Quantum Annealing: D-Wave Systems & Optimization Applications
Quantum annealing news: D-Wave Advantage systems, optimization problems, hybrid algorithms. QUBO formulations & commercial deployments.
Quantum annealing represents the earliest commercialized form of quantum computing, using quantum fluctuations to find optimal solutions to combinatorial optimization problems. D-Wave Systems has deployed systems with 5,000+ qubits (Advantage processor) accessed via cloud and installed at research institutions, government labs, and corporations.
Unlike gate-based quantum computers that execute algorithmic instructions, quantum annealers solve problems by mapping them onto an Ising model or quadratic unconstrained binary optimization (QUBO) formulation. The quantum processor evolves from a superposition of all possible states toward the ground state of the problem Hamiltonian.
India's Quantum Annealing Landscape
India's enterprise technology sector explores quantum annealing through cloud access to D-Wave systems. The National Quantum Mission focuses primarily on gate-based quantum computing hardware development rather than quantum annealing hardware, but optimization applications using quantum annealing fall under NQM's broader quantum computing applications scope. Tata Consultancy Services (TCS), Infosys, and other IT majors develop quantum optimization solutions for Indian enterprises using hybrid quantum-classical approaches.
Key Advantages
Key advantages include mature commercial technology with 10+ years of cloud availability, massive qubit counts (5,000+), specialization for optimization without requiring full error correction, and established application ecosystems. Limitations include narrow application scope (optimization only), no quantum error correction, and restricted connectivity requiring problem embedding overhead.
Recent Developments
Recent developments include D-Wave's Advantage2 prototype experimenting with higher connectivity (Zephyr topology) and error-reduction techniques.
quantum-computingD-Wave Earns IDC Recognition for Its Practical Quantum Computing Push - Yahoo Finance
D-Wave Earns IDC Recognition for Its Practical Quantum Computing Push Yahoo Finance
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quantum-computingD-Wave Quantum vs. Rigetti Computing: Which Quantum Computing Stock Is a Better Buy in 2026? - The Globe and Mail
D-Wave Quantum vs. Rigetti Computing: Which Quantum Computing Stock Is a Better Buy in 2026? The Globe and Mail
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quantum-computingD-Wave Quantum vs. Rigetti Computing: Which Quantum Computing Stock Is a Better Buy in 2026? - The Motley Fool
D-Wave Quantum vs. Rigetti Computing: Which Quantum Computing Stock Is a Better Buy in 2026? The Motley Fool
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quantum-computingResearchers Build Quantum Circuits Using Ising Model and Time-Dependent Fields
Matthias Werner at the IUniversity of Barcelona and colleagues have found a fundamental connection between the transverse-field Ising model and standard gate-based quantum computation. The Ising model, when driven by a specifically tailored, time-dependent transverse field, simulates any quantum circuit with a polynomial increase in computational resources. This finding answers a long-standing question regarding the computational power of analogue quantum simulation platforms, such as those employing quantum annealing, and importantly, suggests inherent limitations for classically simulating this type of Ising model. The research also has implications for complexity theory and the control of quantum systems, potentially motivating improvements in simulating quantum circuits using the Ising model. Transverse-field Ising model replicates universal quantum circuits with polynomial overhead A significant advance in quantum simulation has been realised, demonstrating a polynomial increase in time, qubit number, and energy scale when simulating quantum circuits using the transverse-field Ising model. This represents a substantial improvement over previous methods, which lacked a clear pathway to universal quantum computation with predictable resource scaling. The Ising model, driven by a carefully controlled, time-varying transverse field, effectively replicates any quantum circuit, unlocking the potential for utilising analogue quantum simulation platforms for broader computational tasks. The significance of this lies in the potential to move beyond specialised optimisation problems, for which quantum annealers are currently designed, towards a more general-purpose quantum computing paradigm based on analogue principles. Previous attempts to demonstrate universality often suffered from exponential scaling of resources, rendering them impractical. This work establishes a polynomial scaling relationship, offering a more viable route to scalability, although substantial cha
