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Quantum Materials & Devices: Hardware Components & Fabrication

Quantum materials news: quantum device fabrication, superconductors, quantum dots, 2D materials. Quantum hardware components & substrates.

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Quantum materials and devices form the foundational hardware layer enabling all quantum technologies, requiring specialized materials with precise quantum properties including superconductors, topological insulators, 2D materials like graphene, and semiconductor heterostructures for qubit fabrication.

India's Quantum Materials and Devices Initiatives

India's National Quantum Mission includes Quantum Materials & Devices as the fourth thematic vertical with dedicated funding. The QMD Tech Foundation at IIT Delhi serves as the Thematic Hub on Quantum Materials and Devices, established under the T-Hub framework of NQM. The hub focuses on developing indigenous materials for quantum technologies including substrates for superconducting circuits, quantum dots for spin qubits, and specialized semiconductors.

The ₹720 crore investment for quantum fabrication facilities announced in November 2025 supports this vertical, with facilities at: IISc Bengaluru: Quantum computing fabrication for superconducting, photonic, and spin qubits (3-5 qubits per chip initially, scaling to 20-100 qubits); IIT Bombay: Quantum sensing and device fabrication; IIT Delhi: Quantum materials and packaging; IIT Kanpur: Smaller facility for specialized devices.

The Indian Institute of Technology Madras Centre for Quantum Information, Communication and Computing (CQuICC) houses India's first remotely accessible semiconductor qubit facility, capable of fabricating 3-5 qubit chips per run with 95% device yield.

Research Areas: Superconducting materials: Niobium and aluminum thin films for Josephson junctions; Semiconductor quantum dots: Silicon and III-V materials for spin qubits; 2D materials: Graphene, transition metal dichalcogenides for novel qubit designs; Topological materials: Research into materials exhibiting Majorana zero modes; Photonic materials: Silicon photonics, nonlinear optical crystals for quantum light sources.

The Defence Research and Development Organisation (DRDO) develops quantum materials for defense applications including secure communications and sensing. The Department of Atomic Energy (RRCAT, Indore) provides specialized laser and materials processing capabilities for quantum device fabrication. The NQM targets developing superconductors, novel semiconductor structures, and quantum materials for memory and device fabrication as key deliverables within the 8-year mission timeline.

Japan's Full-Stack Quantum Computer That Works At Room Temperature Has Just Gone Live, Powered By 50 Qubits - iflscience.comquantum-computing

Japan's Full-Stack Quantum Computer That Works At Room Temperature Has Just Gone Live, Powered By 50 Qubits - iflscience.com

Quantum computers have the potential to be world-changing, but they haven't quite fulfilled that bold promise just yet. In the latest slow but steady step forward, Japan has unveiled its first full-stack neutral-atom quantum computer, which can work its "magic" at room temperature.The machine was designed by the Institute for Molecular Science (IMS) at Japan's National Institutes of Natural Sciences in collaboration with Hitachi, using a quantum processing unit (QPU) from the US-based tech company Infleqtion. Welcome to the world, ShunkaiIt's called “Shunkai,” named in honor of Shibukawa Shunkai, AKA Shibukawa Harumi, an astronomer from the Edo Period (1603-1867) who was a dab hand at the wonderfully complex board game Go.The Japanese quantum computer is an example of neutral-atom quantum computing, meaning each of its "qubits" is a single atom held in place by laser light. Quantum calculations are performed by blasting the atom with microwaves or laser light, then observing subtle changes to the light and other electromagnetic radiation it emits.Unlike a classical computer bit, which must be strictly 0 or 1, a qubit can exist in multiple states simultaneously until it is directly measured. Together with other quirks of quantum mechanics, like entanglement, this gives it a huge edge in solving certain problems at far faster speeds than classical computers.In practice, quantum computers still can't beat high-end supercomputers at most tasks because of the many difficulties in scaling the technology and the trickiness of working with quantum mechanics.With the help of Shunkai, though, the researchers hope to flatten some of those hurdles. "Neutral atom-based quantum computers have recently been rapidly attracting attention around the world as a new modality that could exceed the limits of the superconducting modality, which started its development earlier,” Kenji Ohmori, a Professor at the Institute for Molecular Science who is leading the project, said in a

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Researchers Find Fermionic Quantum Error Correction Needs Extra Steps - Quantum Zeitgeist
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Researchers Find Fermionic Quantum Error Correction Needs Extra Steps - Quantum Zeitgeist

