Quantum Computing Drug Discovery: Pharma Applications & Molecular Simulation
Quantum computing drug discovery news: pharmaceutical quantum simulation, molecular modeling, protein folding. Roche, Merck & biotech partnerships.
Quantum computing promises to transform pharmaceutical research by enabling first-principles molecular simulation of drug-target interactions, protein folding dynamics, and chemical reaction mechanisms that classical computers cannot accurately model. The pharmaceutical industry represents one of the highest-value near-term markets for quantum computing.
The Classical Bottleneck
Drug discovery relies heavily on molecular dynamics simulations and density functional theory (DFT) to predict how small-molecule drug candidates bind to protein targets. Classical computers cannot simulate strongly correlated electronic systems without exponential approximation errors, forcing reliance on expensive, time-consuming laboratory screening.
India's Pharmaceutical Quantum Computing Landscape
India's pharmaceutical industry, the world's third-largest by volume and a major global supplier of generic drugs, represents a strategic application domain for quantum computing under the National Quantum Mission. The NQM's Quantum Computing Thematic Hub at IISc Bengaluru includes drug discovery and molecular simulation among priority applications. Indian pharmaceutical companies including Sun Pharma, Dr. Reddy's Laboratories, Cipla, and Lupin are exploring quantum computing partnerships through collaborations with Indian quantum startups and global quantum cloud providers. The Department of Biotechnology (DBT) supports quantum biology research at institutions including IISc Bengaluru, TIFR Mumbai, and IISER Pune. The NQM targets developing quantum computers capable of simulating molecular systems relevant to drug discovery within the mission's 8-year timeline.
Near-Term Applications (NISQ Era)
Near-term applications in the NISQ era include quantum machine learning for molecular property prediction, quantum optimization of clinical trial design, quantum simulation of small molecules (10-50 atoms) for lead optimization, and hybrid approaches integrating quantum and classical molecular dynamics.
quantum-computingSuper-Logarithmic Entanglement Scaling in a Monitored Superconducting Chain
--> Quantum Physics arXiv:2607.07835 (quant-ph) [Submitted on 8 Jul 2026] Title:Super-Logarithmic Entanglement Scaling in a Monitored Superconducting Chain Authors:Rui-Jing Guo, Zhi-Yuan Wei View a PDF of the paper titled Super-Logarithmic Entanglement Scaling in a Monitored Superconducting Chain, by Rui-Jing Guo and 1 other authors View PDF Abstract:We develop a Keldysh-replica non-linear sigma model (NLSM) for the entanglement dynamics of a monitored one-dimensional spinful $s$-wave BCS chain in the rare-measurement regime, $\gamma \ll J,\Delta$. Although the clean spinful $s$-wave BCS Hamiltonian belongs to symmetry class CI, spin-resolved measurements and projection to a conserved $f$-sector reduce the effective problem to class C. Starting from the corresponding parent symplectic saddle, we show that measurement backaction and the pairing amplitude impose complementary mass constraints that gap out different fluctuation channels. Their interplay dynamically projects the surviving massless modes onto an $\textrm{SO(R)}$ target manifold in replica space. A one-loop renormalization group analysis of this $\textrm{SO(R)}$ NLSM shows that, in the replica limit $R\to1$, the beta function becomes negative, producing a weak-anti-localization flow. This flow yields a super-logarithmic steady-state entanglement scaling $S(L)\sim \ln^2 L$ in the rare-measurement regime. Our field-theoretic result explains the numerical evidence reported in the companion Letter [arXiv:2604.04375] and shows that a topologically trivial monitored $s$-wave superconductor can realize an $\textrm{SO(R)}$ weak-anti-localizing critical phase without relying on a Wess-Zumino-Witten term. Comments: Subjects: Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech); Superconductivity (cond-mat.supr-con) Cite as: arXiv:2607.07835 [quant-ph] (or arXiv:2607.07835v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.07835 Focus to learn more arXiv-issued DOI via
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quantum-computingNeural Networks Accelerate Design of Superconducting Quantum Systems
A new application of deep neural networks accelerates the design of superconducting radio-frequency cavities and transmon qubits for bosonic quantum computation. Joseph Yaker of the Superconducting and Quantum Materials System Centre (SQMS), and colleagues, in collaboration with the University of Cambridge, Illinois Institute of Technology, and Northwestern University, tackle the challenge of inverse design, determining device geometries to achieve specific electromagnetic and coupling targets. Traditionally, this becomes computationally expensive as systems scale, demanding significant resources and time for even modest design explorations. Their two deep neural network approaches rapidly map desired device behaviour to candidate designs, achieving accuracy within approximately 5% for cavity observables and 2% for transmon qubit parameters including coupling rate, frequency, and anharmonicity, as verified through re-simulation. This fast alternative to iterative