Quantum Optimization & Logistics: Supply Chain & Routing Applications
Quantum optimization news: logistics, supply chain quantum, routing optimization, QAOA. Combinatorial optimization & enterprise deployments.
Optimization problems—finding the best solution among millions or billions of possibilities—represent the most immediate commercial application for quantum computing. Logistics, supply chain management, manufacturing, and transportation face combinatorial explosion where classical algorithms struggle.
Quantum approaches include quantum annealing solving optimization natively using quantum tunneling; QAOA (Quantum Approximate Optimization Algorithm) as a gate-based alternative; and quantum-inspired algorithms providing immediate business value on classical hardware.
India's Quantum Optimization Landscape
India's National Quantum Mission prioritizes optimization applications given the country's complex logistics challenges. The Indian Railways, the world's largest employer and passenger carrier, represents a prime use case for quantum scheduling optimization. The NQM Thematic Hub at IIT Bombay focuses on quantum algorithms for optimization problems.
Tata Consultancy Services (TCS) develops quantum optimization solutions for Indian enterprises including supply chain, logistics, and manufacturing applications. The Quantum Valley Tech Park in Andhra Pradesh, anchored by an IBM Quantum System Two with 156-qubit Heron processor, targets optimization applications among its use cases including supply chain resilience and energy optimization.
The NQM specifically targets quantum computing applications in optimization, with intermediate-scale quantum computers expected to demonstrate utility in logistics and scheduling problems within the mission timeline.
quantum-computingRan a compiler-generated 3-qubit bit-flip code on Rigetti's Cepheus-1-108Q via Braket, syndrome correctly identified the injected error in 87% of shots, 94.5% logical error recovery under hardware noise
I've been building QSHL (Quantum Self-Healing Language), a small compiler that emits OpenQASM 3.0 with error-correction circuits generated from a high-level specification rather than hand-wired syndrome logic. I wanted to validate the syndrome extraction on real hardware, not just simulators. Setup: 3-qubit bit-flip repetition code Two parity syndromes: s0 = parity(q0,q1) s1 = parity(q1,q2) Syndrome extraction via ancilla qubits Deliberate X error injected on q0 Expected syndrome: "10" Execution: Rigetti Cephus-1-108Q via Amazon Braket 100 shots Observed syndrome distribution: 10 (expected): 87% 11: 5% 00: 5% 01: 3% Using post-process syndrome decoding, the logical recovery rate was 94.5%. The non-ideal outcomes are consistent with real hardware effects: readout noise gate infidelity decoherence routing/SWAP overhead across the device topology For comparison, the same circuit executed deterministically on SV1 (1000/1000 expected outcomes), so the spread here is clearly hardware-driven. Important caveats: this is post-process decoding, not active fault tolerance not closed-loop real-time correction not a logical memory lifetime experiment distance-1 repetition code only Next steps are: mid-circuit measurement + conditional feedback repeated syndrome cycles higher-distance codes cross-hardware benchmarking Happy to answer questions about the compiler or lowering pipeline. submitted by /u/DestinyInDepth [link] [comments]
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quantum-computingSample-efficient benchmarking of shallow all-to-all random quantum circuits
--> Quantum Physics arXiv:2605.22909 (quant-ph) [Submitted on 21 May 2026] Title:Sample-efficient benchmarking of shallow all-to-all random quantum circuits Authors:Gregory Bentsen, Bill Fefferman, Soumik Ghosh, Michael J. Gullans, Yinchen Liu View a PDF of the paper titled Sample-efficient benchmarking of shallow all-to-all random quantum circuits, by Gregory Bentsen and Bill Fefferman and Soumik Ghosh and Michael J. Gullans and Yinchen Liu View PDF HTML (experimental) Abstract:Random circuit sampling (RCS) remains one of the most competitive frameworks for demonstrating quantum advantage in near-term noisy intermediate-scale quantum (NISQ) hardware. Unfortunately, absent error-correction, existing benchmarks to characterize these experiments, like linear cross-entropy, have been classically spoofed due to noise. Because of this, there are interesting regimes, like shallow-depth random quantum circuits, where sampling is plausibly classically intractable, but no existing benchmark can distinguish between a noisy quantum computer and an adversarial classical spoofer. In this paper, we demonstrate that the nonlinear cross-entropy provides a sample-efficient benchmark for shallow-depth all-to-all random quantum circuits whose score cleanly separates noisy quantum computers from state-of-the-art classical spoofers, even in the presence of depolarizing noise. Further, we develop a binary classifier based on the notion of heavy output generation that features logarithmic sample complexity at short depth. Our evidence comes from exact analytic expressions for all-to-all Brownian circuit ensembles derived using replica tricks, and numerical simulations that corroborate these results for discrete Haar-random unitary circuits. