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Quantum Annealing: D-Wave Systems & Optimization Applications

Quantum annealing news: D-Wave Advantage systems, optimization problems, hybrid algorithms. QUBO formulations & commercial deployments.

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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.

An error-mitigated quantum annealing solution for the weighted Max-Cut problem on a cubic latticequantum-computing

An error-mitigated quantum annealing solution for the weighted Max-Cut problem on a cubic lattice

--> Quantum Physics arXiv:2608.15094 (quant-ph) [Submitted on 15 Aug 2026] Title:An error-mitigated quantum annealing solution for the weighted Max-Cut problem on a cubic lattice Authors:Y. S. Yang, P. Tyson, A. B Murphy View a PDF of the paper titled An error-mitigated quantum annealing solution for the weighted Max-Cut problem on a cubic lattice, by Y. S. Yang and 2 other authors View PDF Abstract:The weighted Max-Cut problem is an NP-hard problem with application implications. It is investigated on a cubic lattice with 113 nodes and mixed-signed random edge weights. For a fixed upper bound on edge weights, it has been demonstrated that the computational difficulty increases as the lower bound on edge weights becomes more negative. The solution time for the problem using a novel error-mitigated quantum annealing approach is compared with standard D-Wave quantum annealing (QA) and BQM hybrid solvers, as well as various classical solvers. For the QPU-embeddable weighted Max-Cut instances with mixed-signed edge weights, it has been quantitatively demonstrated that the SEMO (spin-error mitigation for optimisation) error-mitigated quantum annealing achieved substantially shorter time-to solution than standard D-Wave QA, D-Wave BQM, simulated annealing and Tabu search baselines. The error-mitigated quantum annealing approach presented in this article potentially elevates the efficiency and application scope of quantum annealing and would be applicable in solving other discrete optimisation problems that can be formulated as QUBO or Ising instances. The promising solution time advantage would be particularly impactful for time-critical optimisation applications. Comments: Subjects: Quantum Physics (quant-ph); Mathematical Physics (math-ph) Cite as: arXiv:2608.15094 [quant-ph]   (or arXiv:2608.15094v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.15094 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission histor

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A finance expert joins D-Wave as it scales quantum computingquantum-computing

A finance expert joins D-Wave as it scales quantum computing

D-Wave Quantum Inc. has appointed Kevan P. Krysler, formerly CFO of NYSE-listed Everpure, Inc., to its Board of Directors and Audit Committee, signaling a sharpened focus on financial rigor as the publicly traded quantum computing firm (NASDAQ: QBTS) scales. Krysler currently leads finance at Carbon Robotics, a company building AI-powered agricultural robots, which parallels D-Wave’s increasing emphasis on practical quantum applications beyond research, the company says. “D-Wave is at an exciting stage of growth, with a differentiated technology portfolio and accelerating commercial traction,” said Krysler, adding that his experience will support the company’s continued execution and growth strategy. Kevan Krysler Joins D-Wave Board Amidst Company Scaling Kevan P. Krysler’s appointment signals a heightened focus on financial rigor for D-Wave (NASDAQ: QBTS), a strategy less common among early-stage quantum technology companies that often remain private. Prior to Carbon Robotics, Krysler served as senior vice president of finance and chief accounting officer at VMware, Inc., a major player in cloud infrastructure; this background is increasingly relevant as quantum computing transitions toward delivery as a service. Dr. Alan Baratz, CEO of D-Wave, stated that as adoption of their production-grade annealing quantum computing technology scales and their dual-platform strategy advances, Krysler will be a valuable addition. Sharon Holt, chair of the D-Wave board, emphasized Krysler’s value, stating that his perspectives on capital allocation, operating discipline and navigating complex growth environments will complement the board’s existing expertise. Krysler himself expressed enthusiasm, saying. D-Wave is at an exciting stage of growth, with a differentiated technology portfolio and accelerating commercial traction, as quantum computing becomes an important part of the enterprise technology landscape. Kevan P. Krysler, CFO at Carbon Robotics Source: https://www.business

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Got $200? 1 Quantum Computing ETF to Buy Right Now.quantum-computing

Got $200? 1 Quantum Computing ETF to Buy Right Now.

