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-computingWhat is next in quantum advantage?
We are now at an exciting point in our process of developing quantum computers and understanding their computational power: It has been demonstrated that quantum computers can outperform classical ones (if you buy my argument from Parts 1 and 2 of this mini series). And it has been demonstrated that quantum fault-tolerance is possible for at least a few logical qubits. Together, these form the elementary building blocks of useful quantum computing. And yet: the devices we have seen so far are still nowhere near being useful for any advantageous application in, say, condensed-matter physics or quantum chemistry, which is where the promise of quantum computers lies. So what is next in quantum advantage? This is what this third and last part of my mini-series on the question “Has quantum advantage been achieved?” is about. The 100 logical qubits regimeI want to have in mind the regime in which we have 100 well-functioning logical qubits, so 100 qubits on which we can run maybe 100 000 gates. Building devices operating in this regime will require thousand(s) of physical qubits and is therefore well beyond the proof-of-principle quantum advantage and fault-tolerance experiments that have been done. At the same time, it is (so far) still one or more orders of magnitude away from any of the first applications such as simulating, say, the Fermi-Hubbard model or breaking cryptography. In other words, it is a qualitatively different regime from the early fault-tolerant computations we can do now. And yet, there is not a clear picture for what we can and should do with such devices. The next milestone: classically verifiable quantum advantage In this post, I want to argue that a key milestone we should aim for in the 100 logical qubit regime is classically verifiable quantum advantage. Achieving this will not only require the jump in quantum device capabilities but also finding advantage schemes that allow for classical verification using these limited resources. Why is it an
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quantum-computingQuantum Computing (QUBT) Is Up 6.5% After Luminar Deal, NASA Wins And First Bank Cyber Sale - What's Changed - simplywall.st
In recent months, Quantum Computing Inc. (QCi) raised US$1.25 billion in cash, closed a US$110.0 million acquisition of Luminar Semiconductor, secured contracts with NASA and NIST, completed a quantum photonic chip foundry in Tempe, Arizona, and announced it would report Q4 and full-year 2025 results on March 2, 2026. These moves, alongside the company’s first U.S. commercial sale of quantum cybersecurity solutions to a top-five bank and growing Department of Defense work, highlight QCi’s effort to turn its photonics-focused platform and expanded manufacturing base into broader commercial adoption. We’ll now examine how QCi’s Luminar acquisition and new government and financial-sector contracts may influence its existing investment narrative. AI is about to change healthcare. These 27 stocks are working on everything from early diagnostics to drug discovery. The best part - they are all under $10b in market cap - there's still time to get in early.AdvertisementQuantum Computing Investment Narrative RecapTo own Quantum Computing Inc. today, you need to believe its photonics-first approach and new cash, fabs and contracts can evolve from small, lumpy R&D work into scalable products. The key near term catalyst is whether upcoming Q4 and full year 2025 results, plus early customer traction, show progress toward that shift. The biggest risk remains a cost base that is ramping ahead of proven demand and could widen losses if revenue does not build meaningfully. Among the recent announcements, QCi’s first U.S. commercial sale of quantum cybersecurity solutions to a top five bank stands out. It directly connects the company’s photonic and quantum roadmap to a real paying enterprise customer, and gives a reference point for future financial sector wins. How much this sale, along with NASA and NIST contracts, translates into repeatable revenue is likely to be a focus around the March 2, 2026 earnings call. Yet behind the new contracts and foundry build out, inve
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quantum-computingResearchers may have observed triplet superconductivity – the holy grail in quantum computing
The new work focuses on Nb0.18Re0.82, often shortened to NbRe, a noncentrosymmetric superconductor whose crystal structure lacks inversion symmetry. That structural feature can produce antisymmetric spin-orbit coupling. When strong enough, this allows a mixture of singlet and triplet components in the superconducting order parameter. submitted by /u/Brighter-Side-News [link] [comments]
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quantum-computingFrench Quantum Computing Companies 2026