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.22909 [quant-ph] (or arXiv:2605.22909v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.22909 Focus to learn more arXiv-issued DOI via DataCite Submission
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quantum-computingEstimating Green's functions with a robust quantum Arnoldi method
--> Quantum Physics arXiv:2605.22920 (quant-ph) [Submitted on 21 May 2026] Title:Estimating Green's functions with a robust quantum Arnoldi method Authors:Jacob S. Nelson, Andrew B. Baczewski View a PDF of the paper titled Estimating Green's functions with a robust quantum Arnoldi method, by Jacob S. Nelson and Andrew B. Baczewski View PDF HTML (experimental) Abstract:Many applications of Green's functions (GFs) require their evaluation over intervals or at multiple points, motivating quantum algorithms that return an efficiently computable functional representation rather than mere point estimates. We introduce a robust quantum Arnoldi method (ROQAM) that achieves this goal. Its robustness is derived from formulation in terms of orthogonal polynomials, which preserves the upper-Hessenberg structure of the projected matrices despite finite-precision estimation. We also show that as the iteration depth increases, the precision required for matrix-element estimation can be reduced. Resource estimates for the spectral function of a quantum impurity model indicate that ROQAM outperforms pointwise estimation via quantum singular value transformation by multiple orders of magnitude. Finally, we show that the ROQAM can be used to estimate GFs at nonzero temperatures using only a single Krylov subspace. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.22920 [quant-ph] (or arXiv:2605.22920v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.22920 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jacob Nelson [view email] [v1] Thu, 21 May 2026 18:00:41 UTC (1,108 KB) Full-text links: Access Paper: View a PDF of the paper titled Estimating Green's functions with a robust quantum Arnoldi method, by Jacob S. Nelson and Andrew B. BaczewskiView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2026-05 Referen
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quantum-computingEntanglement-facilitated macroscopic cluster formation in quantum many-body dynamics
--> Quantum Physics arXiv:2605.22947 (quant-ph) [Submitted on 21 May 2026] Title:Entanglement-facilitated macroscopic cluster formation in quantum many-body dynamics Authors:Xiao Wang, Alexander Yosifov, Aditya Iyer, Jinzhao Sun View a PDF of the paper titled Entanglement-facilitated macroscopic cluster formation in quantum many-body dynamics, by Xiao Wang and 3 other authors View PDF HTML (experimental) Abstract:The capacity of a quantum many-body system to preserve global information -- encoded in the non-local correlations -- is a prerequisite for robust quantum computing. Unlike local degrees of freedom, large structures offer inherent resilience to noise, but their stability is often compromised by dynamical fragmentation and local excitations. In this work, we investigate under what initial conditions the quantum dynamics can sustain system-size cluster structures by examining false-vacuum decay dynamics in a 2D quantum Ising model. We find that while product states rapidly fragment into uncorrelated domains, initial-state entanglement suppresses the proliferation of true-vacuum bubbles and stabilises macroscopic connected clusters. We find that this passive stabilisation is not a mere consequence of entanglement entropy but rather depends on the specific pre-quench correlations. Our results establish a connection between initial-state preparation and the preservation of global structures, highlighting the role of entanglement for the passive protection of information in 2D quantum many-body simulation. Comments: Subjects: Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech); High Energy Physics - Lattice (hep-lat) Cite as: arXiv:2605.22947 [quant-ph] (or arXiv:2605.22947v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.22947 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jinzhao Sun [view email] [v1] Thu, 21 May 2026 18:21:14 UTC (1,851 KB) Full-text links: Ac