simulation studies represents a key step towards scalable design of complex quantum systems. Deep learning enables two percent accuracy in transmon qubit design prediction Transmon qubit parameters are now predicted with approximately 2% accuracy, a substantial improvement over previous methods reliant on computationally expensive iterative simulations. Conventional methods, typically involving finite element analysis and optimisation algorithms, struggled to achieve comparable accuracy within reasonable timescales, often requiring weeks or months of computation for a single design iteration. Deep neural networks directly map desired device behaviour to candidate designs, bypassing the need for lengthy trial-and-error processes, which is particularly important for scaling up quantum systems where the number of design variables increases exponentially. The inherent complexity arises from the need to simultaneously optimise multiple parameters, including qubit frequency, anharmonicity (which defines the nonli
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quantum-computingDahlem Center for Co Team Models Stellar Rank Protocol for Squeezed State Generation
Scientists have conducted a thorough analysis of photon catalysis to generate squeezed coherent state superpositions, resources crucial for advancing quantum computing and error correction in optical systems. Julian K. Nauth at Freie Universitat Berlin and colleagues from Humboldt-Universitat zu Berlin detail how this technique, utilising interactions between light states, allows assessment of the non-Gaussian characteristics of both initial resources and resulting states, enabling a strong evaluation of protocol efficiency. By identifying scenarios where catalysis achieves provably optimal fidelity, and benchmarking against alternative approaches, the analysis provides practical insights into resource trade-offs and resilience against experimental imperfections, ultimately guiding the development of near-term photonic quantum technologies. High fidelity quantum state superpositions realised through optimised photon catalysis A fidelity of 0.98 in generating squeezed coherent state superpositions via photon catalysis has been achieved, representing a substantial improvement over previous methods limited to 0.75. Scientists at Dahlem Centre for Co and Science and Technology Graduate University employed photon catalysis, a hybrid technique combining low number Fock states, discrete packets of light containing a defined number of photons, and squeezed states, to create these complex quantum states. Squeezed states are non-classical states of light where the quantum uncertainty is redistributed between the amplitude and phase quadratures, reducing noise in one quadrature at the expense of increased noise in the other. This reduction in noise is vital for enhancing the sensitivity of quantum measurements and improving the performance of quantum information processing. The generation of these states typically relies on non-linear optical processes, requiring precise phase matching and efficient conversion of photons. Analysis using stellar rank formalism, a mathematical t
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quantum-computingDiraq and imec Demonstrate Eight-Qubit Linear Array Fabricated on 300 mm CMOS Silicon Foundries
Overview of the operation and calibration of an 8-dot device. Quantum engineering pioneer Diraq has announced a validation milestone in silicon-based solid-state quantum architectures with the publication of its peer-reviewed paper, “Eight-Qubit Operation of a 300 mm SiMOS Foundry-Fabricated Device,” in Nature Communications. In direct collaboration with European nanoelectronics hub imec, the research team successfully scaled a linear array of silicon spin qubits from a two-qubit unit cell to an integrated eight-qubit processor. Crucially, the multi-qubit device was manufactured entirely within a commercial, industry-standard 300 mm Silicon Metal-Oxide-Semiconductor (SiMOS) foundry line, demonstrating that highly uniform, qubit-grade quantum dot configurations can be replicated at volume without sacrificing fundamental quantum coherence or gate operational control. [ Diraq - imec 8-Qubit Hardware Matrix ] Fabrication Node ──► imec 300 mm industrial SiMOS production line on isotopically purified 28Si. Array Configuration ──► 8-dot linear chain managed as 4 double quantum dot (DQD) unit cells. Dephasing Time (T2*)──► Ramsey dephasing intervals reaching up to 41 µs (Average ~21 µs). Coherence (T2 Hahn) ──► Hahn-echo coherence times reaching up to 1.31 ms (Average ~0.7 ms). Readout Architecture──► Two-step cascaded charge-sensing to minimize wire count and thermal load. The Architecture of the 8-Dot SiMOS Micro-Array The physical device utilizes electron spins confined within electrostatically defined quantum dots as effective spin-half systems. The layer stack is fabricated on an epitaxially grown silicon substrate isotopically purified to a residual 29Si concentration of only 400 ppm to eliminate ambient nuclear spin dephasing. A triple-layer overlapping polycrystalline silicon gate stack—patterned with a tight 90 nm gate pitch—is used to outline the quantum dots instead of traditional aluminum gates, significantly reducing low-temperature lattice strain at
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quantum-computingHelium-3 Atoms Could Enable 3× Faster Quantum Simulations
