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-computingStatistical Quantum Phase Estimation: Extensions and Practical Considerations
--> Quantum Physics arXiv:2605.18876 (quant-ph) [Submitted on 15 May 2026] Title:Statistical Quantum Phase Estimation: Extensions and Practical Considerations Authors:Amit Surana, Brandon Allen View a PDF of the paper titled Statistical Quantum Phase Estimation: Extensions and Practical Considerations, by Amit Surana and Brandon Allen View PDF HTML (experimental) Abstract:We present several refinements and extensions of the statistical quantum phase estimation (SQPE) framework to address some of its key practical limitations, improving its applicability to realistic cases. Recently, a family of statistical approaches for QPE have been proposed where each run uses only a few ancillae and shorter circuits than standard QPE and thus is better suited for early fault-tolerant quantum computers that are qubit-and depth-limited. SQPE method within that family estimates the cumulative distribution function (CDF) associated with spectral density of the Hamiltonian for a given trial state by using its Fourier approximation and then identifies the first jump discontinuity of the CDF to determine the ground state energy (GSE) of the Hamiltonian. It relies on random compilation procedure based on linear combination of unitaries (LCU) decomposition of the Hamiltonian assuming positive Pauli weights and requires a good estimate of lower bound on the overlap between the trial and true ground state, both of which may be difficult to achieve in practice. We address these limitations by generalizing the random compilation procedure for negative Pauli weights and employing a changepoint detection method for determining GSE which does not rely on an estimate of this overlap. We also show that by exploiting symmetry of the Fourier series one can reduce number of circuit runs/samples by a factor of 2x while keeping the GSE estimation accuracy the same. We illustrate these new developments numerically via a quantum simulator in Qiskit. Comments: Subjects: Quantum Physics (quant-ph) Cite as
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quantum-computingLogical Resource Estimation for Quantum State Preparation with Compilation
--> Quantum Physics arXiv:2605.18877 (quant-ph) [Submitted on 15 May 2026] Title:Logical Resource Estimation for Quantum State Preparation with Compilation Authors:Diyi Liu, Hanyu Wang, Shuchen Zhu, Jason Cong, Wibe A. de Jong, Di Fang, Zhen Huang, Costin Iancu, Chao Yang View a PDF of the paper titled Logical Resource Estimation for Quantum State Preparation with Compilation, by Diyi Liu and 8 other authors View PDF HTML (experimental) Abstract:Quantum state preparation is a fundamental primitive in quantum algorithms for encoding classical data into quantum amplitudes. We compare the cost of preparing general $n$-qubit states with real amplitudes using two common paradigms: rotation-based methods, based on controlled rotations, and sampling-based methods, based on a structured representation of the target state. Although these approaches are often theoretically compared using CNOT count and $T$-count, their relative performance in total gate count remains less well understood practically. We compare representative rotation-based and sampling-based methods using $T$-count and total gate count, and analyze how compilation overhead affects their relative performance. We also develop a software package for compiling state preparation circuits, designed as a practical subroutine for more general quantum computations. Numerical experiments on resource states and quantum states related to quantum chemistry, condensed matter physics, and simulation via Magnus expansion over a range of target accuracies $\epsilon$ support the analysis. Our results show that sampling-based methods achieve asymptotically lower $T$-count and retain an overall advantage after accounting for total gate count and compilation overhead. Subjects: Quantum Physics (quant-ph) MSC classes: 81P68, 68Q12 Cite as: arXiv:2605.18877 [quant-ph] (or arXiv:2605.18877v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.18877 Focus to learn more arXiv-issued DOI via DataCite (pendin
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quantum-computingOperator-Algebraic Methods for Asymptotic-Preserving Quantum Simulation of Open Systems