Top French Quantum Computing Companies 2026 | Quantum Zeitgeist France has built one of the most deliberately structured quantum ecosystems in the world. The €1.8 billion Plan Quantique, launched in 2021, seeded five hardware champions through the Ministry of Armed Forces PROQCIMA programme — a competitive 10-year race to deliver a universal fault-tolerant quantum computer with 128 logical qubits by 2030 and 2,048 logical qubits by 2035. Beyond hardware, France hosts dedicated quantum networking, pharmaceutical applications and post-quantum cryptography companies, supported by Bpifrance, Quantonation and strategic partnerships with STMicroelectronics, CEA and the EuroHPC Joint Undertaking. For the full global picture, the Quantum Navigator tracks over 940 companies across 47 countries and 124 categories. Quantum Hardware Expand All Alice & Bob Cat qubits · Paris, France Cat Qubits €104M Series B Alice & Bob was founded in 2020 in Paris to develop fault-tolerant quantum computers using cat qubits — a type of superconducting qubit in which the encoding suppresses bit-flip errors, reducing the physical qubit overhead required for error correction by up to 200-fold compared to conventional approaches. The cat qubit architecture was independently adopted by Amazon for its own quantum research, validating the approach. In January 2025, Alice & Bob raised a €104 million Series B led by existing investors, with Bpifrance participating through France’s Deeptech 2030 Fund and Defense Innovation Fund. The company joined NVIDIA’s NVQLink programme in October 2025, integrating its QPUs with NVIDIA GPUs for real-time fault-tolerant hybrid computing. In the same month, Alice & Bob acquired the SQUID-6 UHV deposition system from PLASSYS-BESTEK, backed by France’s Defense Innovation Agency (AID) under the ULTRACAT project, marking a step toward industrial-scale cat qubit chip fabrication. The company launched a plan to hire 100 new employees by mid-2026, targeting a
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quantum-computingQuantum Computers Are About to Change Everything: 5 Strategic Industries Already Betting on Them - Futura, Le média qui explore le monde
Home Quantum Computers Are About to Change Everything: 5 Strategic Industries Already Betting on Them Quantum Computers Are About to Change Everything: 5 Strategic Industries Already Betting on Them Category : Technology February 27, 2026 4 min Several industrial sectors are beginning to take advantage of quantum computing. © XD with ChatGPT Share facebook twitter linkedin newsletter Arnaud Pagès Journalist Xavier Demeersman Journalist For decades, quantum computing was confined to research labs, the preserve of physicists and mathematicians working at the very edge of theory. Today, it is edging into a new phase – one of pre industrial development – where concrete business applications are beginning to emerge. Thanks to its ability to process problems of staggering complexity, far beyond the reach of traditional machines, the quantum computer promises to reshape entire sectors of the global economy. Large scale use may still be limited, but several industries are already positioning themselves at the front of the pack. Here are five that are moving faster than the rest. Chemistry and pharmaceuticals: speeding up drug discovery In the pharmaceutical world, one major obstacle stands in the way of innovation: computing power. Accurately describing the interactions between atoms and electrons demands an astronomical number of calculations. Even the most advanced supercomputers struggle to handle this efficiently. Quantum simulation changes the game. By rapidly modelling complex proteins and chemical reactions, it can help researchers design a therapeutic molecule in a matter of days rather than months – and at significantly lower development cost. Roche is already working with Google Quantum AI to explore these possibilities. The partnership aims to prepare practical applications as the technology matures. For an industry where time quite literally saves lives, that acceleration could prove transformative. Finance: redefining risk management If there is one sector buil
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quantum-computingQuantum Computing – a game-changer for insurance? - Retail Banker International
Stories of Quantum Computing revolutionising modelling are everywhere, and the parallels from quantum to Monte Carlo seem tempting. But how will stochastic modelling work, and how close are we to having working models? Is this the technology to place your bets on? What is it? Quantum computing is a new field of computer science that uses quantum mechanics to solve complex problems. Classical computers use “bits” that take a binary state – 0 or 1 – to represent information. Think of them as similar to a coin at rest that can be thrown to result in either heads or tails landing upwards. By contrast, quantum computers use quantum bits (qubits) that can exist in multiple states simultaneously, like a spinning coin. Qubits can also be entangled, meaning the state of one can directly influence the state of another This enables the development of a whole new set of algorithms beyond the capabilities or performance of traditional computers. However, the compute step time of quantum computers is in the order of milli- or micro-seconds compared to the nanoseconds of traditional computing. With parallelisation of the more plentiful older technology, a classical system can be seven to eight orders of magnitude faster than a quantum step. Are they real? Prototype quantum computers have been built, and these are accessible over cloud platforms. However, while these computers are theoretically capable of delivering, they are hindered by high noise levels (random errors), making superior use of them unattainable. To address this issue, quantum computers often require several magnitudes more physical qubits than the logical qubits presented to the user. The algorithms can be built and tested on traditional computers too. There are software development kits that enable quantum programming and quantum simulators that simulate quantum computers on high-end traditional computers. GlobalData Strategic Intelligence US Tariffs are shifting - will you react or anticipate? Don’t let policy c
