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-computingNew Technique Unlocks Key to Simulating Complex Molecular Behaviour Accurately
Researchers continue to grapple with the long-standing N-representability problem for reduced density matrices, a critical issue within electronic structure theory. Ofelia B. Oña from the Universidad Nacional de La Plata, alongside Gustavo E. Massaccesi and Pablo Capuzzi from the Universidad de Buenos Aires, and et al., present a novel framework for determining ensemble N-representability of p-body matrices. Building upon their previous work utilising adaptive derivative-assembled pseudo-Trotter methods, this study introduces a purification strategy that embeds ensemble states into pure states, enabling assessment via minimisation of the Hilbert-Schmidt distance. This methodology not only allows for the correction of defective density matrices but also offers a pathway for robust state reconstruction, representing a significant advance in density-matrix refinement and validation through numerical simulations on systems ranging from two to four electrons. This breakthrough addresses a critical gap in existing methodologies, which largely focus on pure-state representability while overlooking the importance of ensemble states in diverse applications such as thermal mixtures and open quantum systems. The research introduces a purification strategy, embedding an ensemble state into a pure state defined on an extended Hilbert space, ensuring identical reduced density matrices for both states. By iteratively applying unitaries to an initial purified state, the algorithm minimizes the Hilbert-Schmidt distance between its p-body reduced density matrix and a specified target matrix, effectively gauging the N-representability of the target. This methodology not only assesses whether a given matrix corresponds to a physically valid N-electron state, but also facilitates the correction of defective ensemble reduced density matrices and enables quantum-state reconstruction for density-matrix refinement. The core of the work builds upon a previously established pure-state algorit
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quantum-computingMolecular Chaos Mapped with New Diagrams Reveals Hidden Order in Potassium Cyanide
Scientists have long sought to understand the complex vibrational behaviour of highly nonlinear molecules, and a new study utilising variable parameter correlation diagrams offers significant insight into these systems. H. Párraga, F. J. Arranz, and R. M. Benito, alongside F. Borondo et al., demonstrate the utility of this approach by examining the vibrational spectrum of potassium cyanide (K-CN). Their research reveals how classical structures, specifically Kolmogorov-Arnold-Moser tori, manifest as emerging diabatic states within the correlation diagrams, a phenomenon obscured by conventional constant-Planck analyses. This methodology successfully unveils a transition from order to chaos, presenting it as a frontier of scarred functions and providing a novel means of characterising molecular dynamics. This technique reveals hidden classical structures, specifically Kolmogorov-Arnold-Moser tori, as emerging diabatic states in the quantum levels correlation diagram, structures that would otherwise remain obscured when using a fixed value for Planck’s constant. The research focuses on the K-CN molecule, a system known for its complex and chaotic dynamics, and demonstrates a pathway to understanding the transition from order to chaos through the identification of a frontier of scarred functions. The work builds upon established correlation diagrams, traditionally used to rationalize molecular rovibrational states based on real-valued parameters like geometrical distances or angles. Instead, researchers artificially varied the Planck constant, ħ, to effectively implement a microscopic lens focusing on classical regular structures embedded within chaotic regions of the molecular phase space. By reducing ħ, quantum states are confined to smaller phase space volumes, allowing for detailed examination of their dynamical characteristics within the regular classical region. This approach provides a unique perspective on the interplay between quantum and classical behaviour in
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quantum-computingQuantum Algorithm Cuts Molecular Energy Calculations’ Costs with Streamlined Approach
Scientists are continually seeking improvements to variational quantum eigensolver algorithms for accurate molecular ground state energy calculations. Runhong He, Xin Hong (Key Laboratory of System Software, Chinese Academy of Sciences), and Qiaozhen Chai, alongside Ji Guan, Junyuan Zhou, and Arapat Ablimit, present a novel approach to enhance the adaptive derivative-assembled pseudo-trotter variational eigensolver (ADAPT-VQE). Their research introduces Param-ADAPT-VQE, an algorithm that intelligently selects excitation operators using a parameter-based criterion, effectively reducing redundancy and associated measurement costs. By combining this with a sub-Hamiltonian technique and a hot-start optimisation strategy, the authors demonstrate significant gains in computational accuracy and scalability, paving the way for more practical applications of ADAPT-VQE in molecular simulations. Parameter selection optimises variational