#quantumcentricsupercomputing

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IBM quantum centric supercomputing powers next-gen chemistry

IBM quantum centric supercomputing

IBM and RIKEN researchers used the Fugaku supercomputer and a quantum processor to solve complex chemical equations. This cooperation's closed-loop methodology allowed the two systems to work together and share data continuously. Using a hybrid method, the scientists mapped iron-sulfur compound electrical structures with unprecedented precision. This milestone enables real scientific applications by showing that quantum-centric supercomputing can scale. The work shows a shift toward seamless quantum-classical coordination to enhance computational power and efficiency.

Quantum-centric supercomputer chemistry beyond exact solutions

RIKEN and IBM demonstrated quantum-centric supercomputing (QCSC) at a scale never before feasible, advancing HPC. In a collaborative research initiative, the teams coordinated the Fugaku supercomputer, one of the world's most powerful classical systems, with an IBM Quantum Heron processor on-site. This partnership accomplished the largest and most exact quantum chemistry experiment on a quantum computer, marking a turning point in quantum advantage research.

IBM Director of Research Jay Gambetta presented the experiment at Supercomputing Asia 2026 on January 29. It sought to determine two iron-sulfur molecules' complex electrical structure. Since electron distribution and behavior impact a molecule's interactions and reactions with its surroundings, understanding these structures is a basic chemistry challenge. A very exact answer by the study team revealed that quantum and classical resources can be combined in a smooth, “closed-loop” execution to solve issues that are too tough for precise classical procedures.

An Innovative Method: “Closed-Loop” Workflow

This requires creating and executing a closed-loop workflow. This experiment fed data back and forth in an uninterrupted, iterative cycle, unlike conventional hybrid setups, where a quantum processing unit (QPU) finishes a task before sending results back for classical processing. Practical quantum computing in HPC, where real-world applications require close integration of various compute kinds, is more like this orchestration.

Given its enormity, the orchestration is technically complex. Researchers had to build a complicated job assignment method to use the Fugaku supercomputer and Heron processor during computing. Any idle time wastes valuable runtime for other investigations since classical and quantum resources are expensive and valuable. As a billion-dollar machine, Fugaku must maximize its uptime and avoid “sitting around” during quantum steps. The new approach lowered “time-to-solution” by running both computers as near to simultaneously as possible.

SQD: Hybrid Algorithm Power

The discovery was based on hybrid quantum-classical algorithms sample-based quantum diagonalization (SQD). These algorithms divide a problem into conventional and quantum components. The quantum computer unlatches the hardest aspect of the problem, allowing the classical supercomputer to “turn the handle and open the door,” like the “lifting pin in a lockpicking set,” according to the researchers.

Electronic structure simulations have a vast number of electron combinations that grows exponentially with molecular complexity. This vast region was sampled using the IBM Quantum Heron processor in the SQD workflow to identify traditional computer focus areas. With this understanding, Fugaku reached a conclusion. The systems achieved results similar to state-of-the-art classical approximation techniques and far more accurate than quantum approaches when working together.

Heron-Fugaku Hardware Cooperation

This milestone's hardware is cutting-edge conventional and quantum technology. The 2020–2021 world's fastest computer, Fugaku, provided the experiment's classical foundation. The huge device has 158,976 48-core chips. The system revealed future quantum-centric supercomputing architecture when paired with RIKEN's IBM Quantum Heron CPU.

Director of the Quantum-HPC Hybrid Platform Division at the RIKEN Center for Computational Science, Mitsuhisa Sato, called the achievement “very exciting development for hybrid computing”. The process was designed for Fugaku's architecture, but the researchers found it could be utilized in other cloud-based HPC setups. This shows that standard HPC infrastructures worldwide can interface with quantum computers.

Toward future

Despite this success, the route to full-scale quantum advantage continues. The RIKEN-IBM team expects to integrate GPUs as accelerators into quantum-classical processes next. GPUs using hybrid algorithms like SQD may speed up the process, according to recent studies.