--> Quantum Physics arXiv:2605.18886 (quant-ph) [Submitted on 16 May 2026] Title:Operator-Algebraic Methods for Asymptotic-Preserving Quantum Simulation of Open Systems Authors:M.W. AlMasri View a PDF of the paper titled Operator-Algebraic Methods for Asymptotic-Preserving Quantum Simulation of Open Systems, by M.W. AlMasri View PDF HTML (experimental) Abstract:We develop a mathematically rigorous framework for simulating \emph{multiscale physical systems} using quantum computational resources, by translating the \emph{language of asymptotic-preserving (AP) schemes} into the formalism of quantum channels and Lindbladian dynamics. For stiff open quantum systems governed by singularly perturbed generators $\cL_\eps = \eps^{-1}\cL_{\mathrm{fast}} + \cL_{\mathrm{slow}}$ with $\eps \to 0$, we prove that layered quantum protocols, which implement fast-scale relaxation via native analog evolution or analytic manifold projection, converge uniformly in the diamond norm to consistent discretizations of the limiting slow dynamics, with explicit error bound $\mathcal{O}(\eps\Delta t + \Delta t^2)$ independent of stiffness. We establish precise resource-complexity bounds showing that superlinear gate-count savings $\Omega(\kappa\cdot(d_{\mathrm{tot}}/d_{\mathrm{slow}})^c)$ arise if and only if fast dynamics are resolved via (i) hardware-native analog evolution, or (ii) analytic adiabatic elimination reducing effective Hilbert space dimension. The framework is illustrated through cavity QED in the bad-cavity limit and a quantum-inspired AP discretization of kinetic equations converging to fluid limits, with quantified error propagation in trace and diamond norms. This work provides a principled mathematical bridge between classical multiscale numerical analysis and quantum simulation algorithms. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.18886 [quant-ph] (or arXiv:2605.18886v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.1
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quantum-computingQuantum-centric supercomputing simulates 12,635-atom protein
The scale of chemistry simulations with quantum computing has increased dramatically in just the last few months. In the latest milestone for the field, researchers from Cleveland Clinic, RIKEN, and IBM used a quantum-centric supercomputing (QCSC) framework to calculate the electronic structure of a pair of large protein-ligand complexes, reaching a scale of 12,635 atoms in the largest simulation.
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quantum-computingBoundary-Aware QFT Block-Encoding of Fractional Laplacians
--> Quantum Physics arXiv:2605.16749 (quant-ph) [Submitted on 16 May 2026] Title:Boundary-Aware QFT Block-Encoding of Fractional Laplacians Authors:Younes Javanmard, Sina Kazemian View a PDF of the paper titled Boundary-Aware QFT Block-Encoding of Fractional Laplacians, by Younes Javanmard and Sina Kazemian View PDF HTML (experimental) Abstract:We study the quantum Fourier transform (QFT) block-encoding of the semi-discrete fractional Laplacian on bounded domains with open, zero-extension boundary conditions. In the notation of the main construction, the target operator is the finite Toeplitz truncation \(A^{(N)}_{\alpha,h}\) obtained from the full-lattice semi-discrete operator with symbol \(|\xi|^\alpha\). A finite QFT register, however, diagonalizes circulant matrices rather than Toeplitz truncations. The native QFT circuit therefore implements a periodic surrogate \(\widetilde A^{(N)}_{\alpha,h}\), not the open-boundary operator. We identify this mismatch through an exact Toeplitz-to-circulant aliasing identity. To recover the open-boundary action, we zero-pad the state into a larger \(M\)-point QFT register, apply the same Fourier-symbol block-encoding, and compress back to the physical subspace. The resulting compressed block satisfies \(P_{N\to M}^{\dagger}\widetilde A^{(M)}_{\alpha,h}P_{N\to M} = A^{(N)}_{\alpha,h}+E^{(M)}\), where \(E^{(M)}\) is controlled by the tail of the semi-discrete convolution kernel. Thus, the QFT layer implements the fractional symbol, while zero-padding supplies the open-boundary geometry. The construction is an operator-compilation primitive for boundary-aware quantum simulation rather than a complete PDE solver. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.16749 [quant-ph] (or arXiv:2605.16749v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.16749 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Sina Kazemian [view email] [v1] Sat, 16 Ma
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quantum-computingSandboxAQ brings its drug discovery models to Claude — no PhD in computing required
Drug discovery is one of the most expensive failures in modern industry. Finding a single viable molecule can take a decade and cost billions, and most candidates still don’t make it. A generation of AI startups has promised to fix that — most have made the problem less painful for researchers, who are already technically sophisticated enough to use the tools. But SandboxAQ thinks the bottleneck isn’t the models. It’s the interface. The company has teamed up with Anthropic to integrate its scientific AI models directly into Claude — putting powerful drug discovery and materials science tools behind a conversational interface that requires no specialized computing infrastructure to use. Founded roughly five years ago as an Alphabet spinout, SandboxAQ counts Eric Schmidt, Google’s