Researchers are proposing a new blueprint for quantum simulation utilizing arrays of helium-3 atoms, potentially achieving speeds three times faster than current systems based on lithium-6. The design, from Zheyuan Li of the University of Illinois Urbana-Champaign and colleagues, leverages the lighter mass of helium-3 to accelerate quantum tunneling, allowing for more complex calculations within the limits of atomic coherence. Unlike previous methods that relied solely on atomic position, this system encodes information in both positional and vibrational states, simulating both bosonic modes and fermionic lattice dynamics. The team reports in PRX Quantum that the large energy spacings between vibrational modes of helium-3 make it easy to convert an atom to an intended mode without accidentally exciting it to other levels. Beyond simulation, these helium-3 arrays could also enable precise fundamental measurements, including determining the size of atomic nuclei. This potential for accelerated quantum simulations hinges on a novel approach utilizing helium-3 atoms, as detailed in theoretical work published in PRX Quantum. This departs from earlier methods that relied exclusively on positional data, offering a more comprehensive platform for complex calculations. The lighter mass of helium-3 enables a quantum tunneling rate approximately three times faster than that demonstrated with lithium-6, the next lightest trappable species, promising quicker processing speeds. The researchers’ design employs optical tweezers to trap helium-3 atoms held in a long-lived metastable state, ensuring stability during computation. These meticulously controlled helium-3 arrays offer a pathway to fundamental measurements previously limited by precision, and the team suggests they could be used to determine the size of atomic nuclei with greater accuracy, allowing for direct comparison with existing theoretical predictions. Science writer Sophia Chen notes that this capability extends the
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quantum-computingLocalized control of large ion crystals in a Penning trap using a spatial light modulator
--> Quantum Physics arXiv:2607.06654 (quant-ph) [Submitted on 7 Jul 2026] Title:Localized control of large ion crystals in a Penning trap using a spatial light modulator Authors:Allison L. Carter, Jennifer F. Lilieholm, Bryce B. Bullock, Kurt Thompson, Diep Nguyen, John J. Bollinger View a PDF of the paper titled Localized control of large ion crystals in a Penning trap using a spatial light modulator, by Allison L. Carter and 5 other authors View PDF HTML (experimental) Abstract:Penning ion traps as quantum platforms have primarily utilized global control and symmetric Dicke states for quantum simulation and sensing experiments. The introduction of local control greatly increases the power of the platform as a quantum simulator but is technically challenging due to the rapid rotation of the ion crystals. Here we use an ultraviolet-compatible spatial light modulator (SLM) to imprint programmable AC Stark shift patterns with different azimuthal symmetries and gradients that co-rotate with the ion crystals, demonstrating localized coherent control of single plane crystals with greater than 100 ions. Comparisons of the measured ion qubit populations with calculations from independent measurements of the applied AC Stark shift patterns show good agreement, validating the technique and providing a path, with a higher format SLM, for parallelizable, coherent individual ion addressing in Penning traps. Comments: Subjects: Quantum Physics (quant-ph); Atomic Physics (physics.atom-ph) Cite as: arXiv:2607.06654 [quant-ph] (or arXiv:2607.06654v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.06654 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Allison Carter [view email] [v1] Tue, 7 Jul 2026 17:43:03 UTC (5,481 KB) Full-text links: Access Paper: View a PDF of the paper titled Localized control of large ion crystals in a Penning trap using a spatial light modulator, by Allison L. Carter and 5 othe
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quantum-computingSpin singlets are useful
--> Quantum Physics arXiv:2607.06672 (quant-ph) [Submitted on 7 Jul 2026] Title:Spin singlets are useful Authors:Silas Hoffman, Edward H. Chen, Matthew Brooks, Stephen Carr, Daniel Volya, Alan Tran, Tyler Keating, Thaddeus D. Ladd, Charles Tahan View a PDF of the paper titled Spin singlets are useful, by Silas Hoffman and 8 other authors View PDF HTML (experimental) Abstract:We evaluate the utility of the spin-zero manifold of an exchange-coupled array of $N$ spins for tasks in quantum computation and quantum simulation. Since pairs of electrons can be readily initialized into a product state of singlets in semiconducting quantum dot arrays, the full spin-zero manifold is available with exchange-only control, providing a Hilbert space of approximate dimension $2^N/(N/2)^{3/2}$, asymptotically close to the $2^N$ dimension of the full spin Hilbert space. Leveraging the spin-zero manifold enables larger computational space in a given array compared to traditional exchange-only control, in which spin arrays are organized into modular units of $n$ spins comprising $N/n$ encoded qubits, limiting to the exponentially smaller Hilbert dimension $2^{N/n}$. Here we focus on benchmarking metrics for this resource utilization by generalizing cross-entropy benchmarking, mirror benchmarking, and out-of-time-ordered correlators to this system. We show that operating in the spin-zero manifold can accelerate the realization of computational quantum advantage applications in semiconductor-based spin qubits. Comments: Subjects: Quantum Physics (quant-ph); Mesoscale and Nanoscale Physics (cond-mat.mes-hall) Cite as: arXiv:2607.06672 [quant-ph] (or arXiv:2607.06672v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.06672 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Silas Hoffman [view email] [v1] Tue, 7 Jul 2026 18:00:04 UTC (206 KB) Full-text links: Access Paper: View a PDF of the paper titled Spin single
