#hybridquantum

D-Wave Announces Qubits 2026 Quantum Computing User Conference

Quantum computing equipment, software, and services leader D-Wave Quantum Inc. (NYSE: QBTS) will hold its annual Qubits user conference in Boca Raton, Florida, on January 27 and 28, 2026. The first commercial quantum computing company is D-Wave.

The event highlights client success stories and D-Wave's award-winning quantum technology's capacity to solve problems beyond classical computing. Participants will hear from scientists, industry leaders, D-Wave executives, and customers.

At Qubits 2026, the company will unveil its latest product roadmap and advances in hybrid-quantum solvers, quantum AI, annealing, and gate-model quantum computing. The symposium will highlight the company's energy-efficient annealing quantum computers and hybrid-quantum solvers' demonstrable impact. D-Wave is the sole gate-model and annealing quantum computer developer.

D-Wave CEO Dr. Alan Baratz says the company 'continues to set the pace in quantum computing, delivering tangible effect for customers today while pushing technologies that will define the future'. He said Qubits 2026 will bring together the company's inventors and problem-solvers to highlight how “quantum’s promise is starting to transform the world”.

Program highlights and topics

One of the year's biggest quantum computing conferences, Qubits 2026, will bring together experts from around the world to discuss the fast-growing field. Case studies, live demos, and in-depth seminars will be supplemented by technical and business tracks.

Important topics will be covered:

D-Wave's strategy to outperform conventional systems and provide long-term and short-term customer value with scalable annealing, gate-model, and hybrid-quantum computing technologies. The company's superconducting quantum technology is famous. Hybrid-quantum technology's potential as an energy-efficient AI workflow accelerator.

Customer use cases showing how D-Wave technology solves computationally difficult problems in various industries. Supply chain, manufacturing, aerospace, life sciences, and AI are examples.

Applications in government and defence, emphasising quantum's role in radar detection, resource deployment, and logistics optimisation.

In modern HPC facilities, on-site quantum computing is becoming more important. D-Wave's Leap quantum cloud service or on-site quantum computers are the world's largest. Practical advise on launching quantum projects, including starting today with the D-Wave Launch program and its professional services staff. Customers use the company's quantum systems to tackle research, AI, and optimisation problems with over 200 million issues filed. Ocean SDK users can use the Ocean collection of open-source tools to construct quantum systems quickly. D-Wave Launch helps businesses implement quantum computing.

Important Speakers

Notable companies expected to present include Davidson Technologies, Lighthouse DIG, North Wales Police, Anduril, AT&T, and Unissant. Experts from Florida Atlantic University, Los Alamos National Laboratory, and Jülich Supercomputing Centre will also present.

Jeffrey “Skunk” Baxter, a Rock & Roll Hall of Famer and US government defence expert, will give the conference's keynote address. Baxter will discuss national security, technology, and music innovation.

QiliSDK, an open-source toolkit that unifies hybrid digital-analog quantum workflows, is released by Qilimanjaro.

Qilimanjaro Quantum has announced a significant expansion of its product line with the release of QiliSDK, an open-source Python framework designed specifically to integrate digital, analogue, and hybrid quantum algorithm generation. An important step that shows Qilimanjaro's commitment to providing adaptable, full-stack tools required to achieve practical quantum computing is this modular software development kit (SDK). Accelerating algorithm development and moving from theoretical concepts to simulation and, ultimately, real hardware implementation are the objectives of the QiliSDK launch.

The QiliSDK is the primary entry point to Qilimanjaro's cutting-edge multimodal quantum data centre. The distinctive feature of this data centre is that it is the first in the world to combine analogue QPUs, superconducting digital QPUs, and traditional High-Performance Computing (HPC) accelerators into a single, integrated system. The SDK gives users direct access to the heart of Qilimanjaro's infrastructure.

A Single Platform for Streamlining Workflow

With the integration enabled by QiliSDK, researchers and developers may effectively prototype, implement, and optimise quantum workflows on both simulation environments and real hardware. In addition to more conventional systems like CPUs and GPUs, the SpeQtrum platform offers access to a range of supported execution environments, including Qilimanjaro's own superconducting QPUs.