If you have $200 and want to point it at something that feels like the future, Defiance Quantum ETF (QTUM +0.36%) is a serious contender instead of a science project. When you buy this ETF, you're buying a basket of companies that are already building, selling, and using the hardware and software that could redefine what "computing power" means over the next couple of decades. ExpandNASDAQ: QTUMDefiance Quantum ETFToday's Change(0.36%) $0.57Current Price$159.25Key Data Points*:nth-last-child(-n+2)]:border-b-0">AUM$5.9BDividend Yield0.74%Expense Ratio0.40%*:nth-last-child(-n+2)]:border-b-0">Top HoldingsARQQ2.06%NET1.59%ESTC1.58% What QTUM owns QTUM tracks the BlueStar Machine Learning and Quantum Computing Index, which sounds abstract until you look at what's inside. The fund holds around 80 to 90 stocks tied to quantum computing and advanced machine learning, with most of the weight in technology names that already ship products and services. You get pure play quantum companies like D-Wave Quantum (QBTS -0.35%), IonQ (IONQ +1.23%), and Rigetti (RGTI -0.05%), which are building different kinds of quantum machines and cloud services, alongside more established names that embed quantum and AI capabilities into chips, data platforms, and security tools. Also, companies in that index generally need to generate at least 50% of their revenue or operating activity from quantum-computing-related products or activities. Owning a single early-stage quantum stock is like betting your $200 on one lab's approach to physics. QTUM lets you spread that bet across multiple hardware and software paths, as well as larger firms that can absorb setbacks and keep funding research. You are buying the ecosystem, not one experiment. ExpandNYSE: IONQIonQToday's Change(1.23%) $0.57Current Price$46.83Key Data Points*:nth-last-child(-n+2)]:border-b-0">Market Cap$18BMarket cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded sh

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D-Wave Appoints Kevan Krysler to Board and Audit Committeequantum-computing

D-Wave Appoints Kevan Krysler to Board and Audit Committee

Insider Brief Press release – D-Wave Quantum Inc. (NASDAQ: 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 the appointment of veteran technology finance executive Kevan P. Krysler to its Board of Directors and Audit Committee. Krysler currently serves as chief financial […]

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D-Wave Could Unlock Massive Enterprise Demand but the Valuation Risk Is Realquantum-computing

D-Wave Could Unlock Massive Enterprise Demand but the Valuation Risk Is Real

D-Wave (QBTS +1.29%) is moving beyond experimental quantum research and into critical enterprise operations. Its hybrid computing framework and expansion into scientific simulation could open a major new growth phase, but uneven bookings, missed expectations, and an extreme valuation leave little room for error. Stock prices used were the market prices of Aug. 7, 2026. The video was published on Aug. 16, 2026. Read NextAug 13, 2026 •By Motley Fool TranscribingD-Wave Quantum (QBTS) Q2 2026 Earnings Call TranscriptAug 11, 2026 •By Daniel SparksD-Wave's Revenue Fell 44% in a Year. Its Market Value Rose 38%.Aug 11, 2026 •By Chris NeigerIs D-Wave Quantum a Buy? Here's What the Data Says.Aug 6, 2026 •By Johnny RiceWhy D-Wave Quantum Stock Just FellAug 5, 2026 •By Chris NeigerWhy D-Wave Stock Fell 25% in JulyAug 3, 2026 •By Rich SmithWhy D-Wave Quantum Stock Popped TodayAbout the AuthorRick is a Wall Street Journal best-selling author with over 20 years of experience trading stocks and options. The most authoritative publications, including Good Morning America, Washington Post, Yahoo Finance, MSN, Business Insider, NBC, FOX, CBS, and ABC News, cover his work. His passion is business, and he works tirelessly to deliver content in an easy-to-understand manner. In 2018, Rick wrote The Financially Independent Millennial to inspire his readers with his story about becoming financially independent at age 35 despite not learning about money when he was younger. His books are easy to read and often refer to key points that “He would tell his younger self.” When not thinking about business, Rick writes (mainly about cruise ship travel) for his travel blog and is an enthusiast of fast cars, technology, & cooking.CMFrickorfordStocks MentionedD-Wave QuantumNASDAQ: QBTS$21.17(+1.29%)+$0.27Motley Fool Stock Advisor’s Latest PickGet Access---% Avg Return*Average returns of all recommendations since inception. Cost basis and return based on previous market day close.