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quantum-computingAnalysis: Quantum Medicine’s Promise Raises New Privacy And Governance Risks
Insider Brief Quantum technology could compress drug discovery times and sharpen diagnostics, but without new governance standards it may also erode medical privacy and widen inequality, according to a new analysis in Bill of Health, the blog of the Petrie-Flom Center at Harvard Law School. Mauritz Kop, founder of Stanford Responsible Quantum Technology and a […]
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quantum-computingLockheed Martin Joins Xanadu in Advancing Foundational Quantum Machine Learning Theory
Lockheed Martin is collaborating with Xanadu to further the foundational theory and practical applications of Quantum Machine Learning (QML). The global defense and technology company will work alongside Xanadu, a leader in quantum computing software and hardware, to explore how quantum computers can enhance generative models—machine learning techniques used in areas like artificial intelligence. This research will specifically focus on leveraging Fourier-based operations inaccessible to classical methods, potentially unlocking advancements in defense, finance, and pharmaceutical experiment design. “This work is about rethinking the foundations of how quantum computers can learn,” said Christian Weedbrook, Founder and CEO of Xanadu. “By revisiting core quantum primitives, we hope to uncover entirely new ways of representing and processing data.” Xanadu and Lockheed Martin Advance Quantum Machine Learning Theory Quantum machine learning research received a boost as Xanadu and Lockheed Martin initiated a collaborative effort to fundamentally rethink how quantum computers learn, focusing on generative models—techniques crucial to modern artificial intelligence but often hampered by data scarcity and energy demands. Lockheed Martin’s involvement stems from its active exploration of quantum technologies with the potential to reshape computation and sensing capabilities, with this collaboration designed to expand understanding of how future quantum systems might bolster national security and technological advancement. Founded in 2016, Xanadu also leads the development of PennyLane, an open-source software library for quantum computing and application development. Fourier-Based Operations Expand Generative Model Potential Generative models, the engine behind many current artificial intelligence advancements including large language models, often require substantial data and energy, and falter when data is limited—a challenge that Xanadu and Lockheed Martin are addressing t
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quantum-computingQuantum simulation of massive Thirring and Gross--Neveu models for arbitrary number of flavors
--> Quantum Physics arXiv:2602.22313 (quant-ph) [Submitted on 25 Feb 2026] Title:Quantum simulation of massive Thirring and Gross--Neveu models for arbitrary number of flavors Authors:Bojko N. Bakalov, Joao C. Getelina, Raghav G. Jha, Alexander F. Kemper, Yuan Liu View a PDF of the paper titled Quantum simulation of massive Thirring and Gross--Neveu models for arbitrary number of flavors, by Bojko N. Bakalov and 4 other authors View PDF HTML (experimental) Abstract:The study of fermionic quantum field theories is an important problem for realizing the standard model of particle physics on a quantum computer. As a step towards this goal, we consider the massive Thirring and Gross--Neveu models with arbitrary number of fermion flavors, $N_f$, discretized on a spatial one-dimensional lattice of size $L$ in the Hamiltonian formulation. We compute the gate complexity using the higher-order product formula and using block-encoding/qubitization and quantum singular value transformations in the limit of large $N_f$ and $L$. We also prepare the ground states of both models with excellent fidelity for system sizes up to 20 qubits with $N_f = 1,2,3,4$ using the adaptive-variational quantum imaginary time algorithm. In addition, we also classify the dynamical Lie algebras of these relativistic fermionic models and show that they belong to the same isomorphism class. Our work is a concrete step towards the quantum simulation of real-time dynamics of large $N_f$ fermionic quantum field theories models relevant for chiral symmetry breaking, understanding dimensional transmutation, and exploring the conformal window of field theories on near-term and early fault-tolerant quantum computers. Comments: Subjects: Quantum Physics (quant-ph); High Energy Physics - Lattice (hep-lat); High Energy Physics - Theory (hep-th) Cite as: arXiv:2602.22313 [quant-ph] (or arXiv:2602.22313v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.22313 Focus to learn more arXiv
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quantum-computingLong-Range Interactions Aid Superconductivity Modelling