One of QiliSDK's primary benefits is its modular architecture. The SDK is easily integrated with a variety of quantum platforms due to its fully backend-agnostic design. This adaptability is further increased by high-level abstractions for crucial quantum elements including gates, circuits, Hamiltonians, and optimisers. Additionally, by supporting complex concepts like Pauli algebra, variational programs, and integrated visualisation tools, the SDK's expressive syntax makes the entire quantum development cycle easier.

David Arcos, Director of Software at Qilimanjaro Quantum Tech, highlighted the main goal of the new toolkit. He stated that the company's goal with QiliSDK is to make it easier to design quantum algorithms in all paradigms, including hybrid, circuit-based, and Hamiltonian-based ones. Customers can "move effortlessly from simulation to real hardware with complete transparency and control" using the tools, Mr. Arcos emphasised.

Among the main features included in QiliSDK are:

A single quantum workflow API that manages digital, analogue, and hybrid processing.

Coherent backend switching capabilities allow switching between CPU, GPU, and QPU devices using integrated backends such as the CudaBackend and Qilimanjaro SpeQtrum devices.

A variational program's interface designed specifically for hybrid quantum–classical optimisation.

A model toolbox for complex mathematical models and optimisation.

Integrating visualization tools for scheduling and integrated circuits.

Innovative Co-Design of Analogue

Qilimanjaro keeps moving forward with the objective of developing a practical quantum advantage with its own analogue co-design approach. The QiliSDK is in a position to greatly aid in this mission.

Qilimanjaro employs fluxonium analogue qubits in full-stack quantum computers using a dual technology approach. This technique was developed specifically to eliminate the need for extensive error correction. Analogue quantum computers can generate more stable qubits and fewer circuit-level errors by inherently encoding complex challenges into the physical system itself. This architecture is believed to offer instant advantages in crucial fields including simulation, optimisation, and artificial intelligence (AI), where ordinary digital QPUs could normally require a large overhead due to error correction requirements.

By employing a multimodal approach that blends analogue QPUs, digital QPUs, and conventional supercomputers, the Qilimanjaro platform is made to maximise the utility of any system. Finding real computational value years ahead of roadmaps that solely target digital systems is the aim of this combined approach. The QiliSDK is the tool that makes it easy for developers of quantum applications to access this computational value because of its high-level abstraction and Python syntax.

Interoperability and Upcoming Versions

Qilimanjaro has already set lofty goals for the future development of QiliSDK. The functionality of the toolkit will be significantly increased in future iterations.

Among these expected advancements are pulse programming and enhanced support for digital-analog hybrid processes. Additionally, future versions will have noise-aware modelling. Industry standards such as OpenQASM 3.0 and QIR will be supported to guarantee robust, cross-stack compatibility. One of the main goals is to guarantee seamless integration and compatibility across different quantum stacks.

Deep learning models and Variational Quantum Eigensolver (VQE) algorithms are used in the Hybrid Quantum–AI Framework for Protein Structure Prediction to overcome the disadvantages of each method alone. Our structure prediction method enhances accuracy and has applicability in near-term quantum computing by framing the task as “energy fusion.” Making structure prediction a “energy fusion” problem enhances accuracy and makes it suitable for near-term quantum computing (NISQ) devices.

The idea is to combine neural network data-derived biological priors with quantum algorithm physics-based modeling.

Below, we discuss the framework and its parts:

Considering restrictions requires a hybrid approach.

A challenging protein structure prediction starts with finding the lowest-energy conformation on a high-dimensional energy landscape.

AlphaFold3 and ColabFold limitations: These models are data-driven notwithstanding their success. They tend to use sequence alignments and huge training datasets instead of physical principles. This limits interpretability and generalizability, especially for brief peptide segments or conditions outside their training distribution.

VQE on NISQ devices' limitations: Quantum computing uses a novel physics-based paradigm to approximate a molecular Hamiltonian's ground-state energy. Noise, a paucity of qubits, and shallow circuit depth plague NISQ technology like the 127-qubit superconducting processor used in this study. This makes VQE predictions crude and often fail to reproduce fine-grained features like backbone dihedral angle distributions or secondary-structure motifs. The quantum energy landscape provides a reliable, low-resolution map.

Function of Fused Energy

For energy fusion, three normalized terms are weighted linearly combined:

Basic quantum-mechanical interactions and the conformational landscape's low-resolution global structure are reflected in normalized quantum energy.