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Open system probes of renormalization group flowquantum-computing

Open system probes of renormalization group flow

--> Quantum Physics arXiv:2608.13664 (quant-ph) [Submitted on 13 Aug 2026] Title:Open system probes of renormalization group flow Authors:Andrew Keefe, Brenden Bowen, Saptarshi Biswas, Albion Lawrence, Nishant Agarwal, Archana Kamal View a PDF of the paper titled Open system probes of renormalization group flow, by Andrew Keefe and 5 other authors View PDF HTML (experimental) Abstract:Open system probes can provide an efficient means to characterize quantum many-body systems by employing them as engineered environments. The key idea is to map long-range spatial correlations of the environment onto dynamical correlations in the evolution of a simple quantum probe. Using the example of a qubit coupled to a transverse-field Ising model, we show how the non-Markovian rate or spectral flow can be used to identify stable and unstable fixed points, infer scaling dimensions of relevant fields, and deduce the renormalization group flow induced by deformations around any fixed point. Comments: Subjects: Quantum Physics (quant-ph); Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Statistical Mechanics (cond-mat.stat-mech); High Energy Physics - Theory (hep-th) Cite as: arXiv:2608.13664 [quant-ph]   (or arXiv:2608.13664v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2608.13664 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Archana Kamal [view email] [v1] Thu, 13 Aug 2026 18:03:49 UTC (1,088 KB) Full-text links: Access Paper: View a PDF of the paper titled Open system probes of renormalization group flow, by Andrew Keefe and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-08 Change to browse by: cond-mat cond-mat.mes-hall cond-mat.stat-mech hep-th References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation

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Better Quantum Computing Stock: IBM or D-Wave?quantum-computing

Better Quantum Computing Stock: IBM or D-Wave?

IBM (IBM -1.19%) and D-Wave (QBTS +1.29%) are racing toward commercial quantum adoption, but one has financial strength, enterprise reach, and the ability to withstand a long development cycle that could prove decisive. This video explores why the ultimate quantum winner may not be the company that arrives first, but the one that stays in the race the longest. Stock prices used were the market prices of Aug. 13, 2026. The video was published on Aug. 13, 2026. Read NextAug 15, 2026 •By Will Healy2 Tech Dividend Stocks That Offer High Yields and Payout GrowthAug 14, 2026 •By Seena HassounaArista Networks vs. International Business Machines: Which Technology Stock Is a Better Buy in 2026?Aug 14, 2026 •By Matt Frankel, CFP®10 Best Blockchain Stocks for 2026 and How to InvestAug 10, 2026 •By Anders Bylund9 Best Quantum Computing Stocks for 2026 and How to InvestAug 10, 2026 •By David Jagielski, CPAThis Is When IBM's CEO Says Quantum Computing Could Start to Have a "Measurable Impact" on Its Bottom LineAug 3, 2026 •By Daniel SparksIBM Has Fallen 33% From Its High and Yields 3%. Here's What That Dividend Actually Costs the Company.About the AuthorRick is a Wall Street Journal best-selling author with over 20 years of experience trading stocks and options. The most authoritative publications, including Good Morning America, Washington Post, Yahoo Finance, MSN, Business Insider, NBC, FOX, CBS, and ABC News, cover his work. His passion is business, and he works tirelessly to deliver content in an easy-to-understand manner. In 2018, Rick wrote The Financially Independent Millennial to inspire his readers with his story about becoming financially independent at age 35 despite not learning about money when he was younger. His books are easy to read and often refer to key points that “He would tell his younger self.” When not thinking about business, Rick writes (mainly about cruise ship travel) for his travel blog and is an enthusiast of fast cars, technology, & cooking.CMFr

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Jim Cramer Was Happy The Market “Cared” About D-Wave Quantum – But Not How You Thinkquantum-computing

Jim Cramer Was Happy The Market “Cared” About D-Wave Quantum – But Not How You Think

Jim Cramer Was Happy The Market “Cared” About D-Wave Quantum – But Not How You Think Ramish Cheema Sat, August 15, 2026 at 5:28 PM EDT 3 min read QBTS +1.29% SMR -4.67% D-Wave Quantum Inc. (NYSE:QBTS)'s shares are up by 24% over the past year and are down by just as much year-to-date. Cramer hasn't held back discussing the firm and in most appearances, he has stressed the need to evaluate the firm's business. As D-Wave Quantum Inc. (NYSE:QBTS) reported its second quarter earnings on August 6th, the firm's shares closed the day 9% lower. Cramer discussed the move and wondered whether the market was becoming realistic about D-Wave Quantum Inc. (NYSE:QBTS): "What's incredible is that we never cared about earnings before, for this, for NuScale. And suddenly we care, I don't get it. I just think again, once again, there's kind of a realism sinking in. Let's, a lot of these guys have had such big moves. They're not based on anything. Or, let's just take some profits. I think the Situational mindset is going to be with us for a long time. Jim Cramer Tells Caller Down 15% on Netflix to Average Down While the shares closed lower, D-Wave Quantum Inc. (NYSE:QBTS)'s earnings came with several key figures that support the bullish argument. Arguably, the strongest of these was the firm's first half of 2026 bookings. These surged by 1,120% to $35.5 million. The bookings surge came due to purchase and enterprise agreements with Florida University and a Fortune 100 company. Additionally, 62% of D-Wave Quantum Inc. (NYSE:QBTS)'s second quarter revenue of $3.1 million was accounted for by commercial customers. Both these factors, along with a 668% jump in remaining performance obligations (RPOs), created solid avenues for tailwinds should the firm demonstrate sustained performance in the future. Yet, on the flip side, D-Wave Quantum Inc. (NYSE:QBTS)'s second quarter revenue dipped by 0.6% annually and came after its Q1 revenue had dropped by 81% to $2.