Fermionic platforms offer compelling architectures for quantum computing, ranging from topologically protected Majorana-based qubits to fermionic cold atoms. To achieve scalability, they require quantum error correction. The research proves that any exact and sufficiently accurate approximate fermionic quantum error correction necessarily requires non-Gaussian operations, beyond the free-fermion regime of quadratic dynamics. This is in sharp contrast to the qubit setting, where efficiently classically simulable stabilizer operations form the standard framework for quantum error correction. Specifically, the study demonstrates that the logical space of any non-trivial fermionic error-correcting code contains no pure states. Non-Gaussian Operations Essential For Strong Fermionic Error Correction Scientists at Freie Universität Berlin, collaborating with Quantum Research Centre Tsinghua University and Technology Innovation Institute, have identified a key limitation for scalable quantum computation utilising fermions. They proved that sufficiently accurate fermionic error correction requires non-Gaussian operations when Majorana distance reaches dF ≥3, a threshold previously impossible to cross. Existing codes relied on simpler free-fermion dynamics but lacked the capacity for strong logical qubit protection against accumulating errors during complex calculations. This incompatibility is rooted in Wick’s theorem which governs particle correlations, establishing that the logical space within any effective fermionic code cannot contain pure states describable by Gaussian statistics. The team quantified this limitation showing the number of necessary ‘non-Gaussian gates’ grows linearly alongside both error-protection strength and logically stored information within the system. Further analysis revealed distinctions between how fermions and bosons handle entanglement distillation, a process vital for extending communication range in quantum networks; Gaussian fermionic ope

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5 Millionaire-Maker Quantum Computing Stocks to Buy Now - The Globe and Mail
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5 Millionaire-Maker Quantum Computing Stocks to Buy Now - The Globe and Mail

Key PointsIonQ and Quantinuum are leading the charge on accuracy using the trapped-ion technique.D-Wave and IBM are two top companies using the faster superconducting qubit technology.Infleqtion's neutral-atom approach could become the best of both worlds.10 stocks we like better than IonQ ›Quantum computing has the potential to be the next big technological breakthrough after artificial intelligence (AI). Companies in the field are pursuing the technology in various ways, so a basket approach (a collection of small positions across several stocks) may be the best investment option.While it's unlikely that all of them pan out, if one or two do, they could help fuel a millionaire-making portfolio. Let's look at the stocks I'd put in this quantum computing basket.Missed AI’s "Act 1"? Act 2 Could Be 15x Bigger. Most investors think they missed the AI boat because they didn't buy Nvidia in 2005. But according to our analysts, we’re only at the end of "Act 1"—the R&D phase. "Act 2" is the global rollout. Continue »Image source: Getty ImagesIonQThe first stock I'd add to a quantum basket is IonQ(NYSE: IONQ). The company uses the trapped-ion method with the added twist of embedding microwave antennas directly into its chips. This also resulted in the company achieving the best accuracy in the space, with 99.99% two-qubit gate fidelity. It also recently demonstrated what it called "the industry's first end-to-end real-time quantum error correction decoder," a significant milestone as it pushes to create a fault-tolerant quantum system.In addition to its accuracy lead, the company has made a variety of acquisitions across different areas of the quantum ecosystem. It even acquired a quantum foundry that will help it advance prototypes more quickly and scale more easily.QuantinuumAnother top quantum stock in terms of accuracy is Quantinuum(NASDAQ: QNT). It also uses the trapped-ion approach and has recorded 99.92% 2-qubit gate fidelity. With its new Sol system, meanwhile,

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5 Millionaire-Maker Quantum Computing Stocks to Buy Now - Yahoo Finance
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5 Millionaire-Maker Quantum Computing Stocks to Buy Now - Yahoo Finance

5 Millionaire-Maker Quantum Computing Stocks to Buy Now Geoffrey Seiler, The Motley Fool Mon, September 28, 2026 at 7:50 AM EDT 4 min read IONQ +1.11% NVDA +0.22% Quantum computing has the potential to be the next big technological breakthrough after artificial intelligence (AI). Companies in the field are pursuing the technology in various ways, so a basket approach (a collection of small positions across several stocks) may be the best investment option. While it's unlikely that all of them pan out, if one or two do, they could help fuel a millionaire-making portfolio. Let's look at the stocks I'd put in this quantum computing basket. Missed AI's "Act 1"? Act 2 Could Be 15x Bigger. Most investors think they missed the AI boat because they didn't buy Nvidia in 2005. But according to our analysts, we're only at the end of "Act 1"—the R&D phase. "Act 2" is the global rollout. Continue » Image source: Getty Images IonQ The first stock I'd add to a quantum basket is IonQ (NYSE: IONQ). The company uses the trapped-ion method with the added twist of embedding microwave antennas directly into its chips. This also resulted in the company achieving the best accuracy in the space, with 99.99% two-qubit gate fidelity. It also recently demonstrated what it called "the industry's first end-to-end real-time quantum error correction decoder," a significant milestone as it pushes to create a fault-tolerant quantum system. In addition to its accuracy lead, the company has made a variety of acquisitions across different areas of the quantum ecosystem. It even acquired a quantum foundry that will help it advance prototypes more quickly and scale more easily. Quantinuum Another top quantum stock in terms of accuracy is Quantinuum (NASDAQ: QNT). It also uses the trapped-ion approach and has recorded 99.92% 2-qubit gate fidelity. With its new Sol system, meanwhile, it expects to hit 99.999% logical fidelity in 2027.