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quantum-computingFeynman's clock and hierarchy-informed sampling for quantum error mitigation
--> Quantum Physics arXiv:2607.06752 (quant-ph) [Submitted on 7 Jul 2026] Title:Feynman's clock and hierarchy-informed sampling for quantum error mitigation Authors:Theo Saporiti View a PDF of the paper titled Feynman's clock and hierarchy-informed sampling for quantum error mitigation, by Theo Saporiti View PDF HTML (experimental) Abstract:Near-term physical implementations of quantum algorithms require efficient quantum error mitigation schemes to reduce quantum noise. In this letter we propose a new mitigation technique, by extending the applicability of our BBGKY-ISM scheme from quantum simulations of spin chains to arbitrary quantum circuits. We map executions of quantum circuits using Feynman's clock Hamiltonian to the Hamiltonian dynamics of a corresponding quantum system, whose time evolution obeys a BBGKY-like hierarchy of equations informing the BBGKY-ISM mitigation. We show that the method's classical and quantum overheads are polynomial in the circuit size and in the number of qubits. We apply our method to numerical simulations of tunable Bell state preparation circuits under state-of-the-art quantum noise, and numerically demonstrate its systematic and controllable quantum error reduction capability. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2607.06752 [quant-ph] (or arXiv:2607.06752v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.06752 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Theo Saporiti [view email] [v1] Tue, 7 Jul 2026 19:33:58 UTC (421 KB) Full-text links: Access Paper: View a PDF of the paper titled Feynman's clock and hierarchy-informed sampling for quantum error mitigation, by Theo SaporitiView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2026-07 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX cita
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quantum-computingA quantum model for synchronizing finite state transition systems
--> Quantum Physics arXiv:2607.06953 (quant-ph) [Submitted on 8 Jul 2026] Title:A quantum model for synchronizing finite state transition systems Authors:Martin Lukac, Khaled El-Fakih, Uraz Turker View a PDF of the paper titled A quantum model for synchronizing finite state transition systems, by Martin Lukac and 2 other authors View PDF Abstract:We propose a quantum model for finding a resetting input sequence (RS) which can take a finite state transition system (FA), to particular state independent of its current state. The complexity of finding such sequences for various types of FA can be NP-Hard or even PSPACE-Complete. To this end, we represent the FA states, inputs, and transition function in quantum space. Accordingly, we propose a model to represent the execution of an input sequence of a particular length $l$ starting form an initial FA state. The model is extended considering the application in superposition of all input sequences of length $l$ to an initial state of the FA. The model is further extended considering the application of all input sequences to all initial states of the FA capturing for every input sequence the collection (ordered list) of states reached by applying the sequence to all states of the FA. The amplitude amplification algorithm is then used as it combines similar collections of reached states while preserving all input sequences that reach these collections. A Grover search for a reached collection where its elements correspond to the same FA state provides a RS for the FA. Our approach offers a quadratic gain over the exponential complexity of traditional brute-force method, which is the only method that can be applied to a general FA class. As a proof of concept we provide results of several simulated FAs on a quantum simulator. Comments: Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET) ACM classes: D.2.5; F.1.1; F.2.1; I.1.2; J.6 Cite as: arXiv:2607.06953 [quant-ph] (or arXiv:2607.06953v1 [quant-
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quantum-computingResearchers Build Quantum Circuits Using Ising Model and Time-Dependent Fields