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Quantum Zeitgeist Weekly Digestquantum-computing

Quantum Zeitgeist Weekly Digest

Welcome to this week’s quantum technology digest. The articles below cover advances across the quantum computing stack, from hardware development and error correction to algorithmic improvements and commercial growth. Several companies reported significant progress this week, indicating continued momentum in the field. This week’s updates demonstrate a clear focus on scaling and refinement. Quantinuum features prominently with announcements regarding both hardware manufacturing partnerships and algorithmic efficiency gains. Other companies, including IonQ and Pasqal, are pushing boundaries in error correction and qubit control. Funding news from D-Wave and Infleqtion’s strong revenue growth further illustrate increasing investment and market demand. Overall, this week highlights practical steps toward building more capable and accessible quantum systems. Progress isn’t limited to a single approach; diverse modalities – superconducting, trapped ion, and neutral atom – all saw encouraging developments. The increasing availability of quantum resources on cloud platforms like Oracle also suggests a move toward wider accessibility for researchers and developers. 1. Quanta Computer & Quantinuum Partner to Scale Quantum Computing Hardware Quantinuum and Quanta Computer are collaborating to manufacture infrastructure for large-scale quantum computers. The partnership combines Quantinuum’s quantum technology with Quanta’s manufacturing expertise, shifting focus from research toward deployable systems. This co-development effort aims to improve the modularity and scalability of quantum processors, supporting Quantinuum’s roadmap for fault-tolerant quantum systems. Quanta’s experience in industrializing advanced computing will establish supply chains and manufacturing processes needed for wider quantum access. Read more 2. IBM’s QOBLIB Library Demonstrates Quantum Advantage in Optimization IBM and its partners announced demonstrations of quantum advantage in optimization t

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Global Consortium Launches Quantum Optimization Benchmarking Library (QOBLIB) to Track Path to Quantum Advantagequantum-computing

Global Consortium Launches Quantum Optimization Benchmarking Library (QOBLIB) to Track Path to Quantum Advantage

An international research consortium led by IBM Quantum, Zuse Institute Berlin (ZIB), Technische Universität Berlin, and Purdue University—alongside global academic and industrial partners—has introduced the Quantum Optimization Benchmarking Library (QOBLIB). Published in Nature Computational Science (“The Quantum Optimization Benchmarking Library“), the open-source initiative establishes a standardized, model-independent benchmarking framework to evaluate quantum, classical, and hybrid algorithms across ten NP-hard combinatorial optimization problem classes. [ QOBLIB Model-Independent Benchmarking Stack ] │ ┌────────────────────────────────────────┼────────────────────────────────────────┐ ▼ ▼ ▼ The "Intractable Decathlon" Open-Source Repository & Web Portal Cross-Paradigm Evaluation • 10 Hard Combinatorial Classes. • 1,260+ Curated Problem Instances. • Head-to-Head Solver Tracking. • 20 to 3,000,000+ Variables. • 2,600+ Benchmark Submissions. • Classical MIP/QUBO Baselines. • MIP, ILP, MIQP, & QUBO Formulations. • Live Best-Known Solution Tracking. • Near-Term Quantum Hardware Runs. Structuring the “Intractable Decathlon” QOBLIB addresses a critical gap in quantum optimization: while heuristic algorithms like the Quantum Approximate Optimization Algorithm (QAOA) or quantum annealing lack theoretical performance guarantees, empirical advantage claims require rigorous comparisons against state-of-the-art classical solvers. The library curates 1,264 specific instances spanning ten problem classes that become computationally hard for classical solvers at scales ranging from tens to tens of thousands of decision variables: Market Split (Multidimensional Subset Sum): Hard binary integer linear programming (ILP) instances with dense constraint matrices (20–140 variables). Low-Autocorrelation Binary Sequences (LABS): A canonical spin-glass benchmark with applications in radar and signal processing (2–100 variables). Minimum Birkhoff Decomposition: Doubly stochasti