Leading RIKEN researcher Tomonori Shirakawa was hopeful about quantum advantage. When asked if RIKEN could reach such a milestone this year, Shirakawa responded that while more work is needed, he is still optimistic about the progress. More people believe high-performance computing will become a cooperative environment where CPUs, GPUs, and QPUs solve humanity's biggest problems.

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IBM Quantum-Centric Supercomputing: Pairing CPU-GPU-QPU

Quantum-centric supercomputing merges CPUs, GPUs, and QPUs into a single infrastructure. Instead of viewing quantum computers as scientific instruments, this approach views the QPU as a specialized accelerator that works with HPC resources to solve previously unsolvable problems.

CPU, GPU, and QPU—the “Compute Trinity”

Since each processor type has unique strengths, researchers call this new design a “Compute Trinity”:

CPUs: The main "brain," these manage serial processes, complex workload orchestration, and logical flow.

GPUs: Originally designed for visuals, GPUs can perform millions of fundamental parallel processes, making them indispensable. They simulate quantum systems and confirm results using Mathematically tensor-heavy multidimensional data structures.

A QPU stores data in quantum states and provides natural access to mathematical operations that would need exponential space on conventional hardware. Simulating a 50-qubit circuit requires 250-entry matrices, which contemporary GPUs cannot manage.

Why Quantum Needs “Classical Exoskeleton”

Quantum computing is hindered by environmental noise, which produces decoherence. For this, researchers use computationally expensive Quantum Error Mitigation and Correction.

GPUs perform laborious operations to “clean” quantum results as the QPU's “classical exoskeleton”. When GPUs replace CPUs, partners like Algorithmiq can invert noise effects 300x faster using tensor-based models. This speed makes verification take an hour instead of a week.

Overcoming Latency

The classical and quantum components must be connected very immediately for a hybrid system to work. QPUs connect to conventional computers via normal, high-latency networks in dilution freezers.

New developments reduce this gap:

NVQLink: A GPU-QPU hardware link with ultra-low latency (less than 4 microseconds).

CUDA-Q: A software platform for closely coupling architectures.

Circuit Knitting divides a huge problem into smaller sections, some of which are addressed by the QPU and others by the GPU, and then they are “knitted” back together to generate the final product.

Real-World Chemistry and Physics Milestones

Many famous partnerships and algorithms have proved the utility of this hybrid model:

Trinity College Dublin, Algorithmiq Simulated chaotic systems with verifiable solutions using “dual unitary circuits”.

The SQD process is important in materials research. Encoding a chemical Hamiltonian into a quantum circuit provides a shortlist of configurations. A GPU diagonalizes the resulting tensors. With this iterative loop, molecular energy configurations can be approximated more precisely than with typical supercomputers.

2030 Roadmap

IBM expects HPC facilities to use quantum-centric supercomputing. As IBM Heron QPUs and Grace Blackwell GPUs disperse workloads like supply chain optimizations and battery chemistry simulations, users may no longer need to physically control the hardware by 2030.

IBM plans fault-tolerant quantum computing systems by the 2020s. External classical resources will solve the world's toughest challenges while internal classical resources will repair errors in real time.

Bridging the “Readiness Gap”

Despite these technological advances, a 2026 study warns of a “Readiness Gap”. Despite fast hardware innovation, many firms lack the skills to implement hybrid workflows. Instead than focusing on quantum circuits, researchers recommend organizations adopt “quantum-plus” thinking and teach developers to coordinate across the computing trinity.

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IBM Quantum Network Enables Quantum Computer Internet

IBM Announces Quantum Networking Project to Create Quantum Computing Internet

IBM Quantum Network

Although an important current goal, fault-tolerant quantum computing is only one part of IBM's future computing strategy. IBM is developing quantum-centric supercomputing, which employs CPUs, GPUs, and QPUs to compute. IBM must network quantum computers to expand beyond its current development strategy.