Secondary Structure Distribution Divergence: This measure compares Ramachandran kernel-derived secondary structure probabilities from the quantum candidate's geometry to NSP3 predictions. It emphasises secondary structural pattern compliance.

The final metric, dihedral-angle consistency, measures how well the quantum candidate structure matches NSP3 predictions. This term encodes fine-grained backbone geometry.

Coefficients are user-specified trade-off weights that balance term contributions.

Importance and Results

Statistics showed statistically significant benefits for the hybrid framework over classical and quantum-only baselines.

The hybrid technique yielded a mean RMSD of 4.89 Å (median 4.70 Å) after analyzing 375 conformations from 75 protein fragments.

This accuracy lowers RMSD by 28.6%, ColabFold by 58.5%, and AlphaFold3 by 57.2% for predictions that are quantum-only.Significant improvements are shown by the Wilcoxon signed-rank and paired t-tests (p < 0.001).Practicality: Despite NISQ's low precision, the framework shows how data-driven models can benefit from it.

The fused energy score guides the re-ranking process towards structurally accurate, near-native conformations, as seen by its positive association with RMSD (R^2 = 0.322).

Hybrid Quantum-Classical Computing Framework Revolutionizes Molecular Geometry Prediction

Academics led by Yajie Hao, Qiming Ding, Xiaoting Wang, and Xiao Yuan presented a groundbreaking hybrid quantum-classical computing framework for computational chemistry. the age-old difficulty of accurately forecasting huge molecule architectures, which hinders materials research and pharmaceutical development. The unique method reduces quantum resources needed for exact computations by merging the Variational Quantum Eigensolver (VQE) and Density Matrix Embedding Theory (DMET).

Persistent Molecular Geometry Challenge

A molecule's characteristics and interactions are determined by the three-dimensional configuration of its atoms. Knowing a molecule's equilibrium geometry, or lowest energy structure, is essential to comprehending its behaviour and function. However, this is tough. Traditional computations are too expensive for bigger molecules. This project is hard. Traditional computational methods are too expensive for bigger molecules. While exciting, near-term quantum computers have low qubit counts and noise, making complex chemical calculations challenging. Historically, these restrictions rendered it practically difficult to predict huge molecule equilibrium geometries.

Hybrid Quantum-Classical Computing Framework Unveiled A novel quantum-classical approach for molecular system lowest energy structure identification. This framework uses the Hellmann-Feynman theorem to calculate molecular energy with shape. This essential information is used by a classic optimisation method to iteratively modify the molecular geometry to find the system's most stable configuration.

DMET is crucial to this technique. DMET breaks apart large molecules for easy management. Near-term quantum devices benefit from this fragmentation because it reduces the number of qubits needed for quantum simulation without compromising accuracy. The Variational Quantum Eigensolver (VQE) and DMET are used to estimate energy precisely.

The Direct Co-optimization Breakthrough

The direct co-optimization method distinguishes this framework. Our unique method blends DMET and VQE to improve molecular geometry and quantum variational parameters simultaneously, unlike previous methods that update geometry through computationally expensive repetitive loops. By optimising these parameters simultaneously, the researchers eliminated these expensive iterative loops, expediting convergence and minimising quantum assessments. This integrated, simultaneous optimisation overcomes computationally expensive phases in traditional techniques, improving scalability and efficiency.

Glycollic Acid Validation and Landmark Achievement

Trials proved this revolutionary framework's efficacy. Validating the method on H4 and H2O2 proved its accuracy and efficiency. Following this success, the approach was applied to glycollic acid (C2H4O3), a complicated chemical that has previously prevented quantum geometry optimisation.

Results were groundbreaking. Quantum algorithms have optimised the shape of a molecule this tiny for the first time. The method produced high-fidelity equilibrium geometries for glycollic acid that matched classical reference methods while reducing quantum resource needs and processing expense. This achievement advances scalable quantum simulations by overcoming the restrictions of tiny molecules used in proof-of-concept research and enabling realistic, large-scale molecular shape optimisation on near-term quantum devices.

Future implications and directions

This breakthrough is expected to impact several scientific and industrial domains. The ability to accurately predict large molecule structures allows the "in silico" construction of complex catalysts and drugs. This skill could speed up discovery and development by allowing researchers to construct and test novel drugs and materials using computer simulations.