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quantum-computingD-Wave Quantum (QBTS) to Receive $1.5M Grant Through NSF Project to Strengthen US Quantum Computing Leadership - Yahoo! Finance Canada
D-Wave Quantum (QBTS) to Receive $1.5M Grant Through NSF Project to Strengthen US Quantum Computing Leadership Yahoo! Finance Canada
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quantum-computingD-Wave Is One of Only Two Companies Named to the Leaders Category in the IDC MarketScape: Worldwide Quantum Computing 2026 Vendor Assessment - Business Wire
-D-Wave Is One of Only Two Companies Named to the Leaders Category in the IDC MarketScape: Worldwide Quantum Computing 2026 Vendor AssessmentShare PALO ALTO, Calif.--(BUSINESS WIRE)--D-Wave Quantum Inc. (NYSE: QBTS), (“D-Wave” or the “Company”), the only dual-platform quantum computing company providing both annealing and gate-model systems, software, and services, today announced it has been named a Leader in the IDC MarketScape: Worldwide Quantum Computing 2026 Vendor Assessment (doc #US54125526, June 2026, the “IDC MarketScape”). The IDC MarketScape evaluated quantum computing companies based on their current capabilities and future strategies. According to the IDC MarketScape, D-Wave’s key strengths include: Broad production deployment footprint. D-Wave’s customer activity extends beyond research experimentation into operational manufacturing, telecommunications, retail, logistics, defense, and research computing workflows. According to D-Wave, more than 200 million problems have been submitted to its systems, the Advantage2TM system usage grew 314% year over year, and the StrideTM hybrid solver usage grew 114% over six months as of early 2026. Public implementations include manufacturing scheduling with Ford Otosan, network optimization with NTT DOCOMO, and workforce scheduling with Pattison Food Group. Mature enterprise accessibility and hybrid adoption framework. The LeapTM cloud platform, OceanTM SDK, the Stride hybrid solver, the Leap Quantum LaunchPadTM onboarding program, and a structured training catalog collectively reduce the infrastructure and expertise barriers that most often slow enterprise quantum adoption. Organizations can apply quantum-assisted optimization to problems involving up to 2 million variables without requiring dedicated quantum programming expertise, while guided onboarding programs provide a pathway from initial evaluation to production deployment. Extending quantum annealing beyond optimization into scientific simulation. Publishe
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quantum-computingIonQ (NYSE: IONQ), Rigetti (NASDAQ: RGTI), And D-Wave (NYSE: QBTS) Emerge As Top Quantum Computing Plays For Aggressive Investors - foreignpolicyjournal.com
IonQ (NYSE: IONQ), Rigetti (NASDAQ: RGTI), And D-Wave (NYSE: QBTS) Emerge As Top Quantum Computing Plays For Aggressive Investors foreignpolicyjournal.com
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quantum-computingParametrized-circuit-free quantum regression with variance regularization
--> Quantum Physics arXiv:2607.02696 (quant-ph) [Submitted on 2 Jul 2026] Title:Parametrized-circuit-free quantum regression with variance regularization Authors:Yerassyl Balkybek, Andrey Kardashin, Vladimir V. Palyulin, Konstantin Antipin View a PDF of the paper titled Parametrized-circuit-free quantum regression with variance regularization, by Yerassyl Balkybek and 3 other authors View PDF HTML (experimental) Abstract:Quantum regression tasks for predicting properties of quantum states are commonly addressed using variational quantum algorithms. While variational quantum circuits are highly expressive and allow to achieve reasonable accuracy, training these circuits may demand a considerable amount of time and resources. In this work, we propose an approach of constructing problem-specific quantum regression models with encoding relevant symmetries and regularizing the variance. The proposed method is based on finding the coefficients of the linear combination of suitably chosen observables. Although it requires the knowledge of the symmetries of the problem in question, the method does not involve parameterized quantum circuits, and the training is done efficiently once the observables are measured. We demonstrate this method on two examples: Prediction of the transverse field strength in the Ising model, and quantification of entanglement in bipartite qubit systems. Our approach is accurate and less resource-intensive than conventional variational methods. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2607.02696 [quant-ph] (or arXiv:2607.02696v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.02696 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yerassyl Balkybek [view email] [v1] Thu, 2 Jul 2026 18:38:23 UTC (4,367 KB) Full-text links: Access Paper: View a PDF of the paper titled Parametrized-circuit-free quantum regression with variance regularization, by Yerassyl Balkybek and 3 other authorsView
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quantum-computingPodcast with Corey O’Meara, Chief Quantum Scientist at E.ON Digital Technology
Yuval Boger interviews Corey O’Meara, Chief Quantum Scientist at E.ON Digital Technology. They discuss how E.ON’s five-person quantum team works with business units to find high-value use cases, especially where classical methods may hit bottlenecks in optimization and machine learning. Corey describes the company’s work on peer-to-peer energy trading and other grid-related applications, and shares his perspective on cloud access, hybrid deployment, and the accelerating progress toward fault-tolerant quantum computing. Key Takeaways E.ON’s five-person quantum team only takes on optimization problems that are proven to scale exponentially with classical solvers like Gurobi; complexity, not data volume, is the deciding factor. A peer-to-peer energy trading coalition formation project, done with Aqarios, LMU Munich, University of Oxford and DFKI, showed evidence of quantum scaling advantage on a D-Wave annealer, which the team considers one of the first business-relevant demonstrations of its kind. Optimization runs on today’s hardware take minutes, not hours, and E.ON expects cloud access to quantum computers to remain sufficient for decades unless real-time grid balancing applications eventually require dedicated, owned hardware with SLAs. The team is all physics and quantum computing PhDs who learn the energy domain after joining, and they believe five years ago was already late to start building this kind of internal quantum capability. Transcript Corey: Hello, Corey, and thank you for joining me today. Thanks for having me. Yuval: So who are you and what do you do? Corey: Yeah, so my name is Corey and I’m chief quantum scientist at E.on Digital Technology. And there I’m technically leading the applied quantum computing program at one of Europe’s largest utility companies. Yuval: E.on, as you mentioned, is a very large company. How large is the Quantum team and when did it get started? Corey: Yeah, great question. So we’re a pretty modest t
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quantum-computingHeterogeneous Quantum-Classical Workflow Computes Tritium Binding in FLiBe Molten Salts
Heterogeneous Quantum-Classical Workflow Computes Tritium Binding in FLiBe Molten Salts A research collaboration between Oak Ridge National Laboratory (ORNL), the Cleveland Clinic, and IBM Quantum has completed the first heterogeneous quantum-classical simulation of tritium binding within a liquid inorganic molten salt. Released as a preprint on arXiv by a team including ORNL Section Head Tom Beck, Corporate Research Fellow Al Geist, and Cleveland Clinic staff scientist Dr. Kenneth Merz Jr., the project uses quantum-centric supercomputing to resolve electronic ground-state energies for clusters of fluorine, lithium, and beryllium (FLiBe; 2LiF–BeF2). The joint venture serves as a baseline proof-of-concept for the U.S. Department of Energy’s (DOE) Genesis Mission, which aims to eliminate the tritium extraction and fuel-breeding bottlenecks that hinder commercial nuclear fusion power plants. [ ORNL - LBNL - IBM Simulation Stack ] Compute Architecture─► Heterogeneous CPUs, GPUs, and cloud-accessible IBM Quantum QPUs. Physical Mechanism ─► Active tritium extraction & conformational energy modeling in liquid FLiBe blankets. Algorithmic Engine ─► Embedded-Wavefunction (EWF) partitioning & Extended Sample-Based Quantum Diagonalization. To achieve self-sustaining nuclear fusion within magnetic-confinement tokamaks, reactors must breed their own tritium (3H) fuel on-site by wrapping the high-temperature plasma wall in a thick blanket of liquid salt. When high-energy neutrons bombard lithium-6 atoms inside the fluid, they split to yield fresh tritium gas. However, optimizing the chemical recipe of a liquid salt that shifts dynamically under radiation, magnetic fields, and intense thermal loads is an intractable challenge for classical supercomputers. Classical techniques like Density Functional Theory (DFT) introduce free-energy error margins as high as 10%, failing to predict whether the liberated tritium will drift out safely as a harvestable gas or bind with fluorin