Distributed System Scaling Beyond the Roadmap IBM's development goal calls for a computer with one billion operations on 2,000 qubits to execute quantum circuits by 2033. Distributed quantum computing with networked systems will expand circuits by orders of magnitude in operations and qubits. The goal is a quantum computer internet beyond this scale.

Collaborations and new research are needed for this future. IBM revealed plans to partner with four of the five NQISR centres to advance research into technologies beyond the development roadmap, such as the quantum computing internet. IBM and Cisco plan to connect quantum processors to prepare for distributed quantum computing.

Quantum computer networking is a challenging task that requires novel research and a structured methodology. The first major milestone in five years will be entangling two cryogenically separated quantum processors, which will require partners.

Quantum Entanglement: Connective Tissue Consider classical networking to understand quantum networking. Traditional computers process binary data using code-changing instructions. Linked classical computers follow cause-and-effect concepts; one waits for a message before acting. Links between computers, or nodes, establish a network like the global internet.

Quantum computers process quantum-encoded information using non-intuitive atomic-scale mathematics. Like conventional systems, quantum computer nodes can create distributed networks that may expand into a quantum computing internet.

The formation of entanglement between linked elements following quantum link activation distinguishes quantum from classical networks. Entangled quantum computers act theoretically as a single entity, not as cause-and-effect machines. Two quantum processors linked across a link can function as a quantum computer over long distances. Operations at one node could quickly modify results at the nodes it is entangled with.

Remember that this connectivity does not mean information moves faster than light. The “quantum no-communication theorem” argues that quantum information collapses into a classical output after measurement, meaning nothing done at node A will immediately affect node B. To understand calculations across nodes, classical outputs must be collected and delivered via a classical link.

Data centres, enhanced sensing Networked quantum computers allow users to build larger quantum datacenters with more qubits and quantum circuits. IBM's Crossbill and Flamingo processors have revealed that modular quantum processors need short-range connections to perform larger quantum operations. Quantum processing units connected over meters-long cables might form a datacenter quantum computing cluster.

Quantum sensing may benefit from networking. Quantum systems make precise measurements. In the search for gravitational waves, LIGO uses quantum sensors to produce extremely accurate observations. A quantum connection linked to a quantum sensor may allow interferometry to create a sensor network and increase accuracy.

QNU and Couplers

This link relies on the Quantum Networking Unit (QNU). QNUs interface processors and interconnects. They turn static qubits encoded on stationary processors into “flying” qubits that can travel across a network. Photons inherently support flying qubits, and the network architecture needed depends on their frequency (microwave or optical).

IBM is scoping a variety of coupling technologies for different purposes, length scales, and issues with partners.

IBM is developing l-couplers internally. To connect QPUs, these are engineered to work in dilution refrigerators at quantum processor temperature. Starling needs high-fidelity l-couplers to provide fault-tolerant quantum computing by 2029.

Medium-Range (One to Ten Meters): IBM and Fermilab's Superconducting Quantum Materials and Systems Centre are studying data transfer connections at greater temperatures. These connectors aim to connect quantum computers in a quantum data centre.

The hardest eyesight component is long-range (kilometres). Kilometer-long networks require a transducer to convert microwave photon energy to optical energy for transmission. To entangle over this distance, more peripherals are needed. IBM and Cisco announced a collaboration to study transducers and optical communications between QPUs to reach the ultimate quantum computing internet vision.

After QNUs that can connect QPUs across short and long distances are built, a true quantum computing internet can network QPUs across kilometres and interact with quantum sensors.

This internet of quantum computers is needed to fully utilize quantum technology. Although the journey will be difficult, IBM is hopeful about its success and the backing of its partners and clients.

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QCSC Quantum Centric Supercomputing by IBM & DOE centers

IBM collaborates with four national quantum innovation centres to advance quantum-centric supercomputing.