former CEO, as its chairman. The company, which has raised more than $950 million from investors, has built out a number of different business lines, including a cybersecurity business. One of the more unique things SandboxAQ does, however, is produce large quantitative models, or LQMs. These proprietary models are “physics-grounded,” meaning they’re built on the rules of the physical world rather than patterns in text. They can run quantum chemistry calculations and simulate both molecular dynamics and microkinetics, the study of how chemical reactions unfold at the molecular level. That matters because it tells researchers how candidate molecules are likely to behave before anyone sets foot in a lab. “Trained on real-world lab data and scientific equations, LQMs are AI models engineered for the quantitative economy, a $50+ trillion sector spanning biopharma, financial services, energy, and advanced materials,” the company said in a news release that strongly suggests Sandbox AQ isn’t building another chatbot or code assistant — it’s chasing the economy that AI is supposed to transform. Chai Discovery and Isomorphic Labs — both well-funded bets on better models — have focused on the science
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quantum-computingTwisted WSe₂ reveals elusive charge-neutral quantum modes
Quantum materials, materials with properties that are influenced by the laws of quantum mechanics, have attracted considerable attention over the past few decades. Their unique properties make these materials advantageous for the development of numerous cutting-edge technologies, including quantum computers, highly sensitive sensors and energy-efficient electronics.
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quantum-computingThe Quantum Computing IPO Wall Street Hasn't Figured Out Yet -- but Should in 2026
With computational power that classical systems can only dream of, quantum computing promises to transform industries from drug discovery to cybersecurity. Despite flying under the radar, Quantinuum's initial public offering (IPO) marks a pivotal moment for investors seeking exposure to this nascent segment of the artificial intelligence (AI) sector. Quantinuum's recent S-1 filing invites public scrutiny of the company's ambitious technology roadmap amid sky-high expectations benchmarked against sobering financial realities. With its Nasdaq listing on the horizon, smart investors now have an opportunity to explore Quantinuum's origins and differentiation from other quantum computing pure plays before the shares hit the public markets. Image source: Getty Images. What do smart investors need to know about Quantinuum? Quantinuum was formed in 2021 following the merger of Honeywell Quantum Solutions and Cambridge Quantum Computing. Although Honeywell remains a major shareholder, additional backers include blue chip names such as Nvidia (NVDA 4.39%), JPMorgan Chase, Fidelity, Mitsui, and Amgen. Quantinuum develops trapped-ion quantum computers integrated with advanced software toolkits. Trapped-ion technology uses lasers to suspend individual atoms in electromagnetic fields to yield qubits with exceptionally high fidelity and long coherence times. What sets Quantinuum apart from other quantum artificial intelligence (AI) companies is its full-stack approach. Rigetti Computing's (RGTI 7.37%) superconducting qubits operate at near-zero temperatures, which can bring variability during manufacturing processes. By contrast, Quantinuum's ions are naturally identical and inherently stable. This approach can improve error-correction compared to Rigetti's simulations. Meanwhile, D-Wave's (QBTS 8.04%) annealing technique excels at optimization applications but ultimately lacks the ubiquity of gate-based programs for broad applications. In turn, this limits D-Wave t
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quantum-computingPrediction: D-Wave Quantum Stock (QBTS) Will Be Worth This Much by the End of 2026
There is a version of the narrative around D-Wave Quantum (QBTS 8.04%) that reads like science fiction turned into an investable reality. Then there is the version where investors look at the cold, hard financial profile of the company. Right now, retail investors are trading on the first version of the story. But smart investors should focus on the second, and avoid following the optimistic crowd into D-Wave Quantum. Image source: Getty Images. What does D-Wave Quantum do? Quantum computers operate in a way that is counterintuitive, and wildly different from how the classical computers that we are all familiar with do. While traditional devices hold and process all of their data in bits as binary code -- 1s and 0s -- quantum systems process information using "qubits," which can also hold complex probability states through a property called superposition. Precisely how they capitalize on that property to perform computations is a level