Scientists are developing improved methods for modelling unconventional superconductivity, a phenomenon with potential applications in advanced technologies. Andreas A. Buchheit from Saarland University, Torsten Keßler from Eindhoven University of Technology, and Sergej Rjasanow from Saarland University, in a collaborative effort between these institutions, present a numerical solution to the Bardeen-Cooper-Schrieffer equation, accounting for long-range electron interactions within a tight-binding model. Their work addresses a challenging nonlinear equation governing the superconducting gap, utilising an efficient Galerkin method with B-splines to navigate the complexities arising from power-law singularities. This research is significant because it provides a robust computational framework for understanding and predicting the behaviour of unconventional superconductors, potentially accelerating the development of novel materials with enhanced properties. Imagine building a perfectly frictionless circuit, where electrical current flows without any loss of energy. Understanding the complex behaviour of electrons in certain materials, described by the Bardeen-Cooper-Schrieffer equation, brings us closer to realising such a feat. This work details a new, efficient method for solving that equation, accounting for long-range interactions between electrons on a two-dimensional lattice. Scientists are increasingly focused on understanding superconductivity, a quantum phenomenon with applications spanning energy transmission to quantum computing. After its initial discovery in 1911, the Bardeen-Cooper-Schrieffer (BCS) theory in 1957 provided a microscopic explanation, positing that an attractive force between electrons forms Cooper pairs. Central to this theory is the superconducting gap, a measure of the energy needed to break these pairs, which solving the nonlinear BCS gap equation determines. Recent research concentrates on unconventional superconductors, materials exhi
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quantum-computingLockheed Martin, Xanadu launch quantum AI push for defense edge - Interesting Engineering
Xanadu's public cloud-deployed computer (representational image)Xanadu Quantum computing firm Xanadu has launched a new research initiative with defense giant Lockheed Martin to push the boundaries of Quantum Machine Learning, or QML. The partnership will explore whether quantum systems can power new kinds of generative models that outperform classical AI in data-scarce environments. Both companies say the work could eventually influence defense, finance, and pharmaceutical research. The focus sits at the intersection of quantum theory and advanced machine learning. Rethinking quantum learning foundations The collaboration centers on generative models. These systems learn patterns in data and produce new, realistic outputs. Large language models and image generators rely on similar techniques today. However, classical generative models demand massive datasets and significant computing power. They also consume large amounts of energy. That limits their use in environments where data remains scarce or tightly controlled. Xanadu and Lockheed Martin want to test whether quantum computers can approach the problem differently. Their research will examine Fourier-based and quantum-native operations that classical systems cannot replicate. “This work is about rethinking the foundations of how quantum computers can learn,” said Christian Weedbrook, Founder and CEO of Xanadu. “By revisiting core quantum primitives, we hope to uncover entirely new ways of representing and processing data.” He added that Lockheed Martin’s experience strengthens the effort. “Lockheed Martin brings deep domain expertise that makes them an ideal teammate for this exploration. We’re thrilled to explore these ideas together and contribute to the evolving theory of quantum machine learning.” Quantum-native operations may allow models to represent information in higher-dimensional spaces. That could reduce data requirements. It may also enable new forms of pattern discovery beyond classical limits. Lo
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quantum-computingQuantum Simulation Reveals Curved Spacetime on 80 Qubits
Scientists are increasingly exploring the intersection of quantum mechanics and general relativity, and a new study details the observation of quantum many-body dynamics within emergent curved spacetime. Brendan Rhyno, Bastien Lapierre, and Smitha Vishveshwara from the Department of Physics at the University of Illinois at Urbana-Champaign, working with Khadijeh Najafi from IBM Quantum and IBM T.J. Watson Research Center, and Ramasubramanian Chitra from the Institute for Theoretical Physics at ETH Zurich, demonstrate this phenomenon using 80 superconducting qubits on Heron processors. This research, a collaborative effort between institutions in the United States and Switzerland, is significant because it establishes large-scale digital processors as a platform for investigating many-body dynamics in synthetic, tunable curved spacetimes, allowing observation of effects like curved light-cone propagation and horizon-induced freezing. Researchers have demonstrated a novel way to model complex physical phenomena using quantum computers. The work creates artificial versions of warped spacetime, similar to that around black holes, within a programmable processor. This advance promises to unlock new insights into areas ranging from cosmology to condensed matter physics. Researchers are now using quantum processors to model the behaviour of matter in curved spacetime, a fundamental aspect of gravity and cosmology. This work demonstrates how 80 superconducting qubits on IBM Heron processors can digitally simulate complex quantum dynamics within artificially created curved spaces. By carefully engineering the interactions between these qubits, the team realised excitations that behave as if moving along curved paths dictated by an effective metric. Following a disturbance to the system, a ‘quench’, they observed phenomena mirroring those predicted by theories of curved spacetime, including distorted light-cone propagation and a freezing of magnetization near simulated horizo