IBM will collaborate with four National Quantum Information Science Research Centres (NQISRCs) to boost US quantum computing leadership. This partnership follows the US Department of Energy (DoE)'s announcement today to fund the NQISRCs.

The 2018 National Quantum Initiative Act allowed the DoE to spend $625 million on five quantum information science centres to boost quantum computing research. IBM belongs to four government funded NQISRCs.

Visualising Quantum-Centric Supercomputing IBM is working with the centres to implement quantum-centric supercomputing (QCSC).

QCSC uses CPUs, GPUs, and QPUs in a tightly-coupled architecture to optimise beyond what any single technology can do. Creating a quantum-centric supercomputer requires building a scalable, fault-tolerant quantum computer and establishing the software and infrastructure to integrate quantum into the QCSC computational fabric.

To realise the full potential of quantum technology, QCSC must be unified with quantum sensing and quantum communication. Integrating these pillars may enable a quantum computing internet with multiple quantum processors.

Two Key Exploration Areas

IBM and the four NQISRCs are researching algorithm generation for scientific computing and other practical uses and scaling towards a quantum computing internet to achieve this goal.

Prepare for quantum computing's internet To solve this problem, a unified architecture that seamlessly links processing, communication, and sensing is needed. Initial proof that quantum computing networks are expandable at the metre scale inside a datacenter is crucial.

IBM and SQMS are studying physically connected, deconstructed, and cryogenically preserved IBM quantum computers. IBM is negotiating with Fermi National Accelerator Laboratory's Superconducting Quantum Materials and Systems Centre. SQMS by Fermilab is a global leader in high-quality, scalable microwave cavities and transmission lines.

IBM is launching a Quantum Networking Unit (QNU) to link its quantum computers for extendable microwave-based connection prototyping and research to assist this investigation. The proposed SQMS investigation aims to entangle two IBM quantum computers in cryogenic facilities. In five years, a microwave-based quantum network would connect these devices, demonstrating an integrated data centre.

SQMS will be used by IBM to study quantum workforce development, quantum computing applications for fundamental physics, quantum interconnects, superconducting qubit noise sources, and large-scale cryogenics.

IBM will collaborate with Q-NEXT in Argonne to study future connections between networked quantum computers. This alliance would use optical cables to establish quantum networks with IBM QNUs. An effective microwave-optic transducer, a nonlinear optical device that can transfer microwave photon frequency to the optical domain at a single-photon level, is a critical technological gap being closed. Q-NEXT, which covers hundreds of meters to kilometres, could enable longer lengths than the proposed SQMS program.

Investigating quantum computing methods and applications As quantum computers evolve, algorithm discovery is changing. It's time to confirm when quantum computers can outperform traditional methods and test heuristic algorithms in real life. Classical and quantum computers will be used to solve issues in innovative ways.

QSC partnership: IBM will emphasise algorithms and applications at Oak Ridge National Laboratory's Quantum Science Centre. IBM and QSC want to extend quantum-advantaged use cases and find those with quantum usefulness that exceeds brute-force classical approaches.

New quantum algorithms and advanced error mitigation and correction mechanisms must be implemented to boost quantum computer computing power. The teams are also developing hierarchical quantum error correction decoder algorithms for the next generation of fault-tolerant quantum computers to ensure that quantum technology and high-performance computing coexist in the QCSC architecture. This project enhances the QSC's goal of using quantum computing to study uncommon materials.

IBM also wants to work with Brookhaven National Lab's Co-design Centre for Quantum Advantage on condensed matter and high-energy physics applications. The goal is to create quantum circuits and test physical science questions on hardware.

IBM applauds the DoE for supporting these essential centres as the US pursues quantum-centric supercomputing and maintains its global leadership in quantum computing. IBM expects this continued funding will expedite quantum computing development and create a US quantum computing ecosystem.