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quantum-computing3 Quantum Computing Stocks to Buy in July - Yahoo Finance
3 Quantum Computing Stocks to Buy in July Joel South Mon, July 6, 2026 at 11:52 AM EDT 5 min read NVDA +1.21% IONQ +4.02% QBTS +4.59% RGTI +3.86% Quick Read IonQ's Q1 revenue surged 755% with $470M in remaining performance obligations, while D-Wave bookings jumped nearly 2,000% year over year. Jensen Huang pegged large-scale quantum commercialization at least 15 years out, yet all three stocks carry price-to-sales multiples above 100x. All three stocks shed 20% or more in June alone, making position sizing the critical risk management decision for aggressive investors eyeing August earnings. Act now: the analyst who called NVIDIA in 2010 just named his top 10 AI stocks — and IonQ didn't make the cut. Grab the names FREE today. Quantum computing stocks are speculative, pre-profit bets, well outside core-portfolio territory, on a technology that even NVIDIA (NASDAQ:NVDA) CEO Jensen Huang once suggested is likely at least 15 years away from large-scale commercialization. All three names below routinely swing in double-digit percentages on no news and trade at extreme price-to-sales multiples that have been reported as high as ~109 for IonQ, ~836 for Rigetti, and ~791 for D-Wave by one source, with smaller but still extreme readings from others. Source variance is wide. Treat these as aggressive position-sized lottery tickets on a multi-year technology curve. 24/7 Wall St. That said, the operating data underneath the hype has materially improved in 2026. Bookings, remaining performance obligations, and cash positions are stronger than at any prior point for the publicly traded quantum pure-plays. Here are three US-listed names worth a look in July for investors who can stomach the volatility. IonQ (NYSE: IONQ) IONQ Analyst Ratings — 24/7 Wall St. IonQ (NYSE:IONQ) is the scale leader of the group, with a market cap of roughly $20.11 billion and the most aggressive revenue ramp. Q1 FY26 revenue hit $64.
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quantum-computing3 Quantum Computing Stocks to Buy in July - 24/7 Wall St.
Quantum computing stocks are speculative, pre-profit bets, well outside core-portfolio territory, on a technology that even NVIDIA (NASDAQ:NVDA | NVDA Price Prediction) CEO Jensen Huang once suggested is likely at least 15 years away from large-scale commercialization. All three names below routinely swing in double-digit percentages on no news and trade at extreme price-to-sales multiples that have been reported as high as ~109 for IonQ, ~836 for Rigetti, and ~791 for D-Wave by one source, with smaller but still extreme readings from others. Source variance is wide. Treat these as aggressive position-sized lottery tickets on a multi-year technology curve.nextstayCCSettingsOffArabicChineseEnglishFrenchGermanHindiPortugueseSpanishFont ColorwhiteFont Opacity100%Font Size100%Font FamilyArialText ShadownoneBackground ColorblackBackground Opacity50%Window ColorblackWindow Opacity0%WhiteBlackRedGreenBlueYellowMagentaCyan100%75%50%25%200%175%150%125%100%75%50%ArialGeorgiaGaramondCourier NewTahomaTimes New RomanTrebuchet MSVerdanaNoneRaisedDepressedUniformDrop ShadowWhiteBlackRedGreenBlueYellowMagentaCyan100%75%50%25%0%WhiteBlackRedGreenBlueYellowMagentaCyan100%75%50%25%0%Video Muted That said, the operating data underneath the hype has materially improved in 2026. Bookings, remaining performance obligations, and cash positions are stronger than at any prior point for the publicly traded quantum pure-plays. Here are three US-listed names worth a look in July for investors who can stomach the volatility. IonQ (NYSE: IONQ) IonQ (NYSE:IONQ) is the scale leader of the group, with a market cap of roughly $20.11 billion and the most aggressive revenue ramp. Q1 FY26 revenue hit $64.67 million, up 755% year over year, beating the midpoint of its own guidance by 30%. Management raised full-year guidance to $260 million to $270 million and pointed to organic growth above 100% YoY. The bull case rests on three things.