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5 Millionaire-Maker Quantum Computing Stocks to Buy Now - The Motley Fool
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5 Millionaire-Maker Quantum Computing Stocks to Buy Now - The Motley Fool

Quantum computing has the potential to be the next big technological breakthrough after artificial intelligence (AI). Companies in the field are pursuing the technology in various ways, so a basket approach (a collection of small positions across several stocks) may be the best investment option. While it's unlikely that all of them pan out, if one or two do, they could help fuel a millionaire-making portfolio. Let's look at the stocks I'd put in this quantum computing basket. Image source: Getty Images IonQ The first stock I'd add to a quantum basket is IonQ (IONQ +1.11%). The company uses the trapped-ion method with the added twist of embedding microwave antennas directly into its chips. This also resulted in the company achieving the best accuracy in the space, with 99.99% two-qubit gate fidelity. It also recently demonstrated what it called "the industry's first end-to-end real-time quantum error correction decoder," a significant milestone as it pushes to create a fault-tolerant quantum system. ExpandNYSE: IONQIonQPremium FeatureMoneyball Superscore62/100Today's Change(1.11%) $0.50Current Price$45.48Key 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 shares. Implied market cap may vary.Day's Range$44.12 - $46.5552wk Range$25.89 - $84.64Volume1.2MAvg Vol21.3MGross Margin-3317.96% In addition to its accuracy lead, the company has made a variety of acquisitions across different areas of the quantum ecosystem. It even acquired a quantum foundry that will help it advance prototypes more quickly and scale more easily. Quantinuum Another top quantum stock in terms of accuracy is Quantinuum (QNT +0.28%). It also uses the trapped-ion approach and has recorded 99.92% 2-qubit gate fidelity. With its new Sol system, meanwhile, it expects to hit 99.999% logical fidelity in 2027. Quantinuum is also known to have one of the best quantu

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5 Millionaire-Maker Quantum Computing Stocks to Buy Now
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5 Millionaire-Maker Quantum Computing Stocks to Buy Now

Quantum computing has the potential to be the next big technological breakthrough after artificial intelligence (AI). Companies in the field are pursuing the technology in various ways, so a basket approach (a collection of small positions across several stocks) may be the best investment option. While it's unlikely that all of them pan out, if one or two do, they could help fuel a millionaire-making portfolio. Let's look at the stocks I'd put in this quantum computing basket. Image source: Getty Images IonQ The first stock I'd add to a quantum basket is IonQ (IONQ +1.11%). The company uses the trapped-ion method with the added twist of embedding microwave antennas directly into its chips. This also resulted in the company achieving the best accuracy in the space, with 99.99% two-qubit gate fidelity. It also recently demonstrated what it called "the industry's first end-to-end real-time quantum error correction decoder," a significant milestone as it pushes to create a fault-tolerant quantum system. ExpandNYSE: IONQIonQPremium FeatureMoneyball Superscore62/100Today's Change(1.11%) $0.50Current Price$45.48Key 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 shares. Implied market cap may vary.Day's Range$44.12 - $46.5552wk Range$25.89 - $84.64Volume1.1MAvg Vol21.3MGross Margin-3317.96% In addition to its accuracy lead, the company has made a variety of acquisitions across different areas of the quantum ecosystem. It even acquired a quantum foundry that will help it advance prototypes more quickly and scale more easily. Quantinuum Another top quantum stock in terms of accuracy is Quantinuum (QNT +0.28%). It also uses the trapped-ion approach and has recorded 99.92% 2-qubit gate fidelity. With its new Sol system, meanwhile, it expects to hit 99.999% logical fidelity in 2027. Quantinuum is also known to have one of the best quantu

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Quandela Seeks South Korean Partners for Photonic Quantum Computingquantum-computing

Quandela Seeks South Korean Partners for Photonic Quantum Computing

Insider Brief French photonic quantum computing company Quandela intends to spend the next two years lining up strategic partners in South Korea, with joint work expected to cover quantum chip manufacturing, system integration, demonstration, and real-world operation, Seoul Economic Daily reported. CEO Niccolo Somaschi presented the company’s Korea strategy and a medium- to long-term roadmap […]