quantum eigensolver performance for molecular simulations, leading to improved accuracy and efficiency Scientists have developed a new algorithm, Param-ADAPT-VQE, that significantly enhances the efficiency of molecular ground state energy calculations performed on quantum computers. This breakthrough addresses critical limitations in existing methods by reducing computational inaccuracies, minimising the size of the required quantum circuits, and dramatically lowering the number of measurements needed to achieve reliable results. The research introduces a parameter-based criterion for selecting excitation operators, a key component in building the quantum circuit, effectively avoiding the inclusion of redundant operators that hinder performance. This innovative approach moves beyond traditional gradient-based methods, offering a more robust and streamlined pathway to accurate molecular simulations. The core of this advancement lies in the optimisation of the adaptive derivative-assembled pseudo-trotter variational quantum eigensolver, or ADAPT-
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quantum-computingQuantum Simulations Take a Leap Forward with Superconducting Circuits
Quantum computing promises to revolutionise several scientific and technological domains through fundamentally new ways of processing information. Laurin E. Fischer, affiliated with the Laboratoire de théorie et simulation des matériaux, Faculté des sciences et techniques de l’ingénieur, University of unspecified location and IBM Quantum, alongside colleagues, demonstrate significant progress in enabling large-scale digital quantum simulations using superconducting qubits. This research is particularly significant because it addresses a critical limitation in current quantum devices, imperfections that hinder practical advantage for complex problems in fields such as condensed matter physics and materials science. By exploring methods across the computational stack, including hardware innovations, noise modelling, error mitigation and algorithmic improvements, this work represents a crucial step towards extracting meaningful results from noisy quantum data and realising the full potential of quantum simulation. The thesis was presented on 28 October at the Faculty of Science and Engineering, Laboratory of Theory and Simulation of Materials, Doctoral Programme in Materials Science and Engineering for the degree of Doctor of Science by Laurin Elias Fischer. It was accepted on the proposal of the jury, with Professor Harald Brune as president, Professors Nicola Marzari and Ivano Tavernelli as thesis directors, Professor Zoë Holmes as rapporteur, Professor Zoltán Zimborás as rapporteur, and Professor Frank Wilhelm-Mauch as rapporteur. The work is documented as arXiv:2602.04719v1 [quant-ph] from February 2026. Advancing quantum simulation through hardware innovation, noise mitigation and algorithmic refinement promises to unlock previously intractable scientific challenges Scientists across condensed matter physics and materials science widely recognise the transformative potential of quantum computing. However, the realization of practical quantum advantage for problems
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quantum-computingComplex Chemical Calculations Made 25% Cheaper with New Quantum Technique
Researchers are continually seeking methods to reduce the computational cost of accurately modelling electronic structure, particularly for strongly correlated systems. Prateek Vaish and Brenda M. Rubenstein, both from the Department of Chemistry at Brown University, alongside Vaish et al., present a novel active space partitioning approach to significantly reduce the expense of Unitary Coupled Cluster (UCC) theory. Their work addresses the limitations imposed by the steep scaling of UCC’s Baker-Campbell-Hausdorff expansion by combining a truncated UCCSD(4) method within a selected active space with MP2 treatment of external excitations. This innovation offers a tractable pathway for modelling correlated molecules and reactions on current classical computers, and importantly, provides a viable strategy for scaling UCC calculations to meet the demands of resource-constrained hardware. This work introduces an active space UCCSD(4)/MP2 method, effectively partitioning the complex calculations to make them tractable for both classical computers and emerging quantum hardware. The research centres on a fourth-order truncation of UCCSD within a selected active space, complemented by treatment of external excitations at the MP2 level, offering a pathway to scale UCC calculations for resource-constrained systems. Two distinct formulations were explored: a composite method summing internal and external contributions, and an interacting method coupling amplitudes for enhanced accuracy. Testing encompassed the GW100 dataset, a metaphosphate hydrolysis reaction, and the strongly correlated torsion of ethylene, revealing key insights into the performance of each formulation. Results demonstrate that the interacting method, utilising canonical orbitals, maintains robustness and accurately reproduces full UCCSD(4) potential energy curves while employing only 15, 25% of the virtual orbitals within its active space. In contrast, the composite formulation proved more sensitive to both
