from around the world across all disciplines of physics.

Join �ԹϺ����� at this year’s conference, taking place in our backyard, Denver, Colorado, from March 15^th – 20^th, where we will showcase how our quantum hardware, software, and partnerships are helping define the next era of high-performance and quantum computing.

Find our team at booth #1020 and join our sessions below to discover how we’re advancing quantum technologies and building the bridge between HPC and quantum.

Monday, March 16^th

Speaker: Andrew Potter
Time: 10:12 – 10:48 am

Speaker: Charles Baldwin
Time: 12:36 – 12:48 pm

High-Fidelity Quantum operations in the Helios Barium-Ion Processor
Speaker: Anthony Ransford
Time: 4:18 – 4:30 pm

Speaker: Jem Guhit
Time: 4:42 – 4:54 pm

Speaker: Enrico Rinaldi
Time: 5:54 – 6:30 pm

Tuesday, March 17^th

Speaker: Caroline Figgatt
Time: 8:00 – 8:12 am

Speaker: Adam Reed
Time: 8:12 – 8:24 am

Speaker: Konstantinos Meichanetzidis
Time: 8:48 – 9:00 am

Speaker: Colin Kennedy
Time: 9:00 - 9:12 am

Speaker: David Stephen
Time: 9:12 - 9:24 am

Speaker: Matthew DeCross
Time: 9:36 - 9:48 am

Speaker: Leigh Norris
Time: 10:00 - 10:12 am

Speaker: Andrew Guo
Time: 12:00 - 12:12 pm

Speaker: Carlo Alberto Gaggioli
Time: 3:30 - 3:42 pm

Wednesday, March 18^th

Speaker: Michael Foss-Feig
Time: 8:36 ��- 9:12 am

Speaker: Michelle Wynne Sze
Time: 9:24 - 9:36 am

Speaker: Juan Pedersen��
Time: 9:48 - 10:00 am

Speaker: Noah Berthusen
Time: 12:48 - 1:00 pm

When is enough enough? Efficient estimation of quantum properties by stopping early
Speaker: Oliver Hart
Time: 12:48 - 1:00 pm

Speaker: John Campora
Time: 1:48 - 2:24 pm

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Speaker: Eli Chertkov
Time: 4:42 - 4:54 pm

Thursday, March 19^th

Speaker: Shival Dasu
Time: 8:00 - 8:36 am

Speaker: Ross Hutson
Time: 8:36 - 8:48 am

Speaker: Victor Colussi
Time: 10:00 am

Speaker: Maxwell Urmey
Time: 12:00 - 12:36 pm

Speaker: Matteo Puviani
Time: 5:54 - 6:06 pm

Friday, March 20^th

Speaker: Reza Haghshenas
Time: 8:36 - 8:48 am

Speaker: Imran Bashir
Time: 10:36 - 10:48 am

Speaker: Fabian Finger
Time: 12:36 - 12:48 pm

Speaker: Elijah Durso-Sabina
Time: 12:48 - 1:00 pm

Speaker: Natalie Brown
Time: 1:12 - 1:48 pm

Speaker: Anthony Ransford
Time: 2:24 - 3:00 pm

*All times in MT

In our latest paper, we’ve taken a big step toward large scale fault-tolerant quantum computing, squeezing up to 94 error-detected qubits (and 48 error-corrected qubits) out of just 98 physical qubits, a low-fat encoding that cuts overhead to the bone. With 64 of our logical qubits, we were able to simulate quantum magnetism at a scale that can be exceedingly difficult for classical computers.

The "holy grail" of quantum computing is universal fault-tolerance: the ability to correct errors faster than they occur during any computation. To realize this, we aim to create “logical qubits,” which are groups of entangled physical qubits that share quantum information in a way that protects it. Better protection leads to lower “logical” error rate and greater ability to solve complex problems.

However, it’s never that easy. An unofficial law of physics is “there’s no such thing as a free lunch”. Creating high quality, low error-rate logical qubits often costs many physical qubits, thus reducing the size of calculations you can run, despite your new, lower-than-ever error rates.

With our , we are thrilled to announce that we have hit a key milestone on the �ԹϺ����� roadmap: an ultra-efficient method for creating logical qubits, extracting a whopping 48 error-corrected and 64 error-detected logical qubits out of just 98 physical qubits. Our logical qubits boasted better than “break-even” fidelity, beating their physical counterparts with lower error rates on several different fronts. And still that isn’t the end of the story: we used our 64 error-detected logical qubits in a large-scale quantum magnetism simulation, laying the groundwork for future studies of exotic interactions in materials.

Stacking Wins

To get this world-leading result, we employed a neat trick: ‘nesting’ super efficient quantum error-detecting codes together to make a new, ultra-efficient error-correcting code. Dr. DeCross, a primary author on the paper, said this nesting is like “braiding together ropes made out of ropes made out of ropes”. ��Physicists call this ‘code concatenation’, and you can think of it as adding layers of protection on top of each other.

To begin, we took the now-famous ‘iceberg code’, a quantum error detection code that gives an almost 1:1 ratio of physical qubits to logical qubits. The iceberg code only detects errors, however, which means that instead of actually correcting errors it lets you throw out bits where errors were detected. To make a code that could both detect and correct errors, we concatenated two iceberg codes together, giving a code that can correct small errors while still boasting a world-record 2:1 physical:logical ratio (physicists call this a “high encoding rate”).

The team then benchmarked the logical qubits, checking large system-scale operations and comparing them to their physical counterparts. This introduces a crucial hurdle to clear: oftentimes, researchers end up with logical qubits that perform *worse* than their physical counterparts. It’s critical that logical qubits actually beat physical ones, after all – that is the whole point!