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Several postdoc positions @PhIQuS, Inria Saclay, Paris in quantum information theoryquantum-computing

Several postdoc positions @PhIQuS, Inria Saclay, Paris in quantum information theory

Several postdoc positions @PhIQuS, Inria Saclay, Paris in quantum information theory Application deadline: Monday, November 30, 2026Research group: PhiQus - Inria Saclay, Institut Polytechnique de ParisEmployer web page: Inria SaclayJob type: PostDocTags: quantum informationentanglementquantum correlationsquantum foundationsNoncommutative polynomial optimisationquantum networksnonlocalityI am recruiting several PostDocs in my group at Inria Saclay, Ecole Polytechnique near Paris on the following topics: - Quantum Distributed Computing - Quantum correlations, quantum nonlocality, entanglement - Fermionic quantum information - Quantum Foundations - Mathematical Physics (Noncommutative polynomial optimisation, C* Algebras) - More generally, all areas of Quantum Information Theory Precise projects will be tailored to your expertise. To explore the possibility of joining the group, email me (marc-olivier.renou@inria.fr). These positions are funded by my ERC Starting Grant QINF (fundamental laws ruling Quantum INFormation: bits, qubits and fermionic bits (febits) in networks - see https://marcolivierrenou.com/erc-qinf/), which studies how fermions can carry and process information in ways standard qubits cannot, and by the QuantERA project Quantum Network Algorithms (QNA, 2026–2029 - see https://research.cs.aalto.fi/da/qna/), which aims to identify the first distributed tasks with a practical quantum advantage. As part of a new INRIA Team located in Saclay, you will have the possibility to collaborate with other quantum information researchers in Paris area and abroad: - Distributed Computing: Jukka Suomela (Aalto, Finland), François Le Gall (Nagoya), and the other members of the QNA consortium, ... - Quantum Physics: Nicolas Gisin (Geneva), Omar Fawzi (Inria Lyon), Antonio Acín (ICFO, Barcelona), David Gross (Cologne), ... - Polynomial optimisation: Victor Magron (LAAS Toulouse), Igor Klep (Ljubljana), ... - Inria teams Quriosity, Quacs, Cosmiq,

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Restricting Trainable Lie-Algebra Growth in Equivariant Quantum Networks via Hierarchical Ancilla-Controlled Subspace Projectionsquantum-computing

Restricting Trainable Lie-Algebra Growth in Equivariant Quantum Networks via Hierarchical Ancilla-Controlled Subspace Projections

--> Quantum Physics arXiv:2609.30283 (quant-ph) [Submitted on 3 Sep 2026] Title:Restricting Trainable Lie-Algebra Growth in Equivariant Quantum Networks via Hierarchical Ancilla-Controlled Subspace Projections Authors:Ting Li, Zhiming Xiao, Qibiao Tang View a PDF of the paper titled Restricting Trainable Lie-Algebra Growth in Equivariant Quantum Networks via Hierarchical Ancilla-Controlled Subspace Projections, by Ting Li and 2 other authors View PDF HTML (experimental) Abstract:Equivariant quantum networks encode symmetry as an inductive bias, which can improve generalization and may also favor optimization convergence. Equivariance alone, however, does not constrain the noncommuting closure of trainable generators, and this closure can still grow rapidly in symmetry-preserving variational circuits. We introduce a hierarchical ancilla-controlled architecture that addresses this Lie-algebra-growth mechanism. Commuting invariant-sector projectors on the data register select parameterized operations on a shared ancilla register, where the noncommuting trainable dynamics is confined. The trainable circuit decomposes into compatible joint sectors, giving a sector-probability-weighted ancilla response and an explicit view of parameter sharing across hierarchical paths. For an ancilla dimension $d_A=2^m$ and $K_\ell$ retained layer-wise control modes, we prove the group-independent bound $\dim(\mathfrak g)\le (d_A^2-1)\prod_{\ell=1}^{L}(K_\ell+1)$. The bound is polynomial in the number of data qubits when $m$ and $K_\ell$ remain constant along a logarithmic-depth hierarchy. Particle-number and parity projectors illustrate the general construction, while a fixed Clebsch--Gordan coupling tree supplies a concrete $SU(2)$ realization with rotation-invariant scalar outputs. Finite-size state-vector simulations exhibit slower gradient-variance decay and larger initialization gradients than generic and conventional rotationally equivariant circuits over the studied system sizes.