Matthias Werner at the IUniversity of Barcelona and colleagues have found a fundamental connection between the transverse-field Ising model and standard gate-based quantum computation. The Ising model, when driven by a specifically tailored, time-dependent transverse field, simulates any quantum circuit with a polynomial increase in computational resources. This finding answers a long-standing question regarding the computational power of analogue quantum simulation platforms, such as those employing quantum annealing, and importantly, suggests inherent limitations for classically simulating this type of Ising model. The research also has implications for complexity theory and the control of quantum systems, potentially motivating improvements in simulating quantum circuits using the Ising model. Transverse-field Ising model replicates universal quantum circuits with polynomial overhead A significant advance in quantum simulation has been realised, demonstrating a polynomial increase in time, qubit number, and energy scale when simulating quantum circuits using the transverse-field Ising model. This represents a substantial improvement over previous methods, which lacked a clear pathway to universal quantum computation with predictable resource scaling. The Ising model, driven by a carefully controlled, time-varying transverse field, effectively replicates any quantum circuit, unlocking the potential for utilising analogue quantum simulation platforms for broader computational tasks. The significance of this lies in the potential to move beyond specialised optimisation problems, for which quantum annealers are currently designed, towards a more general-purpose quantum computing paradigm based on analogue principles. Previous attempts to demonstrate universality often suffered from exponential scaling of resources, rendering them impractical. This work establishes a polynomial scaling relationship, offering a more viable route to scalability, although substantial cha
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quantum-computingUniversity of Augsburg Team Designs Valence Bond Embeddings for Deep Chemistry Simulations
Scientists at the University of Augsburg have developed a new methodology addressing a fundamental challenge in quantum chemistry: the accurate and efficient simulation of large molecular systems. Francisco Javier del Arco Santos and Jakob S. Kottmann have combined hybrid Fermionic-Bosonic encodings with Quantum Valence Bond Theory to construct quantum circuits capable of representing more complex molecules than previously achievable, offering a potential pathway towards resolving bottlenecks in quantum computation and expanding the scope of variational quantum eigensolvers. Hybrid encoding and Quantum Valence Bond Theory expand accessible molecular simulation scales A six-fold increase in the size of molecular systems simulated using variational quantum eigensolvers has been demonstrated, significantly exceeding the limitations inherent in traditional active space methods. Published on June 26, this advancement facilitates the simulation of chemically relevant systems that were previously intractable due to computational constraints and the inherent limitations of current quantum hardware. Conventional quantum chemistry calculations often struggle with molecules containing more than a few dozen electrons, owing to the exponential scaling of computational resources with system size. The University of Augsburg researchers overcame this hurdle by strategically combining hybrid Fermionic-Bosonic encodings with Quantum Valence Bond Theory to systematically construct quantum circuits, establishing a clear and direct relationship between the chosen encoding scheme and the resulting electronic structure representation. This allows for a more nuanced and controlled approach to quantum simulation. Quantum circuits now provide novel avenues for simulating molecular properties, circumventing the limitations of existing techniques and opening possibilities for more intricate chemical investigations. The core innovation lies in achieving a more compact and flexible representatio
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quantum-computingOkayama University: Researchers Find New Material Promising for Quantum Computing Advances
Ogawa and colleagues at the University of Tokyo have recently published findings concerning the intriguing superconducting properties of K2Cr3As3. Mapping electron pairing in K2Cr3As3 using arsenic-75 nuclear magnetic resonance Nuclear magnetic resonance (NMR) proved key in discerning the subtle changes occurring within K2Cr3As3 as it transitioned between superconducting states. NMR is a spectroscopic technique that exploits the magnetic properties of atomic nuclei to provide detailed information about the material’s structure and dynamics. It detects the nuclei of atoms, revealing their magnetic environment and thus providing insights into electron behaviour. The 75As isotope was utilised, owing to its sensitivity to local spin susceptibility, to map the arrangement of paired electrons. Arsenic-75 possesses a nuclear spin of I = 3/2, making it particularly well-suited for NMR studies of magnetic materials. Its sensitivity allowed investigation of the material’s superconducting properties, offering a detailed view of electron interactions. The technique relies on applying a radiofrequency pulse to the nuclei in a strong magnetic field and observing the frequency at which they resonate, which is affected by the local magnetic environment. This allows researchers to probe the electronic structure and pairing symmetry of the superconductor. K2Cr3As3 exhibits superconductivity up to 6.2 Kelvin, a relatively high transition temperature for a Chromium-based material, and lacks long-range magnetic order which simplifies analysis. This is significant because many candidate materials for topological superconductivity suffer from the presence of competing magnetic orders that obscure the superconducting signal and complicate the interpretation of experimental results. NMR was favoured over techniques like nuclear quadrupole resonance (NQR) as it directly probes the spin state of the superconducting electrons, confirming a spin-triplet pairing mechanism. In spin-triplet pairin