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AMD & IBM Join To Develop Quantum Centric Supercomputing

AMD and IBM announced a major collaboration to develop next-generation computer architectures. AMD and IBM will collaborate on “quantum centric supercomputing,” combining HPC with quantum computers.

Jump to Next-Generation Architectures

The two IT giants are creating open-source, scalable platforms that could revolutionise computing. This partnership leverages AMD's high-performance computing and AI accelerator expertise and IBM's quantum computer and software expertise. With this collaboration, IBM hopes to construct fault-tolerant quantum computers that can detect and rectify errors quickly before the end of the decade.

Quantum-centric supercomputing définition

Quantum computing represents and processes data differently than ordinary computers. Quantum computers use qubits, which follow quantum mechanical principles and offer more computation space than classical computers, which use zero or one bits.

In a quantum-centric supercomputing architecture, CPUs, GPUs, and other computation engines work with quantum computers. A hybrid approach lets the best computing paradigm be employed for different aspects of a complex problem. AI-powered supercomputers evaluate large data sets, while quantum computers simulate atom and molecular motion. This collaborative effort seeks real-world solutions at unprecedented scale.

Accelerating Innovation and Discovery

Quantum-centric supercomputing may solve complex difficulties that classical computing cannot. Possible uses include:

Drug discovery

Material discovery

Optimisation

Supply networks and logistics

Financial modelling

ML model training and pattern recognition

AMD and IBM are studying ways to integrate AMD CPUs, GPUs, and FPGAs with IBM quantum computers to speed up a new class of developing algorithms.

Fault-Tolerant Quantum Computing Roadmap

Fault-tolerant quantum computers are essential to this relationship. Real-time error correction is necessary for this goal, and AMD solutions show promise. IBM has worked with RIKEN to connect their modular quantum computer, IBM Quantum System Two, to Fugaku, the world's fastest classical supercomputer. They have also worked with Lockheed Martin, the Basque Government, and the Cleveland Clinic to demonstrate the benefits of combining quantum and classical resources for difficult problems.

Initial Demonstrations and Open-Source Collaboration It will be shown later this year how AMD technology and IBM quantum computers may develop hybrid quantum-classical operations. The companies also want to explore how open-source ecosystems like Qiskit might help create and adopt quantum-centric supercomputing algorithms.

AMD CPUs and GPUs power El Capitan at Lawrence Livermore National Laboratory and Frontier, the first exascale supercomputer at Oak Ridge National Laboratory. TOP500 says AMD powers two fastest supercomputers.

Industry context and commentary

The cooperation promises substantial gains, but some in the industry perceive it as a planned attempt to disrupt Nvidia's dominance, especially in AI, by giving something Nvidia does not currently offer. Also explored are whether additional money for classical algorithm research could improve quick results and whether quantum computers are cheaper than GPUs. IBM is a leader in quantum computing, and such exploration-based operations can lead to positive breakthroughs, despite some questions about its immediate practical value.

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Density Matrix Embedding Theory & SQD for Quantum Modelling

Density Matrix Embed Theory

Combining Quantum Simulation Methods to Address Complex Molecules for Medical Progress

Michigan State University, IBM Quantum, and the Cleveland Clinic proved that hybrid quantum-classical techniques can accurately represent complex molecules using quantum computers, a major quantum chemistry breakthrough. Combining Density Matrix Embedding Theory (DMET) and Sample Based Quantum Diagonalisation (SQD), this groundbreaking finding advances quantum-centric scientific computing and has major health and medical implications.

Unlocked with Hybrid Quantum-Classical Power

Even the most advanced supercomputers have struggled to simulate huge molecule stability and behaviour, which is crucial for disease understanding and treatment. Modern high-performance computers cannot track over 33,000 molecular orbitals, which is needed to simulate insulin. Classical techniques fail as molecules increase larger. In addition, classical mean-field approximations fail to capture crucial electron interactions.