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Shots-to-Approximate-Solution Scaling in Neutral-Atom Quantum Optimizationquantum-computing

Shots-to-Approximate-Solution Scaling in Neutral-Atom Quantum Optimization

--> Quantum Physics arXiv:2608.12858 (quant-ph) [Submitted on 13 Aug 2026] Title:Shots-to-Approximate-Solution Scaling in Neutral-Atom Quantum Optimization Authors:Junwoo Jung, Jaewook Ahn View a PDF of the paper titled Shots-to-Approximate-Solution Scaling in Neutral-Atom Quantum Optimization, by Junwoo Jung and Jaewook Ahn View PDF HTML (experimental) Abstract:Whether neutral-atom quantum optimization protocols exhibit genuine concentration toward low-energy solution structure remains an open question. Here, we introduce a shots-to-approximate-solution metric, STS(r), where r denotes the approximation ratio, and evaluate it using postprocessed outputs modeled by a degeneracy-weighted shell distribution governed by a single effective parameter, $\beta$, that quantifies concentration toward near-optimal independent sets. To extract the genuine concentration effect in the quantum data, we apply identical postprocessing to both experimental bitstrings and randomly generated bitstrings with matched excitation density, thereby constructing an excitation-matched random baseline. Experiments on programmable Rydberg-atom arrays with system sizes up to 125 sites show that quantum annealing consistently exceeds the random baseline, demonstrating enhanced concentration toward low-energy solution structure beyond what can be attributed solely to excitation density. The results further reveal two distinct target-dependent regimes. For near-exact targets with $r \approx 1$, the required shot count grows exponentially with system size and is reduced at the same exponential level by quantum annealing within the shell-model description. By contrast, for relaxed targets, the shot cost becomes effectively constant, and the corresponding quantum enhancement diminishes, with the classical postprocessing heuristic alone reaching the target in order-unity attempts. Together, these results establish an operational method for quantifying quantum optimization performance and clarify the reg

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Quantinuum’s Backpropagation Algorithm Rivals Sparse Pauli Simulation Speedquantum-computing

Quantinuum’s Backpropagation Algorithm Rivals Sparse Pauli Simulation Speed

Quantinuum researchers have developed a new backpropagation algorithm that significantly reduces the memory demands of optimizing quantum circuits. The method achieves a reduction in memory cost by a factor of n compared with conventional reverse-mode automatic differentiation, where n denotes the number of parameters in the circuit. This advance maintains gradient accuracy comparable to the corresponding observable expectation values while matching the computational complexity of sparse Pauli simulation. Demonstrating the algorithm’s scalability, the team successfully optimized circuits for transverse-field Ising models in one, two, and three dimensions, as well as the three-dimensional Heisenberg model, and compressed two-dimensional time-evolution circuits; this level of complexity exceeds many early quantum algorithm demonstrations. Sparse Pauli Dynamics and Operator Non-Stabilizerness Recent advances in quantum algorithm design increasingly rely on efficient optimization of quantum circuits, but a significant hurdle remains: the increasing computational demands of calculating parameter gradients. Researchers at Quantinuum have addressed this challenge with a novel backpropagation algorithm centered around sparse Pauli dynamics (SPD), a method for simulating quantum systems by tracking operator evolution in the Heisenberg picture. This approach offers an alternative to traditional methods limited by memory and computational cost. The core innovation lies in leveraging the inherent reversibility of quantum circuits to reduce the memory cost by a factor of n, where n denotes the number of parameters in the circuit. The team demonstrated the method by optimizing low-energy state-preparation circuits for transverse-field Ising models in one, two, and three dimensions and for the three-dimensional Heisenberg model, and by compressing two-dimensional time-evolution circuits. Importantly, this memory reduction does not compromise accuracy. The computational complexity

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D-Wave Awarded National Research Council of Canada Funding to Advance Commercial Annealing Quantum Computingquantum-computing

D-Wave Awarded National Research Council of Canada Funding to Advance Commercial Annealing Quantum Computing

Insider Brief PRESS RELEASE — D-Wave Quantum Inc. (NASDAQ: 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 awarded up to CAD $300,000 in funding from the National Research Council of Canada’s (NRC) Applied Quantum Computing Challenge program to advance annealing […]