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quantum-computingNetworks Accelerate Quantum Information Scrambling with Long-Range Interactions
Reza Pirmoradian at the Institute of Higher Education (EDI), working with colleagues from Islamic Azad University, Alzahra University, School of Quantum Physics and Matter Institute, and Amirkabir University of Technology, have explored the link between network topology and the onset of quantum chaos using the Ising model. Manipulating network structure and interaction strengths sharply alters the speed of quantum information propagation and scrambling within the system. Their investigation, employing out-of-time-order correlators, Krylov complexity, and spectral analysis, reveals a clear correlation between a reduced Thouless time and accelerated information scrambling, providing a unified framework for understanding thermalization and non-equilibrium dynamics in quantum many-body systems. Information scrambling rates reveal quantum chaos transitions in spin networks Out-of-time-order correlators, or OTOCs, quantify how rapidly information within a quantum system becomes unpredictable, functioning similarly to shuffling a deck of cards to assess randomness. In the conof quantum mechanics, OTOCs measure the sensitivity of a system to perturbations, effectively gauging how quickly initial information is lost into the many-body system. Calculating OTOCs for Ising spin networks enabled researchers to track the rate of information scrambling, a key indicator of the transition from orderly to chaotic quantum behaviour. This technique directly probes the system’s sensitivity to initial conditions, a hallmark of chaos, where subtle changes in the starting state lead to dramatically different outcomes, and OTOCs capture this effect by quantifying the growth of initial perturbations. The exponential growth rate of these perturbations, extracted from the OTOC, is directly related to the quantum Lyapunov exponent, a measure of the rate of chaos. The underlying principle relies on the fact that in chaotic systems, even a tiny change in initial conditions will rapidly diverge, m
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quantum-computingUlich GmbH Team Integrates Reverse Annealing for Enhanced Combinatorial Optimisation
Lucas Joshua Menger of ulich GmbH and colleagues at Goethe University have shown that combining forward and reverse annealing consistently improves solution quality and efficiency across several problem types. Their systematic experimental study on a D-Wave Advantage system reveals that reverse annealing offers key benefits, particularly for larger and more complex problems where standard forward annealing struggles. These results highlight the potential of reverse annealing to unlock greater computational power from quantum annealing hardware as it develops towards tackling real-world applications. Reverse annealing achieves substantial energy reduction in quantum optimisation A reduction of 2x 10⁻²⁴ Joules in energy occurred through the implementation of reverse annealing, as demonstrated by scientists at ulich GmbH and Goethe University. This improvement unlocks the potential to solve optimisation problems of a scale and complexity previously considered computationally impossible, now making larger, more intricate problems tractable. Quantum annealing operates on the principle of adiabatic quantum computation, where a quantum system starts in a known ground state and is slowly evolved to represent the problem’s cost function. The system then ideally settles into the ground state of the cost function, representing the optimal solution. However, real quantum annealers are susceptible to noise and have limited coherence times, leading to errors. The energy landscape of complex optimisation problems often contains shallow energy gaps, making it difficult for the system to reliably find the true ground state. Reverse annealing aims to mitigate these issues by, after an initial forward annealing run, briefly reversing the annealing process before continuing forward. This ‘backtracking’ allows the system to escape local minima and explore a wider range of potential solutions. A systematic study on a D-Wave Advantage system revealed that combining forward and reverse app
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quantum-computingAlan Baratz, CEO of D-Wave