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A Mechanism for the R\'enyi Hierarchy of Decoherence-Induced Phase Transitionsquantum-computing

A Mechanism for the R\'enyi Hierarchy of Decoherence-Induced Phase Transitions

--> Quantum Physics arXiv:2609.30377 (quant-ph) [Submitted on 24 Sep 2026] Title:A Mechanism for the Rényi Hierarchy of Decoherence-Induced Phase Transitions Authors:Zhou Yang, Yuri D. Lensky, Chao-Ming Jian View a PDF of the paper titled A Mechanism for the R\'enyi Hierarchy of Decoherence-Induced Phase Transitions, by Zhou Yang and 2 other authors View PDF HTML (experimental) Abstract:Decoherence-induced phase transitions (DIPTs) are associated with singular changes in a quantum system's entanglement structure as the decoherence strength varies. In many cases, their critical strengths depend monotonically on the Rényi index of the entanglement diagnostic, forming a Rényi hierarchy. We identify a general mechanism for this hierarchy based on correlation inequalities in replicated statistical models that describe these DIPTs. We demonstrate it in $\mathbb{Z}_2$ topological stabilizer codes subject to independent phase-flip decoherence on each qubit and in a decohered rotor model with strong $\text{U}(1)$ symmetry. When the relevant correlations diagnose the DIPTs, these inequalities imply that the critical decoherence strength is nondecreasing with integer Rényi index $R\geq2$. We show that this mechanism also applies to a class of models with correlated decoherence. Comments: Subjects: Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Strongly Correlated Electrons (cond-mat.str-el) Cite as: arXiv:2609.30377 [quant-ph]   (or arXiv:2609.30377v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2609.30377 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhou Yang [view email] [v1] Thu, 24 Sep 2026 18:00:04 UTC (31 KB) Full-text links: Access Paper: View a PDF of the paper titled A Mechanism for the R\'enyi Hierarchy of Decoherence-Induced Phase Transitions, by Zhou Yang and 2 other authorsView PDFHTML (experimental)TeX

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Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph propertiesquantum-computing

Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties

--> Quantum Physics arXiv:2609.30399 (quant-ph) [Submitted on 24 Sep 2026] Title:Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties Authors:N. A. Susulovska, Kh. P. Gnatenko View a PDF of the paper titled Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties, by N. A. Susulovska and 1 other authors View PDF HTML (experimental) Abstract:The encoding of 3-uniform edge-ordered weighted hypergraphs into multiqubit quantum states is proposed. We derive an analytical expression for the geometric measure of entanglement of hypergraph quantum states and establish its relation to hypergraph properties. The results establish a direct connection between the local structure of hypergraphs, hyperedge weights, and the entanglement of the corresponding quantum states. For equally weighted hypergraphs, it is shown that the entanglement depends explicitly on the vertex degree. The dependence of entanglement on hypergraph parameters is studied analytically and using quantum computing. Hypergraph states associated with chain, star, regular lattice, and binary-tree hypergraphs are examined, and their entanglement is quantified using quantum computing. These results open up the possibility of studying hypergraph properties with quantum programming. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.30399 [quant-ph]   (or arXiv:2609.30399v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2609.30399 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nataliia Susulovska [view email] [v1] Thu, 24 Sep 2026 18:04:35 UTC (7,410 KB) Full-text links: Access Paper: View a PDF of the paper titled Three-uniform edge-ordered hypergraph quantum states: entanglement and its relation to hypergraph properties, by N. A. Susulovska and 1 other authorsView PDFHTML (experimental)TeX Source view license Current

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Generation of Photonic Graph States with minimal number of quantum emittersquantum-computing

Generation of Photonic Graph States with minimal number of quantum emitters

--> Quantum Physics arXiv:2609.30400 (quant-ph) [Submitted on 24 Sep 2026] Title:Generation of Photonic Graph States with minimal number of quantum emitters Authors:Konstantinos-Rafail Revis, Nils Tomke Ottink, Pierre-Emmanuel Emeriau, Paul Hilaire View a PDF of the paper titled Generation of Photonic Graph States with minimal number of quantum emitters, by Konstantinos-Rafail Revis and 3 other authors View PDF HTML (experimental) Abstract:Graph states are a fundamental resource for measurement and fusion-based quantum computing, quantum networks, and sensing. Preparing them in a photonic system deterministically is, in principle, possible, but finding efficient schemes to prepare them was a long-standing problem addressed recently. Additionally, heuristic optimization schemes for reducing the required number of two-qubit gates were developed. However, the problem of reducing the number of emitters by optimizing the emission ordering was not addressed, due to its computational complexity, as it is connected to a well-known NP-hard problem from graph theory, the linear rank width computation. In this work, we focus on developing heuristic polynomial algorithms to reduce the number of emitters required. In total, we propose four distinct algorithms, which demonstrate up to $30\%$ emitter reduction on random graphs. Furthermore, we provide numerical and statistical evidence that the combination of our optimization schemes with the preexisting algorithms for optimizing the two-qubit gates of the preparation protocol can further reduce them by around $20\%$. Finally, we examine the developed algorithms for various useful graph state families, such as graphs useful for measurement-based quantum algorithms, and cluster states and graph codes used for quantum error correction, to determine the performance of each algorithm. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.30400 [quant-ph]   (or arXiv:2609.30400v1 [quant-ph] for this version)   ht