of detail most investors can skip -- but the key point is that, in theory, quantum computers should be able to rapidly perform unusual and extremely complex computations that would take a traditional supercomputer years (or centuries) to complete. D-Wave is following an unusual strategy in its industry: It's focused specifically on a technology called quantum annealing. On one hand, quantum annealing machines will be unsuitable for a host of workloads that more general-purpose quantum computers could handle. On the other hand, what they are ideally suited for are optimization problems, and those come up heavily in some of the most obvious use cases for quantum computing, including drug discovery, logistics optimization, financial modeling, and cryptography. The artificial intelligence (AI) connection is obvious: As demand for more powerful computing systems rises alongside model training and inference, quantum computing advocates are framing the technology as the next frontier beyond traditional GPU superclusters. ExpandNYSE: QBTSD-Wa
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quantum-computingBeyond Unitary Quantum Simulation: Open-System Approaches to Quantum Chemistry toward Quantum Advantage
--> Quantum Physics arXiv:2605.15277 (quant-ph) [Submitted on 14 May 2026] Title:Beyond Unitary Quantum Simulation: Open-System Approaches to Quantum Chemistry toward Quantum Advantage Authors:Michael Marthaler, Elias Zapusek, Florentin Reiter View a PDF of the paper titled Beyond Unitary Quantum Simulation: Open-System Approaches to Quantum Chemistry toward Quantum Advantage, by Michael Marthaler and 2 other authors View PDF Abstract:Quantum simulation is widely regarded as one of the most promising routes to genuine quantum advantage, yet most existing approaches to quantum chemistry are formulated in terms of closed-system, unitary dynamics and ground-state preparation within the Born--Oppenheimer approximation. In this review, we discuss a broader perspective motivated by the observation that naturally occurring quantum systems are rarely isolated and often reach physically relevant states only through relaxation, decoherence, and thermalization. We first examine what is and is not known about exponential quantum advantage in chemistry, emphasizing that coherent Hamiltonian simulation provides the clearest formal case for speed-up, while many open questions remain for realistic problems. We then discuss how dissipation might ideally be integrated into quantum chemistry on a fault-tolerant quantum computer, using recent proposals for chemically motivated dynamical simulation as a guiding vision. More generally, we highlight the practical appeal of this approach to enhancing the robustness of quantum algorithms. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.15277 [quant-ph] (or arXiv:2605.15277v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.15277 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Florentin Reiter [view email] [v1] Thu, 14 May 2026 18:00:04 UTC (2,611 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond Unitary Quantum Simulatio
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quantum-computingPhysicists Observe Strange Quantum Rotation Effect That Defies Intuition
Like on a Ferris wheel, a powerful terahertz laser drives the atoms of a crystal along precise circular paths. The resulting collective oscillation of the crystal lattice was traced stroboscopically using ultrashort laser pulses; the blue lines show the measured data. Surprisingly, the oscillation rotates in the opposite direction. Credit: O. Minakova/ S.F. Maehrlein/ B. Schröder/ HZDRResearchers discovered that atomic rotations inside a crystal can unexpectedly flip direction while still obeying the laws of angular momentum conservation.An international team of researchers, including scientists from the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) and the Fritz Haber Institute of the Max Planck Society, has directly observed for the first time how angular momentum moves and remains conserved inside a crystal lattice. By using powerful terahertz laser pulses, the team was able to precisely manipulate these motions and discovered an unexpected effect: during the transfer process, the direction of rotation flips because of the material’s rotational symmetry.The study, published in Nature Physics, offers new insight into the origins of magnetism and could help researchers develop more precise ways to control quantum materials.Understanding Angular Momentum in SolidsQuantities such as energy, momentum, and angular momentum are governed by conservation laws, meaning they cannot be created or destroyed in a closed system. Instead, they can only be transferred or converted into other forms. Although angular momentum is commonly associated with spinning objects like bicycles or carousels, it is also fundamental to quantum physics and plays a key role in magnetism.More than a century ago, Albert Einstein and Wander Johannes de Haas showed that altering a material’s magnetization could produce measurable mechanical rotation. Their experiment demonstrated that magnetic and mechanical angular momentum are closely connected. Since then, scientists have tried to determine exactly