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quantum-computingClassical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
--> Quantum Physics arXiv:2605.23138 (quant-ph) [Submitted on 22 May 2026] Title:Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning Authors:Gino Kwun, Dhanvi Bharadwaj, Gokul Subramanian Ravi View a PDF of the paper titled Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning, by Gino Kwun and 2 other authors View PDF HTML (experimental) Abstract:Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren plateaus and numerous local minima. While classically simulable Clifford circuits can warm-start VQAs to accelerate convergence, existing heuristic-based initialization methods struggle to scale within vast combinatorial search spaces. To overcome this bottleneck, we propose CRiSP (a Clifford Reinforcement Learning agent for State Preparation), a framework that formulates discrete prefix selection as a sequential decision-making problem. CRiSP utilizes Neural-Guided Monte Carlo Tree Search, driven by a Transformer-based policy trained via self-play, to insert learned Clifford gates before fixed parameterized rotations. This enables the construction of high-quality initial states entirely through polynomial-time classical stabilizer simulation without altering the underlying circuit architecture. By integrating a curriculum learning strategy that progressively expands the search horizon, the agent efficiently scales to deep circuits. Evaluated on QAOA benchmarks of up to $22$ qubits and $1{,}370$ parameters, CRiSP outperforms state-of-the-art Clifford initialization methods by a mean of $3.17\times$ (max $45.02\times$) in average energy accuracy and $2.44\times$ (max $16.01\times$) in best-achieved energy accuracy. Assessments on VQE tasks further demonstrate the framework's robustness and generalizability. Comments: Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technol
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quantum-computingA General Quantum Speed Limit for Non-Hermitian Systems
--> Quantum Physics arXiv:2605.23250 (quant-ph) [Submitted on 22 May 2026] Title:A General Quantum Speed Limit for Non-Hermitian Systems Authors:Zhanxi Wang, Xiaozhe Hao, X. X. Yi View a PDF of the paper titled A General Quantum Speed Limit for Non-Hermitian Systems, by Zhanxi Wang and 2 other authors View PDF HTML (experimental) Abstract:The quantum speed limit (QSL) refers to the maximum speed of a quantum system to evolve from an initial state to its orthogonal states. The bound on the QSL for Hermitian systems, for example the Mandelstam-Tamm (MT) and Margolus-Levitin (ML) as well as Sun-Zheng(SZ) bound, was studied respectively from the perspectives of average value and variance of the system Hamiltonian as well as the geometry of the system. While the compactness of the MT-type, ML-type and SZ-type bounds has been examined well for Hermitian systems, a compact QSL for non-Hermitian systems has not been well studied. In this work, based on the biorthogonal basis theory we derive two distinct and tighter bounds on the QSL for non-Hermitian systems, which correspond to the MT and ML bounds for Hermitian systems. We show that the shortest evolution time corresponding to the two bounds of the non-Hermitian system can be attained by certain initial states, showing the compactness and tightness of our bounds. These initial states dubbed fastest initial states(FIS) are different from that in Hermitian systems. A bound close to QSL for non-FIS is presented and comparison of our bound with others in literature is performed. To illustrate our results, we present a minimal non-Hermitian system to show QSL, and the condition for the shortest evolution time is derived analytically using the present theory. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.23250 [quant-ph] (or arXiv:2605.23250v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.23250 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submiss
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quantum-computingQuantum Computing Stocks Short Interest Jumps Amid Valuation Concerns - Benzinga
Quantum Computing Stocks Short Interest Jumps Amid Valuation Concerns Benzinga
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quantum-computingPrediction: Quantum Computing Is 2026's Most Underrated Tech Trend