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quantum-computingSimulating Heat with Quantum Particles Unlocks New Materials Science Possibilities
Scientists are developing new methods to simulate the behaviour of thermal states, crucial for understanding complex quantum systems. Manuel S. Rudolph, Armando Angrisani, and Andrew Wright, alongside Iwo Sanderski, Ricard Puig, Zoë Holmes et al. from the Ecole Polytechnique Fédérale de Lausanne and Algorithmiq Ltd, present a propagation-based approach utilising Pauli and Majorana operators to model imaginary-time evolution. This research is significant because it efficiently represents high-temperature states, which are often sparse and difficult to simulate with conventional techniques, offering analytic guarantees for error control and demonstrating effectiveness through large-scale numerical simulations on established models. Simulating Finite Temperature Quantum Systems via Pauli and Majorana Operator Propagation offers a promising avenue for exploring complex quantum phenomena Scientists have developed a novel approach to simulating thermal states using Pauli and Majorana propagation techniques adapted for imaginary-time evolution. This work addresses a critical challenge in material science, condensed matter physics, and quantum chemistry: accurately modelling quantum systems at finite temperatures. The research centres on the observation that high-temperature states exhibit sparsity in Pauli or Majorana bases, simplifying their representation and enabling efficient computation. By formulating imaginary-time evolution directly within these operator bases and initiating the process from a maximally mixed state, researchers have unlocked access to a range of temperatures where the quantum state remains efficiently manageable in terms of computational resources. The study introduces a propagation-based method that begins with the maximally mixed state, represented by the identity operator, and evolves it using a sequence of imaginary-time gates. This allows for the efficient storage and manipulation of high-temperature states, as the complexity increases with de
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quantum-computingArtificial Black Holes Emit Radiation, Mimicking Hawking’s Groundbreaking Prediction
Researchers are increasingly exploring condensed matter systems to simulate and understand phenomena associated with black holes, and a new study by Jaiswal, Shankaranarayanan, and colleagues from the Department of Physics, Indian Institute of Technology Bombay, details the emergence and detection of Hawking radiation within a quenched chiral spin chain. This work is significant because it moves beyond simply demonstrating analogous black hole conditions to analysing the characteristics of the emitted radiation and proposing methods for its unambiguous detection. By employing both field-theoretic calculations and modelling operational quantum sensors, the team reveal deviations from ideal blackbody spectra and establish a clear protocol for differentiating genuine analog Hawking radiation from background noise in experimental platforms. Analogue Hawking radiation emerges from a chirally-driven spin chain quantum simulator through collective excitations of magnons and triplons Researchers have demonstrated the emergence and detection of Hawking radiation within a one-dimensional chiral spin chain, offering a novel platform for investigating quantum gravity phenomena. This work simulates gravitational collapse using a sudden quantum quench, inducing a phase transition that mimics the formation of a black hole horizon. By mapping the spin chain dynamics onto a Dirac fermion in a curved spacetime, the study meticulously analyzes the resulting radiation spectrum and its detectability through two distinct approaches: field-theoretic modes and operational quantum sensors. Initial findings reveal that the observed radiation spectrum deviates from the ideal Planckian form, exhibiting frequency-dependent characteristics analogous to greybody factors, yet maintains robust Poissonian statistics indicative of information loss at the formation scale. To further probe this analogue Hawking radiation, a qubit was introduced as a stationary Unruh-DeWitt detector, coupled to the chir
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quantum-computingPhD Projects in Theoretical Quantum Optics and Quantum Information at he Niels Bohr Institute
PhD Projects in Theoretical Quantum Optics and Quantum Information at he Niels Bohr Institute Application deadline: Sunday, March 15, 2026Research group: Theoretical quantum optics group at the Niels Bohr InstituteTheoretical quantum optics group at the Niels Bohr InstituteEmployer web page: Theoretical Quantum Optics GroupJob type: PhDTags: quantum opticsQuantum theoryquantum informationThe Niels Bohr Institute invites applicants for two PhD fellowships in Theoretical Quantum Optics and Quantum Information. The projects will be part of the theoretical quantum optics group and the Center for Hybrid quantum Networks (Hy-Q). The starting date is (expected to be) 1 September 2026 or as soon as possible thereafter. An earlier starting date may also be a possibility. The projects Two different projects are available Quantum Internet technology. This project will be part of the Quantum Internet Alliance (QIA), a joint European network aiming at bulding the