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quantum-computingJin and Colleagues Develop Learning-Based Methods for Quantum Sensing and Networking
A thorough review of the increasingly intertwined fields of artificial intelligence and quantum information has been completed by Min Chen of University of Pittsburgh and colleagues. The review details how AI acts as a set of tools for advancing quantum system learning, design, control, and verification, whilst quantum information presents new computational models and learning paradigms for AI development. This survey organises recent advances around key tasks including information extraction from limited measurements, quantum algorithm training and discovery, hardware stabilisation, workflow automation, and the extension of learning methods to sensing and networking. Furthermore, the work examines the impact of quantum computation and quantum-inspired structures on learning, considering algorithmic speedups, expressivity, and neural-network design, highlighting the vital need for integrated theory, experiment, and hybrid quantum-classical systems to enable overcoming challenges in reproducibility and scalability. Using tensor networks for advances in quantum and machine learning Tensor-network representations proved central to enabling these advances, functioning as a way of organising complex data into a network of interconnected nodes, similar to how a family tree shows relationships between individuals. Data represented in this interconnected format reduced the computational burden of processing high-dimensional information, a key challenge in both quantum simulations and advanced AI algorithms. These networks were used to model the intricate connections within quantum states and machine learning models, allowing for more efficient computation and analysis. The fields of artificial intelligence and quantum information are becoming increasingly intertwined. A recent survey details progress in using AI to improve quantum systems, focusing on tasks like interpreting limited measurements and training quantum algorithms. The team also examined how quantum computation
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quantum-computingHunan Normal University: Researchers Achieve Highly Pure, Indistinguishable Single Photons for Quantum Computing
A new method for generating single photons offers key components for advancing quantum technologies. Ying Ren and colleagues at Hunan Normal University demonstrate a robust scheme for deterministic single-photon emission utilising a three-level atom coupled to a single-mode cavity. The research achieves second-order correlation functions reaching approximately 10-8 under ultrastrong coupling with pulsed driving. Alongside this, photon indistinguishability exceeds $98.73\% and state purities up to 99.99\%. This near-ideal performance represents a step towards overcoming limitations in current single-photon sources and promises to accelerate progress in quantum computing and fundamental quantum optics. Demonstrated high-purity single-photon emission via ultrastrongly coupled atom-cavity systems Purity levels in this new single-photon source have now reached 99.99%, a substantial improvement over previously demonstrated methods. Achieving such high purity and indistinguishability, essential for complex quantum calculations, remained a significant obstacle until recently. Conventional sources, such as spontaneous parametric down-conversion (SPDC) and quantum dots, struggled to consistently produce photons with the required characteristics, often exhibiting multi-photon emission or lacking the necessary control over photon properties. SPDC, while relatively efficient, inherently generates photon pairs, necessitating complex filtering to isolate single photons. Quantum dots, though capable of single-photon emission, suffer from spectral wandering and limited purity. A scheme utilising a three-level atom coupled to a single-mode cavity surpasses these limitations, demonstrating an indistinguishability of 99.10% under ultrastrong coupling, and even higher values with pulsed driving. The cavity confines the light, enhancing the interaction with the atom and increasing the probability of single-photon emission, while the three-level atomic structure allows for precise control
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quantum-computingRice University and Max Planck Society Launch Quantum Materials Research Partnership
Insider Brief Press release – Rice University and the Max Planck Society officially launched the Quantum Materials – Rice and Max Planck Partnership (Q-RaMP) June 19, aimed at supporting the identification and development of quantum materials that will support breakthroughs in sustainability, energy efficiency and quantum and classical information processing. “Breakthrough technologies begin with breakthrough […]