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Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Modelquantum-computing

Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Model

--> Quantum Physics arXiv:2609.30496 (quant-ph) [Submitted on 24 Sep 2026] Title:Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Model Authors:Karthikeya Machiraju, Krishna Sujith, Kaustav Bhowmick View a PDF of the paper titled Disentangling Expressibility, Symmetry Protection, and Hardware Noise in Variational Quantum Simulation of the Two-Flavor Schwinger Model, by Karthikeya Machiraju and 2 other authors View PDF HTML (experimental) Abstract:Existing quantum simulations of the two-flavor Schwinger model have run at a single lattice size, and it is not known how far the variational approach can be pushed or which weakness stops it first. Following the model from N = 2 to 6 staggered lattice sites, we find that the binding constraint at reachable sizes is hardware noise rather than circuit expressibility or trainability, and identify N = 3 as the immediately viable extension of existing trapped-ion experiments. The energy error of a charge-conserving ansatz collapses onto one function of p/d, the ratio of variational parameters to physical-sector dimension, and falls by more than two orders of magnitude as p/d rises through order unity, giving the expressibility condition L(4N - 1) >= binom(2N,N) for L circuit layers. The condition is local in chemical potential: at N = 3 the layer count sufficient at zero chemical potential leaves a 74.38% error near the first-order boundary, while one further layer reaches 0.08%. Charge conservation also protects trainability and prevents charge-sector leakage: as the qubit count doubles from 4 to 8, the normalized gradient variance falls to 1/3.56 of its starting value for the constrained ansatz, versus 1/13.57 for an unconstrained circuit. Comparing a global contraction with per-gate local noise, a fixed-parameter control shows that the noise model, not whether the optimizer runs inside the noisy loop, sets how strongly noise degrades the fi

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A Novel $q$-Derivative Framework with Applications to $q$-Deformed Thermodynamics and Leakage Suppression in Superconducting Qubitsquantum-computing

A Novel $q$-Derivative Framework with Applications to $q$-Deformed Thermodynamics and Leakage Suppression in Superconducting Qubits

--> Quantum Physics arXiv:2609.30632 (quant-ph) [Submitted on 24 Sep 2026] Title:A Novel $q$-Derivative Framework with Applications to $q$-Deformed Thermodynamics and Leakage Suppression in Superconducting Qubits Authors:André A. A. Marinho, Gisele B. Freitas, Clovis A. C. Filho View a PDF of the paper titled A Novel $q$-Derivative Framework with Applications to $q$-Deformed Thermodynamics and Leakage Suppression in Superconducting Qubits, by Andr\'e A. A. Marinho and 1 other authors View PDF HTML (experimental) Abstract:We propose a new $q$-derivative operator built directly from Jackson's $q$-number formulation, designed to preserve the structural properties of standard differential calculus while incorporating deformation effects. By analyzing $q$-deformed Heisenberg algebras, we demonstrate that this formulation maintains the consistency of thermodynamic quantities-such as internal energy, particle number, and specific heat-in dilute gas limits without requiring ad-hoc chain-rule modifications. Furthermore, we explore the physical implications of algebraic deformation using the Biedenharn-Macfarlane realization, showing how $q$-deformation induces intrinsic anharmonicity in quantum oscillator spectra and affects multi-level quantum systems ($d \ge 3$). Applying this algebraic scheme to superconducting transmon qubits, we derive analytical pulse-shaping corrections that generalize the Derivative Removal by Adiabatic Gate (DRAG) technique, offering a robust method to suppress computational leakage in ultra-fast quantum logic operations. Comments: Subjects: Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech) Cite as: arXiv:2609.30632 [quant-ph]   (or arXiv:2609.30632v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2609.30632 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: André Marinho [view email] [v1] Thu, 24 Sep 2026 23:36:57 UTC (2,730 KB) Full-text links: Access Pape

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Quantum-circuit simulation of three-flavor neutrino oscillations: vacuum, matter, and CP diagnosticsquantum-computing

Quantum-circuit simulation of three-flavor neutrino oscillations: vacuum, matter, and CP diagnostics