Quantum PeopleAlan BaratzThe computer scientist who once shepherded Java into the Fortune 500, now steering the oldest name in commercial quantum computing.CEO of D-Wave QuantumMIT computer science PhDFormer JavaSoft presidentQuantum annealing advocateIn this articleWho Alan Baratz isFrom JavaSoft to the C-suiteArriving at D-WaveThe annealing betGoing public on the marketsThe feud with NvidiaThe quantum supremacy debateThe 2026 gate-model turnLeadership style and the road aheadWhy Alan Baratz matters in quantum computingFrequently asked questionsAlan Baratz at a glanceCurrent rolePresident and CEO, D-Wave QuantumBecame CEOEffective January 1, 2020Joined D-Wave2017DoctorateComputer science, MITUndergraduateMathematics and computer science, UCLANotable past roleFirst president of JavaSoft, Sun MicrosystemsCompany tickerNYSE: QBTSFlagship technologyAdvantage and Advantage2 annealing systems2026 expansionAcquired Quantum Circuits Inc and unveiled a dual-rail gate-model roadmapWho Alan Baratz isAlan Baratz runs the company that turned quantum annealing from a physics curiosity into a commercial product companies can buy time on today. He became president and chief executive of D-Wave Quantum at the start of 2020, but only after years inside the business building its software, its applications, and its research pipeline. That order matters, because the person now selling D-Wave’s machines is the same one who helped design what they do.By training Alan Baratz is a computer scientist rather than a physicist, and that background colors how he talks about the field. He frames quantum computing less as a distant scientific marvel and more as an engineering problem with customers, deadlines, and budgets attached. Under his watch, D-Wave has leaned hard into the argument that quantum machines can deliver useful results today, not only in some far-off corrected-error era.Baratz holds a doctorate in computer science from the Massachusetts Institute of Technology and an undergradua
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quantum-computingD-Wave Quantum (NYSE: QBTS) Trades At 200x Revenue As Valuation Raises Red Flags For Investors - foreignpolicyjournal.com
D-Wave Quantum (NYSE: QBTS) Trades At 200x Revenue As Valuation Raises Red Flags For Investors foreignpolicyjournal.com
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quantum-computingHumar and Colleagues Models Resonant Domain Growth for Understanding Metastable States
Scientists have revealed a new regime where domain growth sharply exceeds nucleation in the dynamics of metastable states, a phenomenon central to diverse fields ranging from cosmology to quantum matter. Gregor Humar and colleagues at the Institute of Science and Technology Austria (ISTA), in collaboration with Technical University of Munich, Jülich Supercomputing Centre, Italy’s National Institute, CENN Nanocenter, University of Leeds, Jozef Stefan Institute, National Institute for Nuclear Physics (INFN), University of Cologne, University of Ljubljana, University of Padova, and Munich Centre for Quantum Science and Technology (MCQST) utilised a 4000-qubit quantum annealer to model a two-dimensional quantum Ising model, demonstrating resonant expansion of true-vacuum domains. The findings establish a growth-dominated regime of false vacuum decay and highlight the potential of large-scale quantum simulation to explore nonequilibrium dynamics relevant to quantum field theory, cosmology, and strongly correlated matter. Quantum annealing simulates false vacuum decay via a two-dimensional Ising model The technique at the heart of this work employed a quantum annealer, a specialised computational device engineered to identify the lowest energy state of a given system, containing over 4000 qubits, the quantum analogue of classical bits. This substantial number of qubits facilitated the modelling of a two-dimensional quantum Ising model, a simplified yet powerful mathematical representation of interacting magnetic spins, conceptually similar to a grid of microscopic compass needles exerting influence upon one another. The Ising model serves as a valuable proxy for understanding more complex physical systems exhibiting phase transitions and collective behaviour. This approach allowed the researchers to simulate a false vacuum decay, a process describing the transition of a system from a metastable, seemingly stable state to a genuinely stable, lower-energy state. By observin
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quantum-computingDao and Colleagues Develop Lorentz 2DRNN Neural Quantum States for 2D Transverse Field Ising Models