world’s first quantum internet protype within the duration of the Ph.D. project. The successful candidate will develop physical models of the system being built with the aim of predicting and optimizing its performance. In addition the project will develop general theories for quantum internet technologies and methods for describing them. Scalable quantum information processing based on quantum dots. The projects aims at developing theories for how to implement quantum information processing with quantum dots strongly coupled to light and will be a collaboration with experimentalists at the Niels Bohr Institute, Ruhr-Universität Bochum and the University of Basel. The goal is to both develop concrete proposals for experiments which can be implemented in the near future and long term architectures for quantum information processors. Who are we looking for? We are looking for candidates within the field of Physics, Quantum Information Processing or related areas. Applicants can have a background f
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quantum-computingSuperconductivity’s Hidden Vibrations Unlocked by New Raman Response Theory
Researchers are increasingly focused on understanding collective excitations within multicomponent superconductors, and a new study by Yuki Yamazaki and Takahiro Morimoto, both from the Department of Applied Physics at The University of Tokyo, details a microscopic theory of Raman responses to these modes. Their work establishes a gauge-invariant expression for Raman susceptibility, applicable to a broad range of superconducting systems including those with multiband structures and unconventional pairing symmetries. Significantly, this research provides a unified framework, based on higher-order Lifshitz invariants, to classify Raman-active collective modes and predict novel in-gap resonances, demonstrated through application to the heavy-fermion material UTe, offering crucial insights into the behaviour of complex superconducting states. Microscopic theory predicts Raman spectra and classifies collective excitations in multicomponent superconductors Scientists have developed a comprehensive microscopic theory detailing how light interacts with collective excitations in multicomponent superconductors. This work establishes a directly computable Raman susceptibility, applicable to a broad range of superconducting systems including those with single or multiple energy bands, spin-singlet or triplet order parameters, and both time-reversal-symmetric and symmetry-breaking states. The resulting framework allows for the prediction of Raman spectra given a specific Bogoliubov, de Gennes (BdG) Hamiltonian, effectively linking material properties to observable spectroscopic signatures. A key achievement is the derivation of a symmetry selection rule, classifying Raman-active collective modes across all crystalline point groups using a “higher-order Lifshitz-invariant” approach. This classification unifies the identification of crucial modes such as the Leggett mode, Bardasis-Schrieffer (BS) mode, and clapping mode, providing a systematic way to understand their behaviour. Re
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New Material Hosts ‘Majorana’ Particles for Robust Quantum Computing Networks
Researchers are actively pursuing higher-order topological superconductivity as a pathway to creating stable and manipulable Majorana networks, circumventing the limitations of vortex-based approaches. Yongting Shi from the Institute of Applied Physics and Computational Mathematics, Qing Wang from the Anhui Provincial Key Laboratory of Low-Energy Quantum Materials and Devices, and Zhen-Guo Fu et al. demonstrate a symmetry-protected realisation of this phenomenon within a MnXPb (X=Se, Te)-Pb heterostructure. Their work reveals that the unique boundary properties of antiferromagnetic topological insulators naturally give rise to Majorana corner modes at the interfaces between superconducting and magnetic regions. Combining first-principles calculations with theoretical modelling, the team show robust corner localisation and, crucially, the potential for purely electrical control over Majorana fusion and braiding in a two-dimensional triangular geometry, establishing MnXPb as a promising platform for future quantum computation. This breakthrough centers on manipulating Majorana zero modes, exotic quasiparticles considered prime candidates for building stable and scalable quantum bits. Unlike existing approaches that often rely on complex structures involving vortices or magnetic fields, this work demonstrates a route to engineer Majorana modes localized at the corners of two-dimensional materials, offering a simpler and more controllable architecture. Researchers propose utilizing heterostructures composed of antiferromagnetic topological insulators, specifically, monolayer MnXPb2 (where X represents selenium or tellurium) combined with lead, to achieve this unique state. The core of this discovery lies in the intrinsic boundary properties of these antiferromagnetic topological insulators. These materials exhibit a dichotomy at their edges, possessing gapless Dirac states protected by time-reversal symmetry on antiferromagnetic boundaries and magnetic gaps on ferromagn
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quantum-computingHybrid Light-Matter Particles Unlock Potential for Terahertz Quantum Technology