The first implementation of the SQD algorithm on genuine quantum hardware and incorporation into the Density Matrix Embedding Theory framework in this paper offers hope. This unique method, DMET-SQD, could replicate hydrogen rings and cyclohexane conformers. The DMET-SQD method replicates chemically important sections of a molecule instead than the entire molecule, which would need thousands of qubits. These fragments are then combined into an approximate electronic environment.

Quantum-centric supercomputing (QCSC) is defined by this “division of labour” between quantum and classical resources. The quantum processor in QCSC does the most computationally difficult operations, while regular high-performance computers handle the rest, including error correction. This hybrid technique allows state-of-the-art quantum computers without error-correcting capabilities to handle larger and more chemically realistic molecules. The Cleveland Clinic's IBM-managed quantum device, IBM Cleveland, the first of its type in the US, used 27 to 32 qubits in the study.

Real-World Accuracy Benchmarking

The researchers employed DMET-SQD to critically assess their hybrid technique on a ring of 18 hydrogen atoms and different cyclohexane conformers. Hydrogen rings are a standard benchmark in computational chemistry due to their high electron correlation effects, while cyclohexane conformers (chair, boat, half-chair, and twist-boat) are common in organic chemistry due to their narrow energy range, which makes them sensitive to even small computational

Heat-Bath Configuration Interaction (HCI), which may approach exact solutions, and Coupled Cluster Singles and Doubles with perturbative triples [CCSD(T)] were compared to quantum-classical results. The results were promising: DMET-SQD energy differences across cyclohexane conformers were within 1 kcal/mol of the best conventional reference methods, a standard chemical accuracy criterion.

For sufficiently large quantum configuration samples (8,000–10,000), DMET-SQD met HCI benchmarks for the hydrogen ring with negligible deviation while retaining cyclohexane conformer energy ordering. These results demonstrate that the technique may properly model physiologically relevant molecules despite hardware limitations.

Noisy Quantum Hardware Engineering Solutions

Practical usage of this embedding strategy on quantum technologies distinguishes the study. Modern quantum devices are noisy and unreliable. However, SQD's noise tolerance considerably reduced common errors. The authors stabilised calculations on IBM's Eagle processor via gate whirling and dynamical decoupling.

The team designed a custom interface to connect Tangelo's Density Matrix Embedding Theory library to Qiskit's SQD implementation. The simulation uses Hartree-Fock configurations encoded by each quantum circuit to maintain particle number and spin characteristics. These configurations were refined using S-CORE.

Future Outlook and Major Implications

Although the DMET-SQD technique is revolutionary, it has limitations and is still in its infancy. The fragment size and quantum sampling quality affect simulation accuracy. In systems with small energy changes or considerable electrical correlation, poor sampling may cause irregular energy ordering. The present study used a minimum basis set; chemically relevant applications will require more complex basis sets, qubits, and error control.

Study authors say more work is needed to improve sampling and reduce standard post-processing computer demand. Quantum hardware improvements in error rates and gate integrity will be crucial to scaling and enhancing these simulations.

This work suggests that hybrid quantum-classical approaches can handle real chemical processes better than “toy models.” Breaking full-molecule simulations into quantum computer-solvable subproblems allows scientists to solve previously intractable drug development and materials science problems.

Lead author Kenneth Merz, PhD, of the Cleveland Clinic, said, “This is a groundbreaking step in computational research that demonstrates how near-term quantum computers can advance biomedical research.” If they grow, DMET-SQD or similar hybrid techniques may anticipate novel materials, reaction mechanisms, and protein-drug interactions by accurately describing quantum effects in huge molecules. This breakthrough launches a new age of computational study that will directly help disease diagnosis and therapy.

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IBM’s Hybrid Quantum-Classical Method to Open Shell Systems

Open-shell systems

IBM and Lockheed Martin Improve Chemistry Simulation with Classical and Quantum Computing

New research represents complex open-shell molecules used in materials science and aerospace using a hybrid method.