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quantum-computingAI Designs Better Drug Candidates with Quantum Aid
Researchers are tackling the challenge of generating high-quality, drug-like molecules using deep generative models, a technology poised to accelerate pharmaceutical research and development. Hayato Kunugi, Yosuke Iyama, and Yutaro Hirono from Innovation to Implementation Laboratories, Central Pharmaceutical Research Institute, Japan Tobacco Inc., working in collaboration with Mohsen Rahmani, Matthew Woolway, Vladimir Vargas-Calderón, William Kim, Kevin Chern, and Mohammad Amin at D-Wave Systems Inc., present a novel framework integrating deep generative models with quantum annealing computation. Their approach utilises a newly developed Neural Hash Function (NHF) for both regularisation and binarisation, bridging continuous and discrete signals between classical and neural networks within the objective function. Significantly, compounds generated by this method demonstrate improved validity and drug-likeness compared to those from traditional models, even surpassing the characteristics of the original training data without specific optimisation constraints. These findings suggest a substantial advancement in stochastic generator design, enhancing feature space sampling and extraction for effective drug discovery. Scientists are pioneering a fresh strategy to accelerate the creation of new medicines. The vastness of potential drug candidates, estimated at 10⁶⁰ molecules, makes discovery intensely challenging. This work offers a pathway to navigate that complexity, designing more effective compounds and shortening the path to market. Scientists are pioneering a new method to accelerate the design of drug candidates using deep generative modelling and quantum computing. This work addresses a longstanding challenge in pharmaceutical research: the difficulty of efficiently searching the vast landscape of potential molecules for those with desirable properties. Existing molecular generative models often struggle to produce a sufficient number of compounds resembling effe
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quantum-computingXanadu and Lockheed Martin Launch Joint Research Initiative for Quantum Machine Learning
Xanadu and Lockheed Martin Launch Joint Research Initiative for Quantum Machine Learning Xanadu and Lockheed Martin have established a research collaboration to study the foundational theory of Quantum Machine Learning (QML), with a specific focus on generative modeling. The initiative aims to investigate how quantum-photonic hardware can implement Fourier-based operations that are computationally prohibitive for classical architectures. Unlike classical generative AI, which relies on high-volume data sets and extensive energy consumption, this research targets the development of quantum primitives that can function effectively in low-data environments typical of defense and pharmaceutical modeling. The technical objective centers on leveraging the intrinsic properties of quantum states to perform data representation and processing in ways that classical kernels cannot replicate. By utilizing the mathematical structure of quantum circuits—specifically those optimized for photonic systems—the teams intend to uncover new methods for complex experimental design. This includes exploring the expressivity of quantum circuits when mapping input data to high-dimensional Hilbert spaces, potentially offering a representational advantage for modeling systems with limited empirical observations. This initiative builds upon Xanadu’s existing software ecosystem, particularly the PennyLane library, to bridge the gap between abstract quantum theory and industrial application. Lockheed Martin provides the domain-specific constraints necessary to test these QML models against challenges in national security and advanced sensing. The partnership is structured to produce peer-reviewed research that validates the utility of quantum generative models, focusing on long-term scalability and the reduction of computational overhead in mission-critical simulations. For further technical details on the joint initiative and research focus, consult the official announcement here. February 26, 20
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quantum-computingRigetti vs. IonQ: Which Quantum Computing Stock Has More Upside? - TradingView
Quantum computing is no longer a sci-fi concept; it’s fast becoming one of the most closely watched frontiers in tech investing. Per a report by Grand View Research, the global quantum computing market was valued at $1.42 billion in 2024 and is projected to reach $4.24 billion by 2030, expanding at a robust 20.5% CAGR from 2025 to 2030. While the market is still early-stage, the growth trajectory signals rising enterprise adoption, increasing government funding and intensifying competition among pure-play innovators. For investors willing to stomach volatility, quantum computing represents a high-risk, high-reward theme that could reshape industries ranging from cybersecurity to drug discovery, making it a space worth keeping firmly on the radar.