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quantum-computingInterplay Between Quantum Coherence and Multiparameter Quantum Estimation in Graphene
--> Quantum Physics arXiv:2607.05661 (quant-ph) [Submitted on 6 Jul 2026] Title:Interplay Between Quantum Coherence and Multiparameter Quantum Estimation in Graphene Authors:Younes Moqine, Brahim Adnane, Abdelilah El Rhazali, and Rachid Houça View a PDF of the paper titled Interplay Between Quantum Coherence and Multiparameter Quantum Estimation in Graphene, by Younes Moqine and Brahim Adnane and Abdelilah El Rhazali and and Rachid Hou\c{c}a View PDF HTML (experimental) Abstract:In this work, we investigate the relationship between quantum coherence and multiparameter quantum estimation in a graphene-based system. We focus on the estimation of two relevant physical parameters, namely the temperature $T$ and the wave vector $k_x$, and analyze how their variations affect both quantum coherence and the achievable metrological precision. The minimum variances associated with the estimation process are evaluated through the quantum Cramér--Rao bound within both simultaneous and independent estimation schemes. Our results show that quantum coherence is enhanced in the low-temperature regime and around $k_x=0$, while it decreases progressively as either the temperature or the wave vector increases. However, the regions where coherence is maximal do not necessarily coincide with those of optimal estimation precision. In particular, the variance associated with temperature estimation exhibits a divergent behavior near $T=0$, indicating that the system becomes weakly sensitive to small temperature variations in this regime. By contrast, the estimation of the wave vector $k_x$ is more directly related to the coherence properties of the system, with improved precision obtained near $k_x=0$. Furthermore, we introduce the ratio $\Gamma$ to compare the total variances obtained from the independent and simultaneous estimation schemes. This quantity provides a useful measure of the relative difference between the two strategies when the parameters are estimated separately or jointly
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quantum-computingInstitut für Physik: Quantum Catalysis: Scientists Detail 3 Key Findings for 2026
Researchers at Humboldt-Universität zu Berlin and Freie Universität Berlin are detailing new findings in quantum catalysis, presenting a method for characterizing the complexity of non-Gaussian quantum states. The team reports employing the stellar rank formalism to measure both the resources needed to create these states and the resulting states themselves, allowing for a systematic comparison of fidelity and optimization of protocols. This work focuses on generating squeezed coherent state superpositions through photon catalysis between low number Fock states and squeezed states, offering a pathway toward more deterministic quantum computing. According to the researchers, “non-Gaussian quantum states and operations constitute essential resources for achieving quantum computational advantage,” and this analysis provides “practical guidelines for near-term photonic implementations.” Finite stellar rank states are robust against approximations with states of lower stellar rank Researchers at Freie Universität Berlin and Humboldt-Universität zu Berlin have demonstrated that finite stellar rank states exhibit a surprising robustness against approximations utilizing states of lower stellar rank, a finding with significant implications for the scalability of photonic quantum computing. This work details the behavior of moving beyond simply creating these states to developing a rigorous method for characterizing their complexity, utilizing the stellar rank formalism to quantify both the input resources and the resulting quantum states. The team’s findings suggest that carefully designed approximations can preserve the essential non-Gaussian characteristics needed for quantum advantage without requiring exponentially increasing resources, a critical step toward practical implementation. The research centers on generating squeezed cat states through a process called photon catalysis, which involves interactions between low number Fock states and squeezed states. This precis
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quantum-computingNVIDIA, JSC Simulate Quantum System With Record 50 Qubits
Researchers at the Jülich Supercomputing Centre (JSC), in collaboration with NVIDIA, have achieved a new milestone in quantum simulation by fully simulating a 50-qubit system on the JUPITER supercomputer. This surpasses the previous record of 48 qubits simulated by Jülich researchers and their Japanese counterparts on Japan’s K supercomputer in 2019, demonstrating the growing capabilities of Europe’s first exascale computer. Utilizing JUPITER’s architecture, which uses NVIDIA’s Grace CPUs and Hopper GPUs for its GH200 superchip architecture, the team tackled the immense computational challenge of representing each qubit’s superposition of states. “HPC-based simulators can act as a perfect flight simulator,” explains Dr. Hans De Raedt of JSC’s Quantum Information Processing group, allowing researchers to isolate algorithmic errors from hardware limitations as they develop future quantum computers. JUPITER Supercomputer Achieves 