Scientists have predicted the emergence of ferron-polaritons, novel quasiparticles formed by the interaction of ferroelectric excitations and light, within superconductor/ferroelectric/superconductor heterostructures. M. Nursagatov, Xiyin Ye, and G. A. Bobkov, alongside Tao Yu and I. V. Bobkova, demonstrate that this coupling not only provides direct evidence for the existence of ferrons but also achieves an ultrastrong-coupling regime with a terahertz-range spectral gap. This gap is significantly larger than observed in magnetic systems, highlighting the potent nature of electric dipole interactions. Their work establishes these heterostructures as a promising new platform for investigating extreme light-matter coupling and potentially enabling the development of rapid, terahertz-frequency quantum technologies based on ferroelectric materials. Ultrastrong coupling between ferroelectric ferrons and superconducting photons Scientists have predicted the formation of ferron-polaritons within superconductor/ferroelectric/superconductor heterostructures, representing a novel hybrid quasiparticle arising from the interaction between collective ferroelectric excitations, termed ferrons, and Swihart photons. This coupling provides direct evidence for the existence of ferrons and reaches the ultrastrong-coupling regime, characterised by a spectral gap in the terahertz range, significantly exceeding that of magnetic counterparts due to the inherent strength of electric dipole interactions. The research establishes these heterostructures as a promising platform for investigating extreme light-matter coupling and developing high-speed, terahertz-frequency quantum technologies based on ferroelectric materials. This work demonstrates that the ferron mode, polarized normal to the film interfaces within the heterostructure, couples to the Swihart photon mode of the superconducting resonator, ultimately forming ferron-polaritons. This interaction, a direct consequence of the ferroel
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quantum-computingFaster Quantum Simulations Unlock New Materials and Drug Discoveries
Scientists tackling the simulation of quantum many-body systems face a persistent challenge due to the exponential growth of computational complexity. Belal Abouraya from the German University in Cairo, Jirawat Saiphet from the University of Tübingen, and Fedor Jelezko et al. present a new method to improve the efficiency of matrix product states (MPS), a key technique for modelling one-dimensional quantum systems. Their research introduces a streamlined approach to simulating time-dependent Hamiltonians, achieving second-order convergence, a significant improvement over standard first-order methods. Demonstrating this advancement with simulations of nitrogen-vacancy colour centres in diamond, the team shows a reduction in average error by a factor of approximately 1000, potentially enabling more accurate and scalable modelling for future quantum technologies. High-order quadrature improves time-dependent quantum many-body simulations Researchers have developed a new method for simulating the complex behaviour of quantum many-body systems, addressing a long-standing challenge in physics and quantum information science. These systems are notoriously difficult to model due to the exponential growth in computational requirements as the system size increases. This work introduces an efficient augmentation to existing matrix product state (MPS) algorithms, enabling more accurate and faster simulations of time-dependent quantum dynamics. The proposed technique achieves second-order convergence, a significant improvement over the first-order convergence of standard methods currently employed. The breakthrough centres on replacing the instantaneous Hamiltonian used in conventional MPS time-evolution solvers with a carefully calculated average Hamiltonian derived from a high-order quadrature rule. This approach, inspired by classical numerical integration techniques like Simpson’s rule, allows for a more precise approximation of the time evolution operator over short time in
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quantum-computingReservoir computing on an analog Rydberg-atom quantum computer
This post shows how quantum reservoir computing (QRC) can tackle machine learning challenges using Rydberg-atom quantum computers. Readers will learn how QRC works, see its performance on image classification and time series prediction tasks, and understand when it outperforms classical methods—particularly for small datasets in pharmaceutical research. After decades of progress, machine learning (ML) has become a foundation of modern technology, powering applications in areas such as computer vision, financial market prediction, and natural language processing. Despite these advances, ML still struggles with problems of growing scale and complexity. To overcome these limitations, there has been growing interest in exploiting the principles of quantum mechanics to design quantum machine learning (QML) algorithms to tackle ML problems such as image classification or those involving quantum data [1]. Among the many QML approaches proposed, quantum reservoir computing (QRC) has emerged as a promising approach for implementation on near-term quantum hardware [2-6].