H. L. Dao and colleagues have shown that hyperbolic neural quantum states (NQS) offer superior performance compared to Euclidean NQS when simulating many-body quantum physics systems. The study introduces the first two-dimensional hyperbolic neural quantum state, a Lorentz 2DRNN, and benchmarks it against a Euclidean 2DRNN using the 2D Transverse Field Ising Model with lattice sizes up to N=12. The findings reveal that hyperbolic NQS definitively outperform Euclidean NQS at the phase transition point, where the system’s physics aligns with conformal field theory and is dual to an Anti-de-Sitter space. Extending this work to one-dimensional systems, the researchers confirm that hyperbolic NQS also surpass Euclidean NQS, benefiting from both the system’s hierarchical structure and critical behaviour. This work highlights the potential of hyperbolic NQS as a key set of tools for tackling complex quantum systems, particularly those exhibiting structural hierarchy or criticality. Hyperbolic neural networks accelerate simulations of critical quantum systems A twelve-fold improvement in modelling quantum systems was achieved when employing a new hyperbolic neural network over existing Euclidean models. The Lorentz 2DRNN, the first two-dimensional hyperbolic neural quantum state, was applied to the 2D Transverse Field Ising Model with lattice sizes up to N=12, enabling accurate simulations of such systems at critical points previously considered computationally prohibitive. This work extended earlier findings, confirming that one-dimensional hyperbolic neural quantum states also outperform their Euclidean counterparts, benefiting from the inherent hierarchical structure of the system and its behaviour at phase transitions. The Lorentz 2DRNN’s superior performance was confirmed by evaluating it on the 2D Transverse Field Ising Model with lattice sizes up to N=12. Specifically when simulating the system at its critical point, this hyperbolic model consistently outperformed it
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quantum-computingIonQ vs. D-Wave (QBTS) vs. Quantum Computing (QUBT): Which Quantum Stock Is the Best Buy for 2H 2026? - TipRanks
IonQ vs. D-Wave (QBTS) vs. Quantum Computing (QUBT): Which Quantum Stock Is the Best Buy for 2H 2026? TipRanks
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quantum-computingD-Wave Targets 100 Logical Qubits With Million-Operation Fault Tolerance
D-Wave is shifting its focus from simply increasing physical qubit counts to achieving a more practical measure of quantum computing power: 100 logical qubits capable of more than one million error-corrected operations. The company reports this milestone is a key goal in its gate-model roadmap, signaling a maturity in approach and a prioritization of reliable computation over sheer scale. D-Wave intends to reach this benchmark by combining superconducting dual-rail qubits with built-in error detection, on-chip cryogenic control, and a forthcoming error-aware simulator designed to help developers prepare for this new era of quantum computing. D-Wave’s Dual-Rail Qubits Enable Real-Time Error Detection D-Wave is targeting a ten-fold improvement in error reduction rates with its novel qubit architecture. This ambition isn’t about brute-force scaling, but about building a system that reliably executes operations, a principle central to D-Wave’s gate-model roadmap. A core component of this strategy is the development of superconducting dual-rail qubits, designed with built-in error detection capabilities. Unlike many current architectures, D-Wave’s approach aims to identify approximately 90% of errors as they occur, significantly reducing the resources needed for quantum error correction. “Error awareness can directly improve the economics, scalability, and practicality of fault-tolerant quantum computing,” explains Trevor Lanting, Chief Development Officer, R&D at D-Wave. This proactive error detection contrasts with systems that attempt to correct errors after they accumulate, potentially requiring exponentially more qubits to achieve reliability. D-Wave has already demonstrated 99.9% two-qubit fidelities, corresponding to physical error rates of roughly one error per 1,000 operations, a performance level they believe will allow for quantum error-correction cycles running 100 to 1,000 times faster than those found in neutral-atom or trapped-ion systems. The company
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