Even though these chemicals are performing well, the scientists said future research will focus on broadening the framework. Adding more advanced quantum hardware and error-reduction methods and applying the technology to periodic materials structures in a crystal lattice are planned. These developments should increase the method's applicability to more chemically and industrially relevant situations.

C-DAC develops advanced computing

C-DAC of India: Strategic International Collaborations for Hybrid Quantum Computing

The Ministry of Electronics and Information Technology's Centre for Development of Advanced Computing (C-DAC) and Rigetti Computing, Inc. have signed an MOU. This strategic partnership highlights how C-DAC advance India's electronics and IT development, particularly quantum and hybrid computing, and technology.

HPC competence makes C-DAC a national leader in India's advanced computing sector. For hybrid computing, quantum processing and supercomputing must be combined. Shri. E. Magesh, C-DAC Director General, said the organization's HPC leadership benefits this relationship. Indian strategic strengths in several critical, cutting-edge technologies are powered by C-DAC.

C-DAC leads India's ChipIN program, a key part of its national mandate. This critical national program aims to develop domestic semiconductor design capabilities for national self-reliance and technological sovereignty.

The opportunity to build cutting-edge cryogenic electronics with Rigetti supports and aligns with the ChipIN objective. This strategy strengthens India's competitiveness in the rapidly changing global technological arena, demonstrating C-DAC's proactive stance of using foreign collaborations to build and embed indigenous capabilities and acquire technology. Dr. S D Sudarsan, Executive Director of C-DAC Bangalore, acknowledged ChipIN's co-development potential.

In addition to high-performance computing and semiconductor development, C-DAC is committed to other national quantum technology efforts. Quantum Processing Units (QPUs) and cryogenic electronics and control systems, which are the foundation of any advanced quantum computing system, are being made locally.

C-DAC has a unique potential to accelerate these vital domestic developments by working with Rigetti, a superconducting quantum computing pioneer. This strategy is also meant to assist India improve superconducting quantum hardware and obtain access to superior processor fabrication facilities.

The MOU with Rigetti is essential and vital to India's technological ambitions, according to C-DAC. Beyond hardware design, the cooperation uses the next hybrid computing architecture to develop crucial use cases and application workflows. To successfully turn theoretical quantum benefits into actual, usable, and applicable solutions for the public and private sectors, this is crucial.

Importantly, the partnership includes many workforce development activities to train highly skilled personnel for long-term innovation and leadership in this inventive business. The MOU states that the goal is to successfully present these jointly generated technologies to the market to demonstrate a strong commitment to their practical implementation and generate significant economic value for the country.

“Our collaboration with Rigetti is a critical step towards creating domestic cryogenic electronics, ensuring access to advanced processor manufacture, and producing superconducting quantum hardware,” said C-DAC Director General Shri. E. Magesh. By advancing electronics and IT, this collaboration should boost India's leadership in next-generation technology. C-DAC Bangalore Executive Director Dr. S D Sudarsan said the alliance will “drive innovation in electronics and information technology, particularly in quantum and hybrid computing, contributing meaningfully to the nation's technological progress.”

This strategic relationship emphasises C-DAC's hard work, which supports India's technical independence and global leadership goals. By focussing on quantum and hybrid computing, C-DAC is helping solve complex problems that classical computers cannot solve.

Because of C-DAC's proactive engagement, India is a major player in this global technological race. Government quantum computing investments are expected to reach $1.8 billion in 2024 (66% of total investments) and surpass $10 billion in early 2025. C-DAC, a vital national asset guiding India through the revolutionary landscape of next-generation computing, is strategically combining domestic development initiatives with strategic access to international expertise to drought a strong and forward-looking strategy for India's technological future.

Like a national space agency designs satellites, rockets, and mission control centres for space travel, C-DAC is India's digital and technical future. C-DAC is establishing advanced computing infrastructure by building quantum processing units and associated sophisticated cryogenic systems and developing more complete strategies for integrating these powerful new instruments into high-performance computing settings.

Dynamic scheduling boosts hybrid quantum-classical computing efficiency.

European Researchers Improve Supercomputing-Quantum Integration by Addressing Resource Idle Time.

European researchers are investigating new methods to maximise supercomputer-quantum processor cooperation to overcome a major barrier to hybrid computing systems. A recent study found that dynamic scheduling, specifically “malleability scheduling,” can reduce idle time and speed up project completion in complex environments. Since quantum tasks are longer than conventional ones, this invention is crucial for sharing classical and quantum resources.