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quantum-computingQuantum Routing Cuts Network Delays Even with Two Link Failures Simultaneously
Researchers are increasingly focused on optimising network resilience against simultaneous link failures, a critical challenge for modern telecommunications infrastructure. This work presents a novel approach to latency-resilient Layer 3 routing, formulated as a graph-based optimisation problem and adapted for solution using quantum computing. Led by Maher Harb, Nader Foroughi, and Matt Stehman of Comcast Corporation, alongside Nati Erez and Erik Garcell of Classiq Technologies et al., this study demonstrates the potential of the quantum approximate optimisation algorithm to minimise latency and maximise network robustness under dual-link failure scenarios. Significantly, the findings validate the proposed mathematical formulation by achieving optimal network designs on both quantum simulators and hardware, paving the way for future quantum solutions to complex network optimisation problems. Scientists address the latency-resilient Layer 3 routing optimisation problem in telecommunications networks with predefined Layer 1 optical links. The research formulates this problem as a graph-based optimisation problem with the objective of minimising latency, creating vertex-disjoint paths from each site to the internet backbone, and maximising overall resiliency by limiting the impact of dual-link failures. By framing the problem as finding two disjoint shortest paths, coupled together with a resiliency component to the objective function, they establish a single formulation to produce optimal path design. The mathematical formulation was adapted to solve the problem. QAOA performance evaluation using uncorrelated and correlated link failure scenarios requires careful consideration of network topology Scientists are employing quantum approximate optimization algorithm (QAOA) executed over both quantum simulator and quantum hardware. QAOA was tested on a toy graph topology with 5 vertices and 7 edges and considering two limiting scenarios respectively representing independe
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quantum-computingQuantum Dots Bring Scalable, Entangled Light Sources Closer to Reality
Researchers are increasingly focused on semiconductor quantum dots as a means of creating entangled photon pairs, a crucial component for scalable photonic technologies. Xingling Pan, Zhiming Chen, and Yingtao Ding, all from the School of Integrated Circuits and Electronics at Beijing Institute of Technology, alongside Weibo Gao from Nanyang Technological University, Fei Ding from the University of Southern Denmark, and Zhaogang Dong et al. from Singapore University of Technology and Design, present a comprehensive review of recent advances in this field. Their work is significant because it details the progression from utilising biexciton-exciton cascades to exploring spontaneous two-photon emission, demonstrating how innovative nanophotonic designs and control techniques are pushing the boundaries of brightness, coherence and entanglement fidelity in these solid-state sources. This review clarifies the remaining hurdles and outlines potential pathways for integrating these quantum dots into real-world applications spanning communication and computation. Recent progress in quantum dot entangled photon sources Semiconductor quantum dots (QDs) have emerged as a leading solid-state platform for generating nonclassical light, providing a viable route towards scalable photonic systems. While single-photon emission from QDs is now well-established, achieving high-fidelity entangled photon-pair sources remains a significant challenge and an area of rapid development. This work surveys recent advances in QD-based entangled photon sources, charting the evolution from the conventional biexciton-exciton cascade to the promising new approach of spontaneous two-photon emission. Researchers have meticulously examined how improvements in nanophotonic architectures and coherent control strategies are redefining performance limits, simultaneously enhancing source brightness, coherence, and entanglement fidelity. The study highlights two principal routes to entangled photon-pair gen
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quantum-computingQuantum Device Generates Perfect Coin Flips and Unhackable Random Numbers