--> Quantum Physics arXiv:2609.30875 (quant-ph) [Submitted on 25 Sep 2026] Title:Quantum-circuit simulation of three-flavor neutrino oscillations: vacuum, matter, and CP diagnostics Authors:Daming Li View a PDF of the paper titled Quantum-circuit simulation of three-flavor neutrino oscillations: vacuum, matter, and CP diagnostics, by Daming Li View PDF HTML (experimental) Abstract:We simulate three-flavor neutrino oscillations on two-qubit quantum circuits. The flavor space is embedded as $\ket{00}=\nu_e$, $\ket{01}=\nu_\mu$, $\ket{10}=\nu_\tau$ with the $\ket{11}$ channel kept inert, and the input mixing parameters (NuFIT~5.3, normal ordering) follow the global fit. We verify three physical settings against analytic or exact references: vacuum propagation, reproduced by a mass-eigenstate phase circuit to $\sim 10^{-16}$; constant-density Mikheev--Smirnov--Wolfenstein (MSW) matter effects, showing resonant flavor conversion when the matter potential $A$ crosses $\Delta m^2_{31}$; and varying-density (supernova shock-shell) propagation by slicing plus second-order Suzuki--Trotter splitting, agreeing with exact evolution to $\sim 10^{-6}$. We report a Suzuki--Trotter precision--resource calibration varying order, step number, and matrix/probability error. It delimits brute-force Trotterization on solar baselines: we quantify both standard alternatives, coherence averaging and adiabatic MSW. We also compute quantum-information diagnostics from the same model parameters, namely mode entanglement and the CP asymmetry. Finally, we extend the framework to open quantum systems via a Lindblad master equation with dephasing and absorptive channels. These results provide a reproducible link between low-energy oscillation observables and quantum-information measures. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2609.30875 [quant-ph]   (or arXiv:2609.30875v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2609.30875 Focus to learn more ar

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Quantum Zeitgeist Weekly Digestquantum-computing

Quantum Zeitgeist Weekly Digest

Logical qubits were the yardstick this week. Microsoft and Qolab published a definition of what makes a logical qubit scalable, and Infleqtion entangled 30 of them on its neutral-atom machine. IonQ showed that the classical decoding behind error correction can run on one ordinary CPU, removing a hardware bottleneck many had expected. Germany put money behind the same goal. It picked planqc and the LOGIQC consortium in its €640 million competition for error-corrected computers, and committed €122 million to a QUDORA-led project aiming for 50 logical qubits. IQM’s latest sales, in Brazil, Japan and a four-country European group, include staged upgrades toward logical operations in Finland. IonQ had the busiest week. Its Superion 256 is headed to NVIDIA’s research center, Florida International University and a new manufacturing site in South Korea. QuEra’s own survey found 45 percent of buyers now rank a fault-tolerance roadmap among their top criteria, though cost still comes first. Companies still count qubits, but buyers now want to know how many of them will be reliable. 1. Microsoft Quantum Defines Scalable Logical Qubit Characteristics Microsoft Quantum researchers, working with Qolab, have set out a definition of a scalable logical qubit. A logical qubit is one reliable unit of quantum information built from many error-prone physical qubits and kept alive by repeated error correction. The team judges them on reliability, scale, capability and performance, and says gains in one often cost ground in another. Microsoft is also working with Atom Computing and QuNorth on the Magne project, which aims to deliver a machine with more than 1,200 physical qubits encoding 50 logical qubits by late 2026. The definition gives buyers a way to compare machines on more than raw qubit count. Read more 2. IonQ Runs Real-Time Error Correction Decoder on a Single CPU IonQ has run a real-time error correction decoder on a single standard CPU. A decoder reads the error signals from a

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Quantum Computing Weekly Round-Up: Week Ending September 26, 2026quantum-computing

Quantum Computing Weekly Round-Up: Week Ending September 26, 2026

Infleqtion entangled 30 logical qubits on 80 physical atoms this week while IonQ parked Superion 256 as the first QPU at NVIDIA's Accelerated Quantum Research Center. Germany selected QUDORA for a €122 million 1,000-qubit ion-trap pilot line and planqc for a competition funded up to €640 million. Click through for Taiwan's 150-qubit RFP, Japan's Shunkai room-temperature stack, QShield 2.0, and 104 findings against China's first NGCC PQC round. Plus much more in this TQR week-ending summary. The post Quantum Computing Weekly Round-Up: Week Ending September 26, 2026 appeared first on The Qubit Report.