IBM Quantum and Lockheed Martin experts showed how quantum computers can accurately describe electrical systems in The Journal of Chemical Theory and Computation. Some molecules' structures are difficult to duplicate using classical methods. These challenging compounds are called “open-shell” molecules.

This study is the first to employ sample-based quantum diagonalisation (SQD) on open shell systems. IBM researchers believe SQD is a promising candidate for near-term quantum advantage demonstrations because it allows researchers to use high-performance quantum and conventional computers to solve tough simulation challenges.

Quantum Chemistry—Why?

Quantum chemistry has long been considered a promising use of quantum computers. High-performance computers struggle to model transition metals, radical species, transition states, and excited states due to strong electron correlation. The processing cost of precisely simulating even small molecules increases exponentially with the number of interacting electrons, making ideal solutions almost impossible.

Quantum computers offer an alternative. Quantum computer simulations of quantum chemistry can accurately calculate these complex systems' electronic structure and energy. As they acquire fault-tolerance, quantum computers may mimic strongly coupled systems more accurately and at bigger scales than classical approximation methods. In computational chemistry, a singlet-triplet gap is a crucial energy difference between molecule electronic states. These transition energies are essential for understanding chemical reactivity, generating compounds with specific properties, modelling excited-state behaviour in materials, and confirming studies.

Modelling open-shell molecules with unpaired electrons is tricky. Open shell systems are more sophisticated than closed-shell molecules with all electrons linked. High reactivity, magnetic properties, and sophisticated “multi-reference” electronic structures that need several wave functions for correct representation are achievable. These properties are important in atmospheric, catalytic, and combustion chemistry, but modelling is difficult.

Their unpaired electrons create a complicated correlation structure, making radicals and transition metal complexes difficult to manage using classical methods. Quantum algorithms are superior for open shell systems because they naturally encode and process electron entanglement.

Methylene (CH2): A Key Test Case

Most recent research focused on methylene (CH2). Atmospheric chemistry, combustion emissions, and interstellar activities depend on CH2, a three-atom reactive molecule.

Electronically, CH2 has a diradical triplet ground state. This state gives the carbon atom a spin of one due to its outer shell of two unpaired electrons with parallel spins. CH2's stable, low-energy triplet form is interesting. The initial excited state is a carbene singlet state with opposing spins on the carbon electrons.

The singlet-triplet energy gap, the energy difference between these states, must be accurately predicted to understand CH2's behaviour in complex chemical processes. Classical methods often struggle to estimate this gap in open-shell compounds like CH2.

Quantum computers can simulate CH2 and other radical species' reactivity because they can manage complex wavefunctions and electron correlations.

Study Results

Using an IBM quantum processor and SQD, the researchers determined CH2's singlet and triplet dissociation energies and energy gaps. This study legitimises quantum approaches for molecules with complex electronic structures, such as carbenes and radicals, by applying the SQD method to an open-shell system for the first time.

Compared to high-accuracy classical techniques (Selected Configuration Interaction, or SCI), studies showed:

Singlet dissociation energy is strongly agreed upon within a few milli Hartrees of the SCI reference.

Near equilibrium geometries have consistent triplet energies. accurate singlet-triplet energy gap prediction that matches experiment and classical modelling.

The computations used a 52-qubit IBM quantum processor and up to 3,000 two-qubit gates per experiment.

Quantum-focused supercomputing

IBM's quantum-centric supercomputing infrastructure, which blends quantum computers with regular computing resources, hosted the experiment. This method allows scalable molecular system simulations.

This study reveals that quantum computers are beginning to succeed in chemical simulations. Radical molecules are crucial in sensor design, combustion chemistry, and aerospace, and simulating their behaviour can improve predictive models and new technologies. As quantum hardware and SQD improve, this strategy allows quantum tools to model complex reaction dynamics and develop superior materials.