Thanks to some clever circuit design and our natively high fidelities, the new logical qubits outperformed their physical counterparts in every test we performed, sometimes by a factor of 10 to 100.

Computing Logically

Of course, the whole point is to use our logical qubits for something useful, the ultimate measure of functionality. With 64 error-detected qubits, we performed a simulation of quantum magnetism; a crucial milestone that validates our roadmap.

The team took extra care to perform their simulation in 3 dimensions to best reflect the real-world (often, studies like this will only be in 1D or 2D to make them easier). Problems like this are both incredibly important for expanding our understanding of materials, but are also incredibly hard, as their complexity scales quickly. To make qubits interact as if they are in a 3D material when they are trapped in 2D inside the computer, we used our all-to-all connectivity, a feature that results from our movable qubits.

Maximizing Entanglement

Breaking the encoding rate record and performing a world-leading logical simulation wasn’t enough for the team. For their final feat, the team generated 94 error-detected logical qubits, and entangled them all in a special state called a “GHZ” state (also known as a ‘cat’ state, alluding to Schrödinger’s cat). GHZ states are often used by experts as a simple benchmark for showcasing quantum computing’s unique capacity to use entanglement across many qubits. Our best 94-logical qubit GHZ state boasted a fidelity of 94.9%, crushing its un-encoded counterpart.

Logical Qubits Are the New Normal

Taken together, these results show that we can suppress errors more effectively than ever before, proving that Helios is capable of delivering complex, high-fidelity operations that were previously thought to be years away. While the magnetism simulation was only error-detected, it showcases our ability to protect universal computations with partially fault-tolerant methods. On top of that, the team also demonstrated key error-corrected primitives on Helios at scale.

All of this has real-world implications for the quantum ecosystem: we are working to package these iceberg codes into QCorrect, an upcoming tool that will help developers automatically improve the performance of their own applications.

This is just the beginning: we are officially entering the era of large-scale logical computing. The path to fault-tolerance is no longer just theoretical—it is being built, gate by gate, on Helios.

Japan has made bold, strategic investments in both high-performance computing (HPC) and quantum technologies. As these capabilities mature, an important question arises for policymakers and research leaders: how do we move from building advanced machines to demonstrating meaningful, integrated use?

Last year, �ԹϺ����� installed its Reimei quantum computer at a world-class facility in Japan operated by RIKEN, the country’s largest comprehensive research institution. The system was integrated with Japan’s famed supercomputer Fugaku, one of the most powerful in the world, as part of an ambitious national project commissioned by the New Energy and Industrial Technology Development Organization (NEDO), the national research and development entity under the Ministry of Economy, Trade and Industry.

Now, for the first time, a full scientific workflow has been executed across Fugaku, one of the world’s most powerful supercomputers, and Reimei, our trapped-ion quantum computer. This marks a transition from infrastructure development to practical deployment.

Quantum Biology

In this first foray into hybrid HPC-quantum computation, the team explored chemical reactions that occur inside biomolecules such as proteins. Reactions of this type are found throughout biology, from enzyme functions to drug interactions.

Simulating such reactions accurately is extremely challenging. The region where the chemical reaction occurs—the “active site”—requires very high precision, because subtle electronic effects determine the outcome. At the same time, this active site is embedded within a much larger molecular environment that must also be represented, though typically at a lower level of detail.

To address this complexity, computational chemistry has long relied on layered approaches, in which different parts of a system are treated with different methods. In our work, we extended this concept into the hybrid computing era by combining classical supercomputing with quantum computing.

Shifting the Paradigm

While the long-term goal of quantum computing is to outperform classical approaches alone, the purpose of this project was to demonstrate a fully functional hybrid system working as an end-to-end platform for real scientific applications. We believe it is not enough to develop hardware in isolation – we must also build workflows where classical and quantum resources create a whole that is greater than the parts. We believe this is a crucial step for our industry; large-scale national investments in quantum computing must ultimately show how the technology can be embedded within existing research infrastructure.

In this work, the supercomputer Fugaku handled geometry optimization and baseline electronic structure calculations. The quantum computer Reimei was used to enhance the treatment of the most difficult electronic interactions in the active site, those that are known to challenge conventional approximate methods. The entire process was coordinated through �ԹϺ�����’s workflow system , which allows jobs to move efficiently between machines.

Hybrid Computation is Now an Operational Reality

With this infrastructure in place, we are now poised to truly leverage the power of quantum computing. In this instance, the researchers designed the algorithm to specifically exploit the strengths of both the quantum and the classical hardware.

First, the classical computer constructs an approximate description of the molecular system. Then, the quantum computer is used to model the detailed quantum mechanics that the classical computer can’t handle. Together, this improves accuracy, extending the utility of the classical system.

A Path to Hybrid Advantage

Accurate simulation of biomolecular reactions remains one of the major challenges in biochemistry. Although the present study uses simplified systems to focus on methodology, it lays the groundwork for future applications in drug design, enzyme engineering, and photoactive biological systems.

While fully fault-tolerant, large-scale quantum computers are still under development, hybrid approaches allow today’s quantum hardware to augment powerful classical systems, such as Fugaku, to explore meaningful applications. As quantum technology matures, the same workflows can scale accordingly.

High-performance computing centers worldwide are actively exploring how quantum devices might integrate into their ecosystems. By demonstrating coordinated job scheduling, direct hardware access, and workflow orchestration across heterogeneous architectures, this work offers a concrete example of how such integration can be achieved.

As quantum hardware matures, we believe the algorithms and workflows developed here can be extended to increasingly realistic and industrially relevant problems. For Japan’s research ecosystem, this first application milestone signals that hybrid quantum–supercomputing is moving from ambition to implementation.