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Question about the non-cloning theoremquantum-computing

Question about the non-cloning theorem

Quantum Computing is part of Stack Overflow’s open communities: specialist spaces where curiosity is welcome, knowledge is shared freely, and the best answers rise to the top. Stack Overflow for Teams is now called Stack Internal. Bring the best of human thought and AI automation together at your work. Bring the best of human thought and AI automation together at your work. Learn more Bring the best of human thought and AI automation together at your work. The standard proof to the non-cloning theorem shows that no sequence of unitary quantum gates/base states measurements could be used to duplicate the state of some unknown qubit while preserving its state. But may it be possible to clone a qubit with some physical mechanism other than quantum gates or measurement? The proof ignores the possibility of other physical ways to extract information from the quantum system. So may it be possible to clone the full quantum state of a qubit by other means? Thanks for contributing an answer to Quantum Computing Stack Exchange! Use MathJax to format equations. MathJax reference. To learn more, see our tips on writing great answers. By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. This comment attacks a person or group. Learn more in our Abusive behavior policy. This comment is rude or condescending. Learn more in our Code of Conduct. A problem not listed above. Try to be as specific as possible. You'll need to complete a few actions and gain 15 reputation points before being able to upvote. Upvoting indicates when questions and answers are useful. What's reputation and how do I get it? Instead, you can save this post to reference later. Site design / logo © 2026 Stack Exchange Inc; user contributions licensed under CC BY-SA .

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Quantum chip predicts time series with a feedback loopquantum-computing

Quantum chip predicts time series with a feedback loop

Nearly four decades passed between the 1971 postulation of the memristor and its eventual demonstration in 2008. The memristor functions as the primary example of neuromorphic components due to its ability to retain memory through hysteresis, mirroring how synapses function in the human brain. This work explores integrating the memristor with quantum computing to create more efficient machine learning platforms. Photonic Quantum Memristor Enables Neuromorphic Computing The implementation of a quantum reservoir computing system using single photon states marks a first for the field, according to work detailed in a recent publication. Researchers designed the device to update its internal phase via feedback based on measurements at one output mode. The reservoir’s output then undergoes processing by a linear regression model, calculating a weighted sum to produce a final result. To test the system’s efficacy, the team addressed four distinct tasks: predicting a smooth nonlinear function and forecasting three random time series, NARMA, Mackey-Glass and Santa Fe, with vowel recognition also numerically simulated. These benchmarks allowed for a direct comparison of performance with and without the quantum memristor’s dynamic enhancements. The photonic quantum memristor itself is modeled as a tunable Mach-Zehnder interferometer, with its internal phase updated by a feedback rule dependent on measurement outcomes. The researchers employed a unitary representation of the memristor action, adaptively updated based on previous outcomes, with coefficients serving as hyperparameters within the model. For time-series prediction, they fixed certain parameters, focusing on the memory decay rate as an adjustable hyperparameter. This design allows the system to exploit the feedback mechanism to implement nonlinear operations on input states and use short-term memory, effectively demonstrating a proof-of-principle quantum reservoir computing system. The team encoded classical data us

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IonQ Just Announced a Major Breakthrough. Should Investors Buy the Stock Now?quantum-computing

IonQ Just Announced a Major Breakthrough. Should Investors Buy the Stock Now?

Quantum computers have a real problem. Small errors slip into calculations all the time, and catching and fixing them fast enough has been a real issue for the industry. On Tuesday, Sept. 22, IonQ (IONQ +1.11%) said it may have found a solution to this problem. ExpandNYSE: IONQIonQPremium FeatureMoneyball Superscore62/100Today's Change(1.11%) $0.50Current Price$45.48Key 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 shares. Implied market cap may vary.Day's Range$44.12 - $46.5552wk Range$25.89 - $84.64Volume27.6MAvg Vol21.3MGross Margin-3317.96% IonQ's statement said it has developed the industry's first end-to-end, real-time quantum error-correction decoder. Even better, this breakthrough runs on a single standard CPU. This gives IonQ a real edge in this highly competitive race. The day after the announcement, IonQ also said its Superion 256 will become the first on-premises quantum processor at Nvidia's (NVDA +0.22%) Accelerated Quantum Research Center. Image source: The Motley Fool. The stock price rose more than 11% on Wednesday, in line with the good news. IonQ remains a high-risk, speculative investment. In particular, the decoder was validated on simulated data rather than in a live system. How commercially viable quantum computing will be in the coming years remains to be seen. IonQ's valuation is also quite rich. It's deeply unprofitable as well. Investors interested in this space should recognize the sector's longer time horizon and volatility. Shares of IonQ have actually fallen more than 40% in the past year. For investors willing to stay invested for the next several years as this nascent sector finds its footing and use cases, this IonQ breakthrough is significant enough to consider buying the stock, in my opinion. Read NextSep 25, 2026 •By Parkev Tatevosian, CFAGreat News for IonQ Stock Investors!Sep 25, 2026

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