Quantum computing is often considered a speculative field, since those systems are still primarily used for niche government and research projects rather than mainstream computing applications. Quantum systems are much more powerful than classical computers, but they're also bigger, pricier, consume more power, and make more mistakes. But from 2025 to 2030, Grand View Research expects the global quantum computing market to expand at a 20.5% CAGR as smaller, more scalable, and more accurate systems hit the market. They could also be deployed for a broader range of purposes, including processing AI tasks, streamlining supply chains, developing new drugs, and strengthening cybersecurity services. Image source: Getty Images. That's why it wasn't surprising when the U.S. government recently ramped up its investments in the quantum computing market, sending many of those stocks soaring. On May 21, the U.S. Department of Commerce said it had signed nine letters of intent to provide $2.01 billion in federal incentives under the CHIPS and Science Act to support a portfolio of quantum computing companies. Let's see which companies will profit from those fresh government investments, and why they imply the growth of the quantum computing market is still an underrated tech trend right now. Which nine companies will benefit from those investments? The Department of Commerce has earmarked $1 billion for IBM (IBM +0.34%) and $375 million for GlobalFoundries (GS +0.87%) to build new domestic quantum computing foundries. ExpandNYSE: IBMInternational Business MachinesToday's Change(0.34%) $0.85Current Price$253.82Key Data PointsMarket Cap$239BDay's Range$253.45 - $264.3552wk Range$212.34 - $324.90Volume821.8KAvg Vol6.8MGross Margin57.80%Dividend Yield2.65% Those investments could breathe fresh life into both companies. IBM is expanding its hybrid cloud and AI businesses to offset the slower growth of its older software and hardware businesses, and the expansion of its quantum computi
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quantum-computingIs anyone working on QRAM?
QRAM sure would solve a lot problems for quantum algorithms. Yet I don’t know of anyone working on it. Is anyone working on it? submitted by /u/SurinamPam [link] [comments]
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quantum-computingQuantum Computing Company IonQ Is A Buy (Technical Analysis)
Walter Zelezniak Jr5.2K FollowersFollow5ShareSavePlay(11min)Comments(2)SummaryIonQ, Inc. demonstrates bullish technicals with strong price action, momentum, volume, and relative strength since summer 2024.IONQ reported Q1 2026 revenues of $64.67M, an 8x YoY increase, and raised full-year guidance to $270M, despite negative EPS and poor profitability.Technical indicators—30-week EMA, PPO momentum, and institutional volume—signal continued accumulation and outperformance versus the S&P 500.I am buying IONQ, using a stop-loss strategy below the 30-week EMA to manage downside risk amid ongoing unprofitability. spawns/iStock via Getty Images In this article will outline my bullish thesis for the quantum computing company IonQ, Inc. (IONQ). I will briefly discuss quantum computing, IONQ’s recent earnings report, and its valuation grade. Then I will outline my bullishThis article was written byWalter Zelezniak Jr5.2K FollowersFollowAs an individual investor nearing retirement I am trying to build my financial assets in order to have a fulfilling retirement. I am interested in trading both long and short; or at least using inverse ETFs, to take advantage of market declines. Having long term and short term trading strategies, proper execution of my trading plan, and absolute investing results are my goals. I see my articles as a way to keep me focused on developing winning trades. I also expect to learn much from the feedback that is provided in the comments section.Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, but may initiate a beneficial Long position through a purchase of the stock, or the purchase of call options or similar derivatives in IONQ over the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article. Seeking A
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quantum-computingUncle Sam's $2B Quantum Gift: Buy D-Wave, Skip Rigetti
Uttam Dey6.5K FollowersFollow5ShareSavePlay(9min)CommentsSummaryD-Wave Quantum (QBTS) is rated Buy, benefiting from the US DoC's $2B quantum industry investment, while Rigetti Computing (RGTI) remains Hold.The $2B federal funding, distributed as cash-for-equity across 9 companies, signals a strategic shift and accelerates US quantum industry development.QBTS stands out with a ~2000% backlog growth, unique quantum annealing approach, and a more attractive ~9x forward book value multiple versus RGTI's 14-15x.Risks include potential shareholder dilution, non-binding funding agreements, and a 6–18 month timeline before capital deployment. koto_feja/E+ via Getty Images Investment Thesis The US government announced a