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D-Wave Awarded NRC Funding to Advance Commercial Annealing Algorithms and Softwarequantum-computing

D-Wave Awarded NRC Funding to Advance Commercial Annealing Algorithms and Software

D-Wave Awarded NRC Funding to Advance Commercial Annealing Algorithms and Software Quantum computing provider D-Wave Quantum Inc. (NASDAQ: QBTS) has been awarded up to CAD $300,000 ($299,025) in funding from the National Research Council of Canada’s (NRC) Applied Quantum Computing Challenge program. Conducted at D-Wave’s Quantum Centre of Engineering Excellence in Burnaby, British Columbia, the joint project aims to develop next-generation graph-embedding algorithms and open-source software tools tailored for commercial quantum annealing systems. The collaboration focuses on engineering advanced graph minor-embedding algorithms optimized for D-Wave’s Zephyr™ chip topology. Minor embedding is the foundational mathematical translation layer that maps complex, highly connected discrete optimization problems onto the physical qubit graph of an annealing QPU. By integrating these algorithms directly into D-Wave’s open-source Ocean™ Software Development Kit (SDK), the project aims to expand the scale, variable density, and problem complexity that can be solved natively on the company’s 4,400+ qubit Advantage2™ quantum annealing processors across commercial domains such as supply chain logistics, manufacturing scheduling, financial portfolio optimization, machine learning, and quantum chemistry simulations. [ NRC & D-Wave Algorithmic Embedding Pipeline ] │ ┌─────────────────────────────────┴─────────────────────────────────┐ ▼ ▼ Open-Source Ocean™ SDK Integration Zephyr™ Topology Hardware Execution • Advanced Graph Minor-Embedding Algorithms. • 4,400+ Qubit Advantage2™ Annealing Systems. • Translates High-Density Graph Problems. • 20-Way Inter-Qubit Connectivity (Zephyr). • Optimizes Ising & QUBO Problem Formulations. • Enables Larger Scale Logistics & Simulation. Operating under Canada’s broader National Quantum Strategy, the NRC’s Applied Quantum Computing Challenge program aligns federal research infrastructure with domestic industry leaders to accelerate th

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Canada funds D-Wave to boost quantum computing software toolsquantum-computing

Canada funds D-Wave to boost quantum computing software tools

D-Wave Quantum has secured CAD $300,000 in funding from the National Research Council of Canada to refine software for its Advantage2 annealing quantum computers. The investment, awarded through the Applied Quantum Computing Challenge program, will focus on new graph minor-embedding algorithms designed to map complex optimization problems to D-Wave’s Zephyr topology. These algorithms will be integrated into D-Wave’s open-source Ocean software development kit, expanding the scale of computations possible across fields like logistics and machine learning. “Software innovation is essential to expanding the performance and commercial impact of quantum computing,” said Dr. Trevor Lanting, chief development officer at D-Wave. NRC Funding Supports Advantage2 Algorithm Development D-Wave’s team in Burnaby, British Columbia, will focus on developing new graph minor-embedding algorithms tailored for the Zephyr topology of the Advantage2 system. These algorithms are critical for translating real-world optimization problems into a format the quantum computer can process, and improvements are expected to expand the scale of solvable problems. This collaboration between D-Wave and the NRC underscores a commitment to applied quantum computing, bringing together government, industry, and academia to accelerate commercialization. The resulting software is intended to enable customers to tackle optimization problems previously beyond the reach of D-Wave’s Advantage2 systems, strengthening Canada’s position in the rapidly evolving field of quantum technology and expanding the range of potential applications. Software innovation is essential to expanding the performance and commercial impact of quantum computing. Dr. Trevor Lanting, chief development officer at D-Wave Source: https://www.dwavequantum.com/company/newsroom/press-release/d-wave-awarded-national-research-council-of-canada-funding/ Stay currentSee today’s quantum computing news on Quantum Zeitgeist for the latest breakthrou

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Quantum Resource Comparison for Two Leading Surface Code Lattice Surgery Approachesquantum-computing

Quantum Resource Comparison for Two Leading Surface Code Lattice Surgery Approaches