Hybrid Systems and Uneven Resources Challenge

Quantum computers may accelerate highly specialised computational workloads on classical HPC systems. Quantum processing units (QPUs) may help with optimisation, materials modelling, and quantum simulation. Merging these computational models is hindered by inefficient resource allocation.

Presently, hybrid computing jobs may reserve both a QPU and conventional HPC nodes, even while only one is being used. Due to CPU core idleness during quantum processes and vice versa, a lot of expensive hardware is wasted. QPUs are scarce, perhaps only one or two in a cluster, increasing the possibility of bottlenecks and wasted hardware.

Smart Solution: Dynamic Scheduling

Researchers recommend dynamic scheduling over static resource allocation to address this problem. Unlike static methods that assign resources for the length of a work, dynamic scheduling frees classical resources when quantum activities are offloaded and precisely reallocates them as needed. This strategy increases hybrid algorithm execution throughput, reduces idle time, and boosts task completion.

European study team members from E4 Computer Engineering, LINKS Foundation, Barcelona Supercomputing Centre, CINECA, and other universities examined two primary dynamic scheduling algorithms. These methods were workflow- and malleability-based.

Organising Task Dependencies: Workflow Management

Workflow management systems (WMS) break down complex operations into smaller, interrelated tasks. After then, resources are scheduled as needed for each task. By modelling a hybrid application as a three-step loop with StreamFlow WMS, the team tested parallel classical algorithms, quantum routine aggregation, and quality evaluation. This method frees up HPC nodes by only using quantum resources when needed.

Flexibility: Resize

Malleability offers a different yet effective dynamic scheduling method. This approach allows HPC workload resizing during quantum phases without re-queuing. Software can dynamically adjust the number of compute nodes it uses during runtime. In quantum computation, the researchers used the Dynamic Management of Resources (DMR) framework to lower the HPC footprint while keeping a minimum process. The footprint may rise again when classical computation resumes. This approach avoids re-queuing and frees up cores for other operations.

Experimental Hybrid Clustering Validation

To compare techniques, the researchers changed a clustering aggregation algorithm into a hybrid HPC-QC application. K-means, DBSCAN, and hierarchical clustering were run on HPC nodes during the classical phase. After graphing their outputs, the problem became a quadratic unconstrained binary optimisation (QUBO) problem and was resolved in the quantum phase.

Since their testbed lacked a quantum processing unit (QPU), simulated annealing was done using a “quantum emulator” node, which simulates quantum technology runtimes by adding customisable delays. Three setups were tested on a small SLURM-managed cluster:

Baseline: HPC and quantum resources are static throughout the job.

Workflow: Task-based scheduling that only uses quantum resources when needed.

Malleability: HPC allocation's quantum-phase dynamic resizing.

Key Findings and Implications

Dynamic scheduling's benefits became clear from experiments. For two-minute quantum phase simulations (like neutral-atom devices):

Despite using the least HPC node time, the workflow strategy took the longest to complete because to the scheduler's constant resource requests.

The baseline static allocation was the fastest for a single run but the least effective and idled resources.

Malleability saved resources without workflow scheduling delays.

When two jobs were done simultaneously, benefits rose. Both operations took longer and consumed more resources due to the baseline technique. However, workflow and malleability improved overlap. Malleability allowed computation to restart immediately after the quantum phase, even if not all HPC nodes were online. Finishing times dropped considerably. Malleability helps manage concurrent processes even during brief quantum phases (less than one second).

Static scheduling will not work well for hybrid workloads, especially when quantum processes are longer than conventional ones, the study found. Dynamic computing node reallocation in resource-constrained clusters could dramatically boost HPC gear consumption and reduce queue wait times. Workflow and malleability have pros and cons. Workflow systems require modular application design, yet malleability makes program state management with different resources harder, despite being easier to implement into present code.

Future Directions and “Maturity Gap”

Even though the tests were limited to simulated quantum workloads on a tiny test cluster, the discovery shows that hardware developments alone won't improve real-world performance until software and scheduling methods change. The researchers recommend more workloads, real conflict circumstances, and larger quantum experiments.