landmark $2B sovereign funding scheme that will see the US Commerce Dept. invest $2B worth of grants and equity throughout America’s quantum computing industry. The news, confirmed by theThis article was written byUttam Dey6.5K FollowersFollowUttam is a growth-oriented investment analyst whose equity research primarily focuses on the technology sector. Semiconductors, Artificial Intelligence and Cloud software are some of the key sectors that are regularly researched and published by him. His research also focuses on other areas such as MedTech, Defense Tech, and Renewable Energy. In addition, Uttam also authors The Pragmatic Optimist Newsletter along with his wife, Amrita Roy, who is also an author on the newsletter as well as on this platform. Their newsletter gets regularly cited by leading publications such as the Wall Street Journal, Forbes, etc. Prior to publishing his research, Uttam worked in Silicon Valley, leading teams for some of the largest technology firms in the world, including Apple and Google.Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am
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quantum-computingOnline Short Course on “Free-Space Quantum Communication”
Online Short Course on “Free-Space Quantum Communication” Dates: Monday, July 27, 2026 to Friday, July 31, 2026Web page: https://www.prl.res.in/prl-eng/uncssteapRegistration deadline: Saturday, May 23, 2026Submission deadline: Saturday, May 23, 2026Applications are invited for a short course on “Free-Space Quantum Communication” (July 27-31, 2026) to be conducted Online by Physical Research Laboratory (PRL), Ahmedabad under the auspices of the Center for Space Science and Technology Education in Asia and the Pacific (CSSTEAP), affiliated to the United Nations. Application deadline June 30, 2026 Log in or register to post comments
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quantum-computingEPB and University of Tennessee at Chattanooga Launch $6.8 Million Quantum Workforce Initiative
EPB and University of Tennessee at Chattanooga Launch $6.8 Million Quantum Workforce Initiative The Board of Directors for EPB has approved a formal resolution establishing a $6.8 million USD joint funding partnership with the University of Tennessee at Chattanooga (UTC). The matching investment allocates $850,000 annually from each institution over a four-year operational term. The programmatic mandate expands regional academic infrastructure, funds applied research tracks, and builds commercialization pathways for emerging quantum hardware and software protocols. The initiative leverages Chattanooga’s existing municipal infrastructure, centering its operational workflows around the EPB Quantum Center. Technical Architecture & Specifications / Operational Implementation The technical framework builds directly upon the regional fiber-optic distribution grid, expanding academic access to the EPB Quantum Network. Launched commercially in 2023, the software-managed network provides programmable channels for quantum key distribution (QKD) and quantum networking experimentation, with UTC operating an active, on-campus network node. The newly expanded funding expands this physical testbed to integrate upcoming EPB Quantum Computing cloud-service resources slated for rollout later in 2026. The capital injection funds active research programs across four core technical disciplines: quantum algorithm design, quantum machine learning (QML) data models, multi-node quantum networking protocols, and nitrogen-vacancy or atom-based quantum sensing systems. Strategic Positioning & Ecosystem Integration The strategic investment aims to capture localized economic value from the commercialization of frontier technologies, aligning with long-term regional macro-projections. According to data from the McKinsey Quantum Technology Monitor 2026, the commercial scaling of quantum computing use cases is projected to generate up to $2.7 trillion in global economic value by 2035. On a
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quantum-computingWhy Rigetti Computing Stock Keeps Going Up