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quantum-computingRead-out of Majorana qubits reveals their hidden nature - Physics World
Connect the dots: The researchers created a system known as a minimal Kitaev chain using two quantum dots coupled through a superconducting segment. (Courtesy: QuTech) Quantum computers could solve problems that are out of reach for today’s classical machines. However, the quantum states they rely on are prone to decohering – that is, losing their quantum information due to local noise. One possible way around this is to use quantum bits (qubits) constructed from quasiparticle states known as Majorana zero modes (MZMs) that are protected from this noise. But there’s a catch. To perform computations, you need to be able to measure, or read out, the states of your qubits. How do you do that in a system that is inherently protected from its environment? Scientists at QuTech in the Netherlands, together with researchers from the Madrid Institute of Materials Science (ICMM) in Spain, say they may have found an answer. By measuring a property known as quantum capacitance, they report that they have read out the parity of their MZM system, backing up an earlier readout demonstration from a team at Microsoft Quantum Hardware on a different Majorana platform. Measuring parity The QuTech/ICMM researchers generated their MZMs across two quantum dots – semiconductor structures that can confine electrons – connected by a superconducting nanowire. Electrons can transfer, or tunnel, between the quantum dots through this wire. Majorana-based qubits store their quantum information across these separated MZMs, with both elements in the pair required to encode a single “parity” bit. A pair of parity bits (combining four MZMs in total) forms a qubit. A parity bit has two possible states. When the two quantum dots are in a superposition of both having one electron and both having none, the system is said to have even parity (a “0”). When the system is instead in superposition of only one of the quantum dots having an electron, the parity is said to be odd (a “1”).
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quantum-computingRead-out of Majorana qubits reveals their hidden nature
Connect the dots: The researchers created a system known as a minimal Kitaev chain using two quantum dots coupled through a superconducting segment. (Courtesy: QuTech) Quantum computers could solve problems that are out of reach for today’s classical machines. However, the quantum states they rely on are prone to decohering – that is, losing their quantum information due to local noise. One possible way around this is to use quantum bits (qubits) constructed from quasiparticle states known as Majorana zero modes (MZMs) that are protected from this noise. But there’s a catch. To perform computations, you need to be able to measure, or read out, the states of your qubits. How do you do that in a system that is inherently protected from its environment? Scientists at QuTech in the Netherlands, together with researchers from the Madrid Institute of Materials Science (ICMM) in Spain, say they may have found an answer. By measuring a property known as quantum capacitance, they report that they have read out the parity of their MZM system, backing up an earlier readout demonstration from a team at Microsoft Quantum Hardware on a different Majorana platform. Measuring parity The QuTech/ICMM researchers generated their MZMs across two quantum dots – semiconductor structures that can confine electrons – connected by a superconducting nanowire. Electrons can transfer, or tunnel, between the quantum dots through this wire. Majorana-based qubits store their quantum information across these separated MZMs, with both elements in the pair required to encode a single “parity” bit. A pair of parity bits (combining four MZMs in total) forms a qubit. A parity bit has two possible states. When the two quantum dots are in a superposition of both having one electron and both having none, the system is said to have even parity (a “0”). When the system is instead in superposition of only one of the quantum dots having an electron, the parity is said to be odd (a “1”). Importantly, these eve
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quantum-computingAssessing quantum coherence in quantum annealers
--> Quantum Physics arXiv:2602.21355 (quant-ph) [Submitted on 24 Feb 2026] Title:Assessing quantum coherence in quantum annealers Authors:Connor Aronoff, Travis Howard, David Nicholaeff, Alejandro Lopez-Bezanilla, Wade DeGottardi View a PDF of the paper titled Assessing quantum coherence in quantum annealers, by Connor Aronoff and 4 other authors View PDF HTML (experimental) Abstract:Demonstrating genuine many-body quantum coherence in large-scale quantum processors remains a central challenge for near-term quantum technologies. Recent experiments on D-Wave quantum annealers have investigated quenches of Ising chains and observed defect densities that show Kibble-Zurek scaling, consistent with coherent quantum dynamics. However, identical scaling can arise from classical or thermal processes. Here we propose the use of many-body coherent oscillations (MBCO) as a diagnostic for the identification of system-wide coherence in analog quantum