Robotic Inspection

D-Wave Quantum Annealers Improve Industrial Optimisation and Promise for Hard Problems

D-Wave Systems' hybrid quantum-classical algorithms and quantum annealers are extending industrial applications, accelerating quantum computing. These technologies are increasingly being researched for their ability to address computationally challenging optimisation problems in complex industrial contexts. Recent investigations show that they can compete with classical techniques for certain issue kinds and are feasible in robotic inspection.

D-Wave's technology is being used to optimise robotic inspection pathways for industrial components with complex three-dimensional geometries from CAD models. The Travelling Salesman Problem (TSP) is reframed in three dimensions. The renowned combinatorial optimisation problem TSP finds the shortest path that stops at exactly one of a defined group of “cities” before returning to the starting point. To alter this problem for robotic inspection, researchers use realistic constraints like open-route requirements and flawed network designs.

D-Wave's quantum-based solvers, which combine classical optimisation approaches with quantum processing principles, have been likened to Google OR-Tools and GUROBI. D-Wave's hybrid solvers (NL-Hybrid and CQM-Hybrid) have produced competitive solution quality while substantially reducing calculation times for car doors, bumpers, toy bears, aircraft parts, and H-spheres in five industry case studies.

Even if Gurobi sometimes produces somewhat better results, D-Wave's solvers' speed advantage is an attractive trade-off, especially in dynamic production contexts where flexibility and quick response are crucial. According to the research, D-Wave solvers provide solutions with AR values between 0.62 and 0.94, indicating near-optimal inspection trajectories. The research measures solution quality using the Approximation Ratio (AR) metric and computing efficiency using the Run Time (RT) metric.

Quantum annealing is a metaheuristic that finds the global minimum of an objective function using quantum fluctuations. Quantum annealers set a system of qubits in an initial ground state and let it grow adiabatically to a final state that encodes the solution, unlike gate-based quantum computers that use controlled qubits in non-binary states. It uses quantum mechanical tunnelling over potential barriers to escape local minima better than classic “hill climbing” methods like Simulated Annealing (SA).

D-Wave's quantum annealers, particularly the Advantage QPU with the Pegasus architecture, are promising quantum processing devices. Pegasus topology, a substantial improvement over previous generations, links each qubit to 15 others. These annealers are not universal quantum computers and have fewer applications than gate-based systems, but they are ideal for optimisation problems, especially those related to the Ising model or QUBO problems.

D-Wave's hybrid solvers are also compared against top-tier classical solvers like CPLEX, Gurobi, and IPOPT on a wider range of optimisation problems. These hybrid solvers address quantum computer scaling limits by dividing problems down and executing parts normally and others on the Quantum Processing Unit (QPU). D-Wave's cloud-based Leap Hybrid Solvers automatically apply constraint penalty terms using quantum hardware and traditional algorithms like Tabu search and simulated annealing.

The hybrid solver of D-Wave provided optimal Binary Linear Programming (BLP) results with linear constraints comparable to CPLEX and Gurobi. However, more variables made it slower to calculate, especially near its scalability limit. At D-Wave's variable limit, adding constraints increased calculation time and decreased solution quality compared to traditional solvers.

D-Wave found poor solutions, often two times the ideal, while addressing integer/binary linear programming problems with quadratic constraints, suggesting it may become stranded in local minima. However, it solved some variable counts faster than IPOPT and CPLEX.

D-Wave's hybrid solver performs best in Binary Quadratic Programming (BQP), which theoretically replicates the Ising Hamiltonian, making these problems ideal for quantum annealers. In this scenario, D-Wave consistently found optimal solutions faster than CPLEX (topped at 1000s runtime) and IPOPT for BQP with 500 binary variables.

Despite longer run times, D-Wave

LeapHybridCQMSolver couldn't compete with Gurobi in computing time and solution quality for Mixed-Integer Linear Programming (MILP) on a power sector Unit Commitment problem. D-Wave may address MILP problems, however its computing advantage appears to be limited to quadratic binary objective functions or limitations.

Quantum Diagonalisation by Sample

Integrating Solvent Effects into SQD: A New IEF-PCM Method

Quantum Chemistry Comes to Life: Quantum Computers Simulate Solvent Molecules

Cleveland Clinic Scientists Improve Real-World Chemical Issues

Cleveland Clinic researchers developed a method to help quantum computers solve chemical issues that were previously unsolvable. The Journal of Physical Chemistry B reports that quantum computers can accurately describe molecules in solvents. This is necessary to apply quantum computation for biology and industry challenges including drug behaviour and catalytic events.