Yesterday, as you've probably heard, The Wall Street Journal reported on a Trump Administration plan to award $2 billion to nine quantum computing companies -- Rigetti Computing (RGTI +20.87%) among them -- and to take government equity stakes in the companies in return. Rigetti shares started moving one day before the announcement was made, then rocketed higher yesterday -- and higher again today. Up 18% through 10:55 a.m. Friday morning, Rigetti shares have gained an astounding 63% in just three days of trading, and investors are wondering: Is any price too high to pay for this quantum computing stock? Image source: Getty Images. And now it's official Shortly after WSJ broke the story, the U.S. Department of Commerce confirmed that not only does it plan to award grants, but it has in fact already signed letters of intent to do so. Operating under the CHIPS and Science Act, Commerce will "support and accelerate critical research and manufacturing of technologies for the quantum ecosystem to ensure continued United States leadership and national security." Two quantum foundries, Globalfoundries (GFS +7.54%) and International Business Machines (IBM +1.56%), will receive $375 million and $1 billion, respectively. Rigetti and five others will receive $100 million apiece, and the ninth company will receive $38 million. Each of the seven non-foundry recipients will focus on specific technologies needed to build quantum computers. Rigetti in particular will focus on miniaturization and cryostat devices for maintaining extremely low temperatures. ExpandNASDAQ: RGTIRigetti ComputingToday's Change(20.87%) $4.60Current Price$26.64Key Data PointsMarket Cap$7.3BDay's Range$22.67 - $26.7252wk Range$10.30 - $58.15Volume3.6MAvg Vol30.6MGross Margin-5945.49% What does this mean for Rigetti stock? The question now is how much good even this money can do for Rigetti, which is burning more than $80 million a year. Even if Rigetti gets all of the "up to $100 million" it's allotted, thi
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quantum-computingFlatiron Institute Tensor Network Algorithm Overturns Historical D-Wave Quantum Supremacy Claim
Flatiron Institute Tensor Network Algorithm Overturns Historical D-Wave Quantum Supremacy Claim Physicists at the Center for Computational Quantum Physics (CCQ) at the Simons Foundation’s Flatiron Institute, in collaboration with Boston University, have developed a classical algorithm that successfully simulates complex three-dimensional quantum dynamics previously claimed to be impossible without a quantum computer. Published in Science, the study refutes a high-profile “beyond-classical” computation milestone reported in March 2025 by researchers utilizing D-Wave Systems’ 5,000-qubit Advantage2 superconducting quantum annealing processor. By repurposing and optimizing decades-old data compression and mathematical routing techniques, the CCQ team proved that classical workstations—and in some configurations, standard commercial laptops—can achieve state-of-the-art accuracy when calculating highly entangled quantum state progressions. Technical Architecture & Specifications / Operational Implementation The computational breakthrough targets the simulation of continuous-time quantum dynamics within the transverse-field Ising model (TFIM) across multi-dimensional square, cubic, and diamond disordered spin-glass lattices. The 2025 D-Wave demonstration relied on the premise that as hundreds of interacting qubits undergo a rapid quench through a quantum phase transition, the system’s wave function generates area-law entanglement that causes classical matrix-product-state approaches to scale exponentially in memory and runtime. To bypass this exponential memory wall without directly storing the massive wave function, the CCQ team constructed a lattice-specific, three-dimensional tensor network architecture utilizing ITensor, an in-house high-performance software library. The mathematical implementation processes the state evolution via a two-stage pipeline: Time Evolution Tracking: The algorithm adapts belief propagation (BP)—a localized message-passing routine origin
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quantum-computingQROM Copying Mechanism Halves Quantum Data Loading Costs
Xanadu Quantum Technologies has achieved a reduction in the operational costs of quantum computing through a breakthrough in Quantum Read-Only Memory (QROM) technology. The company’s new implementation approximately halves the number of expensive Toffoli gates required within QROM modules, a critical advancement for problem sizes constrained by qubit availability. This addresses a longstanding bottleneck in loading classical data onto quantum computers; QROM performance had remained stagnant for seven years prior to this innovation. Xanadu achieves these optimizations by replacing traditional qubit “swapping” with a “copying” mechanism, and streamlining data unloading processes. “Our team focuses on making quantum computing practical for real-world use,” said Dr. Christian Weedbrook, Xanadu Founder and Chief Executive Officer. “By halving QROM costs, we are using quantum algorithm developments to reduce the cost of quantum computation for many applications.” QROM Optimization Halves Toffoli Gate Count Seven years of stagnant Quantum Read-Only Memory (QROM) performance