simulators. Solving the time-dependent Schrodinger equation, we show that quenches of a staggered one-dimensional Ising chain across a quantum critical point produce oscillatory signatures in defect observables. We implement this model on the D-Wave Advantage quantum annealer. Using fast-anneal protocols, we find that, although defect densities follow Kibble-Zurek scaling, the expected oscillatory behavior is absent. We demonstrate that static disorder associated with individual qubits is not likely responsible for the absence of MBCO. Modest modifications to annealing schedules can dramatically enhance oscillation visibility. This work gives a general roadmap for the search for quantum coherence in noisy, large-scale quantum platforms. Comments: Subjects: Quantum Physics (quant-ph); Other Condensed Matter (cond-mat.other) Cite as: arXiv:2602.21355 [quant-ph] (or arXiv:2602.21355v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.21355 Focus to learn more arXiv-issued DOI via DataCite (pending registrat
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quantum-computingQuantum Computers Cut Measurement Costs with New Method
Researchers are developing new methods to efficiently find the ground state of quantum systems, a crucial task for utilising quantum computers and a key subroutine for many algorithms. Jona Erle from the Mathematical Institute, University of Oxford, Quantum Motion, and Moderna, working with Balint Koczor from the Mathematical Institute, University of Oxford, present a novel approach called the Shadow Enhanced Greedy Quantum Eigensolver (SEGQE). This framework significantly reduces the need for costly logical measurements by employing classical shadows to rapidly evaluate the impact of potential quantum gates. Their work establishes rigorous bounds on computational cost and demonstrates, through numerical testing on transverse-field Ising models and random Hamiltonians, that SEGQE scales favourably with system size, offering a promising measurement-efficient strategy for early-stage quantum computing architectures. Imagine sculpting a perfect miniature from a block of stone, refining it with each careful strike. To achieve similar precision with quantum systems is immensely challenging. Yet a new method dramatically reduces the number of operations needed to find a system’s lowest energy state. This advance offers a practical path toward utilising near-term quantum computers for complex calculations. Scientists are increasingly focused on preparing high-fidelity ground states as a central task within quantum computing, with applications extending to quantum chemistry, quantum optimisation, and quantum machine learning. Highly expressive circuits can suffer from barren plateaus, regions where gradients become exponentially small, while simpler circuits may lack the necessary accuracy to approximate the target state effectively. These trainability issues are often compounded by poor local minima during optimisation, demanding a substantial measurement budget for practical applications. Resource minimisation is particularly critical in the early stages of fault-tolerant
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quantum-computingQuantum Computing. Explained. - - rbc.com
MENU Explained Quantum Computing. Explained. What you need to know about the rapidly emerging field of quantum computing, which can solve problems faster than a supercomputer Quantum computing is quietly moving from an interesting physics challenge to potentially a strategic solution in boardrooms worldwide. The global market for quantum technology is expected to reach up to US$97 billion by 2035. Canada sits close to that shift, with a deep research base and a small set of firms trying to translate scientific advantage into industrial capability. What is quantum computing? Quantum computers aren’t replacing classical machines. They’re a specialist tool for problems that even today’s best supercomputers can’t handle. In 2024, Google’s Willow chip completed a benchmark calculation in under five minutes that would take a leading supercomputer an estimated 10 septillion years, vastly exceeding the age of the universe. A classical computer tries possibilities one by one, through binary bits (0 or 1). Whereas a quantum computer uses qubits, which keep many possibilities alive at once (superposition), links parts of the problem so they move together (entanglement) and uses cancellation/reinforcement to make wrong answers fade and right answers stand out (interference). Why does quantum computing matter? It can solve problems classical computers can’t handle. Bain estimates quantum computing could unlock up to $250 billion in value across pharmaceuticals, finance, logistics, and materials science. Consider drug discovery: bringing a drug to market could cost up to $4 billion and can take more than a decade. Add to that, the fact that about 90% of drug trials fail. Quantum computers can simulate molecular interactions at the atomic level: something classical machines can only approximate, heavily compressing timelines. The security clock is already ticking. The most immediate business risk is “harvest now, decrypt later”: adversaries collect encrypted data today and wait fo
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