Quantum computer simulations of quantum chemistry have ignored the environment for years, focussing on molecules. A solvent like water conducts most natural and industrial chemical processes. The solvent-solute interaction fundamentally influences drug binding, protein folding, and catalysis. Modelling solvent effects has been tricky and largely done by traditional computers.

The study team, coordinated by Kenneth Merz Jr., PhD, of the Cleveland Clinic Centre for Computational Life Sciences, filled this gap. Sample-based quantum diagonalisation (SQD), originally developed for gas-phase simulations, now includes solvent effects. A well-known classical technique, the Integral Equation Formalism Polarisable Continuum Model (IEF-PCM), was integrated. Since IEF-PCM treats the solvent as a continuous, smooth substance around the solute rather than distinct molecules, complicated interactions are simplified.

New approach sample-based quantum diagonalization-IEF-PCM combines quantum and classical computing advantages. Using quantum hardware, electronic configurations, or “samples,” are created from a molecule's wavefunction. These samples are delivered to a regular computer, where quantum device noise may affect them. S-CORE corrects samples to restore electron number and spin.

Importantly, the corrected quantum samples create a more manageable subdomain of the chemical issue that can be solved traditionally. Consider the solvent's influence by include the IEF-PCM effect as a perturbation to the molecule's Hamiltonian, which defines its total energy. This process is repeated until solvent and solute are constant. The solvent effect updates the molecular wavefunction, which the solvent model reacts to. This hybrid quantum-classical technique achieves chemical precision while reducing computational expense.

The scientists tested sample-based quantum diagonalization-IEF-PCM on IBM quantum computers with 52 qubits. Their watery solution mimicked biochemistry's four polar molecules: ethanol, methylamine, methanol, and water. Current quantum gear has intrinsic noise, but simulations yielded solvation free energies that were close to classical benchmarks.

The quantum-classical method's methanol solvation energy was less than 0.2 kcal/mol below the chemical precision criterion. Usually measured in kilocalories per mole, chemical accuracy is the precision needed for chemically significant results. This method achieved chemical precision of less than 1 kcal/mol for the four chemicals studied.

The sample-based quantum diagonalization-IEF-PCM approach was more accurate with more samples per simulation. Even for bigger molecules like ethanol with huge quantum configuration spaces, the technique focused on the most important locations with only a modest amount of data. In combination with IEF-PCM, the complete active space configuration interaction (CASCI) improves energy convergence to high-accuracy classical techniques. Solutions were always within 1 kcal/mol of experimental values from the MNSol database and conventional CASCI reference.

These results demonstrate that the SQD-IEF-PCM technique is scalable, adaptable to numerous chemical systems, and hardware noise-resistant. “This work represents a major advancement in practical quantum chemistry on quantum computers,” Dr. Merz added. He added, “Very few quantum hybrid models have been tested on quantum hardware, and they are mostly unexplored. We are testing this model on quantum hardware to demonstrate its potential for chemical research using quantum computers. Advanced quantum hardware and computational methods are allowing quantum computers to solve chemical challenges previously unsolvable.

Pharmaceutical and materials science research will benefit instantly from quantum algorithms to simulate molecules in solution. It opens new avenues for studying pharmaceutical-biological interactions and industrial catalysts.

The long-awaited "quantum advantage," the ability of quantum computers to perform better than classical ones for certain tasks, has yet to be demonstrated in practical chemical issues, but sample-based diagonalisation techniques like sample-based quantum diagonalisation may be a viable route to its realisation. Sample-based quantum diagonalisation limits the quantum workload to sampling, leaving the most computationally difficult tasks to classical algorithms, unlike some variational quantum algorithms that need complex, noise-sensitive operations.

Despite their advances, the researchers recognise its limitations. Since it works best for neutral molecules, the current technique should be tested for charged systems. They also note that enhancing quantum circuit parameterisation reduces the number of samples needed for accurate results.

Even though it accurately portrays electrostatic interactions, the implicit solvent model does not account for dispersion forces and hydrogen bonding. This requires further extensions, possibly involving explicit solvent molecules or more advanced hybrid models.