have been overcome by Xanadu Quantum Technologies with a new algorithmic breakthrough that is expected to significantly reduce the operational cost of quantum applications. Efficiently loading classical data onto quantum computers has long presented a challenge, limiting the potential of near-term, utility-scale fault-tolerant systems. Xanadu’s implementation is expected to approximately halve the number of expensive quantum operations required for QROM, a reduction that promises to unlock more complex computations on existing hardware. The core of this optimization lies in a novel approach to reducing Toffoli gates, among the most computationally intensive operations a quantum computer performs, within QROM modules. The team also streamlined the process of unloading data from QROM, consolidating multiple redundant steps into a single, efficient operation. This combined approach allows quantum programs
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quantum-computingQuantum Algorithms Now Solve Complex Industrial Problems with Fewer Qubits
Scientists at West Virginia University and Cornell University have introduced a novel quantum reinforcement learning framework to address the significant computational challenges inherent in process synthesis, a crucial aspect of chemical engineering. Austin Braniff and colleagues, spanning the Department of Chemical and Biomedical Engineering at West Virginia University and the R.F. Smith School of Chemical and Biomolecular Engineering at Cornell University, have engineered a system that demonstrably improves scalability and overcomes previous limitations related to qubit requirements in the design of complex chemical processes. This framework not only provides a robust methodology for tackling these intricate problems but also establishes a valuable benchmark for rigorously comparing the performance of classical and quantum algorithms. This paves the way for future quantum applications within the broader field of process systems engineering Quantum algorithms enhance process synthesis optimisation efficiency and scalability Quantum reinforcement learning algorithms achieved a 1.2x improvement in efficiency on a per-parameter basis when compared to established classical reinforcement learning benchmarks for moderate-scale process synthesis problems. This enhancement stems from a critical decoupling of qubit requirements from the size of the problem being addressed. Traditionally, the computational burden of process synthesis escalates rapidly with increasing complexity, often rendering large-scale designs intractable. By reducing the dependence on qubit numbers, the fundamental units of quantum information, this new framework unlocks the potential to tackle more complex flowsheet designs than previously possible. The core innovation lies in the development of novel state encoding algorithms which efficiently represent the process design space within the quantum system, minimising the number of qubits needed for simulation. This circumvents the limitations imposed b
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quantum-computing$38M CHIPS Act Award to Scale Diraq Silicon Quantum Processors
Diraq has secured a Letter of Intent for 38 million in proposed funding from the U.S. Department of Commerce through the CHIPS Research and Development Office, a commitment designed to scale domestic production of silicon quantum computing processors. The company’s approach, leveraging silicon spin technology, is potentially a more economical and scalable path toward utility-scale quantum computing than other qubit technologies currently in development. “The Department of Commerce’s incentives strengthen U.S. quantum leadership and technological resilience,” said Bill Frauenhofer, Executive Director of Semiconductor Investment and Innovation. This investment builds upon 25 years of U.S. government support, through the U.S. Army Research Office and DARPA, that underpins Diraq’s technology, potentially establishing a fully American quantum supply chain for cryostats, chips, and packaging. 38M CHIPS Act Funding to Scale Silicon Quantum Processors A proposed 38 million investment from the U.S. Department of Commerce signals a move toward silicon-based quantum computing, specifically targeting the scaling of processors developed by Diraq. Diraq’s approach centers on utilizing existing CMOS manufacturing processes, a strategy designed to circumvent the substantial infrastructure hurdles facing other quantum modalities and potentially deliver millions of qubits on a single chip. This financial commitment is not a sudden endorsement, but rather a continuation of decades-long government support; Andrew Dzurak, Diraq Founder and CEO, explained that “The U.S. Government has played an important role for over 25 years in funding silicon quantum research through entities such as the U.S. Army Research Office and more recently DARPA.” Diraq is targeting a price point of less than 1 per physical qubit, a critical threshold for utility-scale quantum computing, and designing systems compatible with standard data center infrastructure. GlobalFoundries, a key partner, is leveraging its