AbstractHamiltonian simulation is one of the most promising candidates for the demonstration of quantum advantage within the next ten years, and several studies have proposed end-to-end resource estimates for executing such algorithms on fault-tolerant quantum processors. Usually, these resource estimates are based upon the assumption that quantum error correction is implemented using the surface code, and that the best surface code compilation scheme involves serializing input circuits by eliminating all Clifford gates. This transformation is thought to make best use of the native multi-body measurement (lattice surgery) instruction set available to surface codes. Some work, however, has suggested that direct compilation from Clifford+T to lattice surgery operations may be beneficial for circuits that have high degrees of logical parallelism. In this study, we analyze the resource costs for implementing Hamiltonian simulation using example approaches from each of these leading surface code compilation families. The Hamiltonians whose dynamics we consider are those of the transverse-field Ising model in several geometries, the Kitaev honeycomb model, and the $\mathrm{\alpha-RuCl_3}$ complex under a time-varying magnetic field. We show, among other things, that the optimal scheme depends on whether Hamiltonian simulation is implemented using the quantum signal processing or Trotter-Suzuki algorithms, with Trotterization benefiting by orders of magnitude from direct Clifford+T compilation for these applications. Our results suggest that surface code quantum computers should not have a one-size-fits-all compilation scheme, but that smart compilers should predict the optimal scheme based upon high-level quantities from logical circuits such as average circuit density, numbers of logical qubits, and T fraction.► BibTeX data@article{LeBlond2026quantumresource, doi = {10.22331/q-2026-08-10-2187}, url = {https://doi.org/10.22331/q-2026-08-10-2187}, title = {Quantum {R}esour

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This Trillion-Dollar AI Stock Offers Better Quantum Computing Exposure Than IonQ, Rigetti, or D-Wave at a Multi-Year Valuation Lowquantum-computing

This Trillion-Dollar AI Stock Offers Better Quantum Computing Exposure Than IonQ, Rigetti, or D-Wave at a Multi-Year Valuation Low

If you want quantum computing exposure without betting the farm on a pre‑profit science project, I think a case is building that Microsoft (MSFT +0.03%) is the more interesting option right now. Microsoft is a $3 trillion AI stock whose own quantum roadmap has matured quietly in the background, and with sentiment cooled after a year of worry about AI spending, you're getting that quantum upside at what looks like a multiyear valuation low instead of peak euphoria. Image source: Getty Images. Microsoft is already a quantum platform Microsoft doesn't market itself as a quantum stock, but its Azure Quantum materials read like a company that has spent years building a full stack. Azure Quantum is a cloud service where developers can run quantum programs today on hardware from partners such as IonQ (IONQ +11.86%), Rigetti (RGTI +8.53%), Quantinuum (QNT -0.29%), and Pasqal, or on advanced simulators, using the same Azure environment they use for AI and high-performance computing. That matters. Quantum is not off in a lab. It's already being wired into Microsoft's mainstream developer tools and cloud workflows. ExpandNASDAQ: MSFTMicrosoftToday's Change(0.03%) $0.13Current Price$499.99Key Data Points*:nth-last-child(-n+2)]:border-b-0">Market Cap$3.7TMarket cap calculated using publicly traded shares outstanding only. Does not include unlisted, private, or dual-class non-traded shares. Implied market cap may vary.Day's Range$498.73 - $505.1852wk Range$349.20 - $553.72Volume28.8MAvg Vol41.2MGross Margin67.94%Dividend Yield0.71% In its quantum overview, Microsoft describes Azure Quantum as an "open, flexible, and future-proofed path" that adapts to how customers actually work. The company is effectively acting as the orchestrator, sitting between enterprise demand and multiple hardware providers. That is a very different position from a single hardware vendor trying to persuade the world to come and build on its island. Azure Quantum Elements and the long game The part that re

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D-Wave Reports Its Q2 2026 Financial Results Showing Bookings Surge, Technical Progress, and Quantum Circuits Acquisition Impactquantum-computing

D-Wave Reports Its Q2 2026 Financial Results Showing Bookings Surge, Technical Progress, and Quantum Circuits Acquisition Impact