Researchers have demonstrated a functional quantum Bernoulli factory, a device capable of transforming initial randomness into enhanced, classically unattainable forms. Tanay Roy, affiliated with both Fermi National Accelerator Laboratory and the Superconducting Quantum Materials and Systems (SQMS) Center, alongside Tanay Roy et al., achieved this by implementing an entanglement-assisted Bernoulli factory using Bell-basis measurements on superconducting hardware. This work is significant because it experimentally realises the classically inconstructible Bernoulli doubling primitive, alongside an exact fair coin and another inconstructible function, all without relying on external classical randomness. The findings establish a resource-efficient experimental primitive for randomness processing and bolster the potential of Bernoulli factories for advanced stochastic simulation and sampling. Realising classically inconstructible randomness functions via superconducting qubits Researchers have demonstrated a novel quantum-to-classical randomness-processing primitive utilising Bell-basis measurements on two identical input quoins prepared on superconducting hardware. This work establishes a resource-efficient method for transforming a biased source of randomness into new coins with specifically tailored biases, achieving results impossible or highly inefficient with purely classical approaches. The study experimentally realises the classically inconstructible Bernoulli doubling function, f(p) = 2p, without relying on any external classical randomness source. Simultaneously, the same Bell-measurement statistics generate an exact fair coin, f(p) = 1/2, and another classically inconstructible function, f(p) = 4p(1 −p), as intermediate outputs. Benchmarking the measured output biases against ideal predictions confirms the constant average quoin cost for generating both the fair coin and the function f(p), requiring two and four quoins respectively, irrespective of the input
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quantum-computingQuantum Material’s Dynamics Remotely Controlled, Bridging Simulator and Computer Worlds
Scientists are increasingly focused on harnessing quantum mechanics to design and control novel materials. Andrei Vrajitoarea from New York University, Gabrielle Roberts from the University of Chicago, and Kaden R. A. Hazzard from Rice University, alongside Jonathan Simon and David I. Schuster et al., demonstrate a significant step towards this goal by merging the strengths of analog quantum simulators and digital quantum computers. Their research details the embedding of digital control within the analog evolution of a synthetic quantum material, specifically a Bose-Hubbard circuit, allowing for Hamiltonian-level control and the creation of previously inaccessible strongly-correlated states. This hybrid approach not only guides the system into novel phases of matter, exhibiting both solid and fluid characteristics, but also enhances coherence through many-body echo techniques, ultimately illustrating a pathway for improved sensing and materials characterisation via entanglement between quantum computers and quantum matter. Entangling Bose-Hubbard circuits and qubits for hybrid quantum control Scientists have demonstrated a quantum-controlled synthetic material by entangling a Bose-Hubbard circuit with an ancilla qubit. This breakthrough merges the strengths of analog quantum simulators and digital quantum computers, paving the way for new capabilities in state preparation, characterization, and dynamical control of many-body systems. The research details a novel approach to Hamiltonian-level control, where the lattice potential landscape of a Bose-Hubbard circuit is directly manipulated through entanglement with a single qubit. This allows for dynamics under a superposition of different lattice configurations, guiding the system towards previously inaccessible strongly-correlated states. Specifically, the work successfully orders photons into superpositions of solid and fluid eigenstates, effectively creating a “photonic transistor” where quantum logic is embedded
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quantum-computingQuantum Simulations Boosted by Technique Correcting Atomic ‘jitter’ at the Nanoscale