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quantum-computingBalancing Quasi-Bragg Regime and Velocity Selectivity in Quantum-Enhanced Atom Interferometry
--> Quantum Physics arXiv:2605.21643 (quant-ph) [Submitted on 20 May 2026] Title:Balancing Quasi-Bragg Regime and Velocity Selectivity in Quantum-Enhanced Atom Interferometry Authors:Christian Miguel Karres (1 and 2), Daniel Derr (1), Enno Giese (1) ((1) Technical University of Darmstadt, (2) Johannes Gutenberg University Mainz) View a PDF of the paper titled Balancing Quasi-Bragg Regime and Velocity Selectivity in Quantum-Enhanced Atom Interferometry, by Christian Miguel Karres (1 and 2) and 2 other authors View PDF HTML (experimental) Abstract:Spin squeezing in atomic ensembles enables atom interferometry with sensitivities below the shot-noise limit, but the associated entanglement is highly susceptible to loss, making imperfections in atom optics a central limitation. Bragg diffraction is an established technique for driving transitions between atomic momentum states and enables large-momentum transfer through higher-order diffraction while preserving the internal state. However, it is intrinsically limited by two competing mechanisms: short light pulses induce parasitic diffraction into off-resonant orders beyond an effective two-level description, while long pulses face velocity selectivity. We derive analytical expressions in a second-quantized framework for the atom optics and phase uncertainty of a Mach-Zehnder interferometer including these effects. We demonstrate that sub-shot-noise scaling is achieved only in a regime of intermediate pulse duration. Furthermore, we show that deleterious effects of higher-order diffraction are partially mitigated by optimizing the input quantum state. Comments: Subjects: Quantum Physics (quant-ph); Atomic Physics (physics.atom-ph) Cite as: arXiv:2605.21643 [quant-ph] (or arXiv:2605.21643v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.21643 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Christian Karres [view email] [v1] Wed, 20 May 2026 1
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quantum-computingATHENA: A Compiler For Optimized Scheduling In Distributed Quantum Computers
--> Quantum Physics arXiv:2605.21795 (quant-ph) [Submitted on 20 May 2026] Title:ATHENA: A Compiler For Optimized Scheduling In Distributed Quantum Computers Authors:Won Joon Yun (1), Dhilan Nag (1), Sneha Ballabh (1), Jiapeng Zhao (2), Eneet Kaur (2), Poulami Das (1) ((1) The University of Texas at Austin (2) Cisco Quantum Lab) View a PDF of the paper titled ATHENA: A Compiler For Optimized Scheduling In Distributed Quantum Computers, by Won Joon Yun (1) and 5 other authors View PDF HTML (experimental) Abstract:Distributed Quantum Computers (DQCs) enable large system sizes by connecting smaller chips via photonic interconnects. DQCs use teleportation to relocate qubits and execute CNOTs between qubits on different chips. However, non-local CNOTs are 4.3-7.7$\times$ slower and 4$\times$ more error-prone than local CNOTs within a chip, which degrades program fidelities. Existing compilers group CNOTs with overlapping qubits into blocks and collectively optimize teleportations for each block. However, block-level scheduling has two key drawbacks. First, it lacks lookahead ability across blocks because it selects the optimal schedule for one block before proceeding to the next. As a result, it cannot assess the impact of a teleportation on future blocks. Our studies show that naively expanding the lookahead window to include subsequent blocks does not address this issue. Second, existing approaches do not schedule future block operations or the teleportations they require until preceding blocks are fully scheduled, introducing delay and latency overheads. We propose ATHENA, a DQC compiler that addresses these limitations using two key insights: Utility-driven Lookahead with Multi-Candidate Block Scheduling (UMS) and EPR-Capacity-Aware Early Scheduling (EES). UMS schedules a block by considering only useful future blocks in its lookahead window. A future block has utility if it shares overlapping qubits with the current block being scheduled. UMS also maintains multiple
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