D-Wave Reports Its Q2 2026 Financial Results Showing Bookings Surge, Technical Progress, and Quantum Circuits Acquisition Impact D-Wave Quantum Inc. reported its financial results for the second quarter and first half of 2026, highlighting significant commercial momentum and rapid growth in long-term commitments. While quarterly revenue remained flat due to system sale timing, the company saw dramatic expansion in its backlog, enterprise engagements, and technical roadmaps across both annealing and gate-model quantum architectures. The table below summarizes key financial metrics for Q2 2026 compared with the prior quarter (Q1 2026) and the year-ago quarter (Q2 2025). Amounts in $MQ2’2026Q1’2026Q2’2025% vs Q1’2026% vs Q2’2025Revenue$3.1$2.9$3.1+6.9%+0.0%Operating Expenses$55.0$56.5$28.5-2.7%+93.0%Operating Loss($53.3)($54.7)($26.5)-2.6%+101.1%Net Loss($48.0)($18.4)($167.3)160.9%-71.3%Cash, Cash Equivalents and Investments$546.2$588.4$819.3-7.2%-33.3% Financial Performance & Commercial Expansion The primary highlight of the report is D-Wave’s massive increase in bookings and backlog. First-half bookings surged over 1,120% year-over-year to $35.5 million, anchored by a new $20 million system sale and $2.3 million contributed from the acquisition of Quantum Circuits, Inc. As a result, Remaining Performance Obligations (RPOs) jumped 668% to $40.7 million, with 57% expected to convert to recognized revenue over the next 12 months. Commercial adoption continues to shift from experimental pilots to production workloads. Commercial customers generated 67.7% of first-half revenue (up from 16.0% in H1 2025), with Forbes Global 2000 clients accounting for nearly half of total revenue. Production applications now represent 37.3% of total Quantum Computing as a Service (QCaaS) revenue. Net loss narrowed significantly to $48.0 million for the quarter, largely due to reduced non-cash warrant liability charges, while operating expenses grew to support aggressive R&D and go-

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Before claiming a Quantum advantage, what can classical computers already solve?quantum-computing

Before claiming a Quantum advantage, what can classical computers already solve?

When evaluating quantum algorithms for combinatorial optimization, the comparison is only meaningful if the classical baseline is taken seriously. I created a technical walkthrough examining how Gurobi, a state-of-the-art classical optimization solver, handles QUBO problems. The purpose is to establish a practical classical reference before moving on to quantum annealers and variational quantum algorithms. The video begins with weighted Max-Cut, derives its QUBO representation, and implements the resulting quadratic binary model in Python using gurobipy. It then explores: - exact versus heuristic approaches to QUBO; - Gurobi’s branch-and-bound search and bound convergence; - primal heuristics for finding high-quality incumbents; - why finding a solution and proving optimality are different tasks; - how MIPGap trades optimality guarantees for runtime; - why runtime depends heavily on the specific problem instance; - the effect of dense versus sparse QUBO matrices; - deterministic behavior under fixed parameters and hardware; - and practical access through Gurobi’s academic licensing. The larger point is not that classical solvers make quantum optimization unnecessary. It is that claims of quantum utility require carefully designed comparisons against highly optimized classical methods. A useful benchmark should consider more than wall-clock runtime: - solution quality and optimality gap; - instance distribution and graph density; - preprocessing and model-conversion costs; - time to the first good solution; - total time required to certify optimality; - solver parameter tuning; - hardware and reproducibility; - and end-to-end execution overhead. Video: https://youtu.be/TB1ny8o4ImQ I’d be interested in the community’s view: which classical baselines and metrics should be considered essential when benchmarking quantum annealing or variational algorithms on QUBO problems? submitted by /u/Future_Ad7567 [link] [comments]

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Ising Models Simulate Majorana Fermions in Black Hole Spacetimequantum-computing

Ising Models Simulate Majorana Fermions in Black Hole Spacetime

Researchers at the National Institute of Physics, University of the Philippines Diliman, have found that transverse-field Ising models, systems of interacting quantum spins, can effectively simulate Majorana fermions within the curved spacetime surrounding a Schwarzschild black hole. The study finds that four distinct mathematical representations of this spacetime, Schwarzschild, tortoise, Kruskal, and conformally flat, each map onto a different microscopic Ising spin model, yet all converge to the same Majorana field theory. This convergence, described as exhibiting an emergent form of general covariance, provides a rare example of a fundamental symmetry of general relativity arising as an emergent property of a condensed matter system. The authors further demonstrate how black hole particle production can be simulated and detected through spin correlation measurements, and discuss experimental platforms capable of realizing these models. The work establishes a practical route for investigating fermionic quantum field theory in curved spacetime using controllable quantum many-body systems and tabletop experiments. The assertion that distinct mathematical descriptions of the same physical spacetime can map onto fundamentally different microscopic models is now being validated through novel quantum simulations. This unexpected connection highlights a deep relationship between the mathematical tools used to describe spacetime and the underlying physical models that govern its behavior. This work builds upon the understanding that quantum field theory (QFT) emerges universally as an effective low-energy description of a broad class of quantum many-body systems. A new approach detailed in recent work suggests a pathway toward tabletop experiments utilizing condensed matter systems as analog gravitational environments. This work builds on previous findings, demonstrating how the Unruh effect can emerge in spin models representing an expanding universe. Their work details

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