50-Qubit Quantum Simulation JUPITER, Europe’s fastest high-performance computing system and the first in the region to exceed the exascale threshold, was central to this milestone. Accurately modeling quantum systems is crucial given the challenges in building physical quantum computers, which are expensive and prone to errors. Dr. De Raedt explains, “Building a quantum computer is incredibly expensive and the hardware is still very noisy and prone to errors.” Researchers face exponential growth in computational demands as qubit counts increase; simulating 50 qubits requires tracking over a quadrillion possibilities. To overcome this, the team utilized 4,096 nodes of JUPITER, generating more than a petabyte of data, and implemented a novel memory compression technique that reduced memory requirements eightfold. This simulation provides a flight simulator for quantum algorithms, allowing researchers to test their ‘flight plans’, the quantum algorithms, in a controlled environment where the correct answer is known. This enables researchers to is
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quantum-computingVeeco & k-Space Link MBE Growth to Qubit Integrity in Real Time
The delicate quantum states underpinning future computation are now subject to scrutiny during their very creation, as Veeco and k-Space Associates combine advanced material growth with real-time measurement. Researchers report that qubit coherence times, the duration a qubit maintains its state, are directly impacted by a previously understated sensitivity in quantum material fabrication. This collaboration moves beyond simply achieving process capability to integrating measurement into the growth process itself, prioritizing a deeper understanding of material development. “Quantum computing places extraordinary demands on materials quality, where even minor atomic-scale variations can significantly impact device performance,” said Matt Marek, Vice President of MBE Products at Veeco, emphasizing a shift toward valuing the ability to grow materials as much as the ability to grow materials at all. MBE & RHEED Enable Quantum Material Growth Insight The pursuit of stable qubits hinges on a narrow margin for error during material creation; even subtle atomic-scale variations can dramatically impact performance. A collaboration between Veeco and k-Space Associates exemplifies this trend, combining advanced molecular-beam epitaxy (MBE) systems with real-time metrology to provide researchers with insight into material development. Process intelligence is now considered as vital as process capability, signaling a move from merely being able to grow quantum materials to deeply understanding the growth process for improved consistency and reproducibility. Veeco MBE systems already incorporate reflection high-energy electron diffraction (RHEED), and this is frequently paired with RHEED analysis technology from k-Space. RHEED functions as a real-time window into crystal growth, revealing surface structure, morphology, and growth dynamics as materials are deposited atom by atom. The k-Space KSA 400 platform is a leading solution for acquiring and analyzing this RHEED data, a
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quantum-computingAlfred University and Classiq Launch Joint Academic Quantum Computing Initiative
Alfred University and Classiq Launch Joint Academic Quantum Computing Initiative Alfred University, the New York State College of Ceramics, and quantum development firm Classiq have announced a joint quantum computing initiative designed to integrate functional hardware-portable software modeling into engineering curricula and energy systems research. The academic collaboration deploys Classiq’s high-level synthesis platform to bypass manual, gate-level quantum circuit construction, allowing students and researchers to engineer functional algorithms without deep low-level compilation skills. The curriculum expansion is intended to support workforce development and applied energy optimization models across the State University of New York (SUNY) network. [ Alfred University - Classiq Framework ] Software Engine ──► Classiq high-level functional synthesis platform using agentic compilation workflows. Research Focus ──► Power system unit commitment optimization and ceramic/glass materials discovery. Academic Integration──► Inamori School of Engineering curricula, expanding across CUNY and SUNY networks. The instructional integration is led by Junpeng Zhan, Assistant Professor of Renewable Energy Engineering at the Inamori School of Engineering, who has embedded the platform into active courses. Zhan’s core research focuses on power systems optimization, specifically the “unit commitment problem”—a multi-variable calculation where electric grid operators determine the most cost-effective generation schedules to meet fluctuating regional energy demands. The joint initiative builds upon Zhan’s previous National Science Foundation (NSF)-funded computational grants and a 2024 collaborative research program with the Rochester Institute of Technology and ISO-New England to explore quantum optimization paths for wholesale electrical grids. Concurrently, the initiative expands into solid-state physics and advanced materials modeling under S. K. Sundaram, Inamori Professor of Ma
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