Nuclear quantum effects (NQEs) significantly influence the behaviour of many atomistic systems, yet accurately modelling them within molecular simulations presents a considerable hurdle. Ádám Madarász, Bence Balázs Mészáros, and János Daru, all from Eötvös Loránd University, present a new post-processing framework, path-integral generalized smoothed trajectory analysis (PIGSTA), designed to systematically incorporate these effects into both classical and path-integral molecular dynamics simulations. This research is significant because PIGSTA efficiently corrects for discretization errors arising from finite bead numbers in path-integral simulations, improving convergence and offering a reference-free method to assess simulation accuracy. By enabling physically consistent results with fewer computational resources, PIGSTA provides a practical and broadly applicable approach to account for NQEs in a wide range of atomistic simulations. This post-processing technique systematically improves the convergence of simulations, addressing a long-standing challenge in accurately modelling complex systems at the atomic level. PIGSTA applies analytically defined convolution kernels to existing simulation trajectories, correcting for discretization errors caused by a finite number of ‘beads’ used to represent quantum particles without altering the underlying dynamics. For harmonic systems, PIGSTA achieves the exact quantum-mechanical limit regardless of the bead number, a significant improvement over standard path-integral molecular dynamics which requires an infinite number of beads for exactness. The research introduces a method to enhance the accuracy of thermodynamic and structural properties calculated from simulations at reduced bead numbers. PIGSTA also provides an internal diagnostic tool to assess whether a simulation has adequately converged, eliminating the need for costly reference calculations or arbitrarily increasing the number of beads. Researchers validated PIG
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quantum-computingNew Superconductor Design Unlocks Potential for Vastly More Powerful Quantum Computers
Researchers have discovered a novel topological superconductor capable of encoding and manipulating quantum information using robust, easily controlled particles called qutrits, potentially advancing the development of fault-tolerant quantum computers.
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quantum-computingTrotter error and gate complexity of the SYK and sparse SYK models
AbstractThe Sachdev–Ye–Kitaev (SYK) model is a prominent model of strongly interacting fermions that serves as a toy model of quantum gravity and black hole physics. In this work, we study the Trotter error and gate complexity of the quantum simulation of the SYK model using Lie–Trotter–Suzuki formulas. Building on recent results by Chen and Brandão [6] — in particular their uniform smoothing technique for random matrix polynomials — we derive bounds on the first- and higher-order Trotter error of the SYK model, and subsequently find near-optimal gate complexities for simulating these models using Lie–Trotter–Suzuki formulas. For the $k$-local SYK model on $n$ Majorana fermions, at time $t$, our gate complexity estimates for the first-order Lie–Trotter–Suzuki formula scales with $\tilde{\mathcal{O}}(n^{k+\frac{5}{2}}t^2)$ for even $k$ and $\tilde{\mathcal{O}}(n^{k+3}t^2)$ for odd $k$, and the gate complexity of simulations using higher-order formulas scales with $\tilde{\mathcal{O}}(n^{k+\frac{1}{2}}t)$ for even $k$ and $\tilde{\mathcal{O}}(n^{k+1}t)$ for odd $k$. Given that the SYK model has $\Theta(n^k)$ terms, these estimates are close to optimal. These gate complexities can be further improved upon in the context of simulating the time evolution of an arbitrary fixed input state $|\psi\rangle$, leading to a $\mathcal{O}(n^2)$-reduction in gate complexity for first-order formulas and $\mathcal{O}(\sqrt{n})$-reduction for higher-order formulas. We also apply our techniques to the sparse SYK model, which is a simplified variant of the SYK model obtained by deleting all but a $\Theta(n)$ fraction of the terms in a uniformly i.i.d. manner. We find the average (over the random term removal) gate complexity for simulating this model using higher-order formulas scales with $\tilde{\mathcal{O}}(n^{1+\frac{1}{2}} t)$ for even $k$ and $\tilde{\mathcal{O}}(n^{2} t)$ for odd $k$. Similar to the full SYK model, we obtain a $\mathcal{O}(\sqrt{n})$-reduction simulating the time
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