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quantum-computingPrediction: This Quantum Computing Stock Will Be Up More Than 20% by the End of 2026
Last month, Nvidia (NVDA 4.39%) unveiled a family of open-source artificial intelligence (AI) models designed to address calibration and error-correction challenges in quantum computing. Known as Ising, Nvidia's new toolkit helps make fragile qubits more usable by pairing them with the power of graphics processing units (GPUs). The question smart investors are asking is not whether quantum computing matters, but how Nvidia's expanding ecosystem positions the company to capture more value as investment in AI infrastructure accelerates. Image source: Nvidia. Why do investors care about quantum computing? Today's generative AI models devour vast amounts of computing power. However, some quantum enthusiasts believe that certain applications -- molecular simulation in drug discovery or complex optimization in logistics and financial modeling -- can scale better on quantum hardware. In essence, quantum systems are not a replacement for traditional AI platforms, but rather an accelerator. Nvidia's vision is to create a hybrid future in which quantum processors handle complex tasks while GPUs handle the heavy workload of training and inference. Nvidia is expanding its role in the quantum computing landscape Nvidia found that Ising's calibration model can automate processor tuning, reducing a task that once took days down to hours. Moreover, its decoding models deliver real-time error correction up to 2.5 times faster and 3 times more accurately than legacy methods such as PyMatching. Ising's tools run on Nvidia GPUs, integrate natively with the company's CUDA-Q software, and are stitched together via the NVQLink interconnect. By anchoring Ising to its hardware and software stack, Nvidia ensures that advances in quantum capabilities fuel further demand for its GPU architectures and data center services. This strategy helps Nvidia turn yet another AI opportunity, quantum machines, into a natural tailwind for its existing infrastructure. Nvidia's path to more upside in 2026 Re
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quantum-computingPossible application of Quantum Information
Recently, I was thinking where Quantum Computing might have a real world impact after recent advancements in Quantum Computing. The use cases include many, but I was searching for something related to fundamental sciences. In this quest, I came across a lecture given by Prof. David Tong at The Royal Institution about Quantum Field 9 years back. It explains the Standard Model with 12 fundamental particles, 4 fields and Dirac equation that explains all the experiments that we can carry out ourselves. However, it can't explain a lot of things happening in the universe, things influenced by dark matter, dark energy and an event that marks the initial period of the universe termed as inflation. He further talked about the importance of Large Hadron Collider in finding the Higgs Boson particle and field; which explains the gravitational force and field. The conclusion of the video was about what comes next and he discussed 3 possible ways. That's the part where I seem to find my answer. He believes that, the answer to the unexplainable observations might be hidden in Dirac equation itself, it's just that we have to look through a different perspective. However, LHC operations are too cost and resource heavy for a government to sponsor these experiments and one of the possible ways was Quantum Information. This video was posted 9 years back when Quantum Computing was really in it's infant phase but with recent advancements, we've hardwares and algorithms that are much better at Quantum Simulation . Maybe we can use these tools to understand and explain the unexplainable? What are your thoughts? Also, here is the link to the lecture: https://youtu.be/zNVQfWC\_evg?si=NxRKlgliLilSKZNX submitted by /u/SoumyadipNayak [link] [comments]
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quantum-computingGoogle Launches REPLIQA to Integrate Quantum AI and Life Sciences
Google Launches REPLIQA to Integrate Quantum AI and Life Sciences Google Quantum AI and Google.org have launched the Research Program at the Intersection of Life Sciences & Quantum AI (REPLIQA), a $10 million initiative dedicated to applying quantum science and artificial intelligence to molecular biology. The program provides foundational research funding to five academic institutions: Harvard University, MIT, UC San Diego, UC Santa Barbara, and the University of Arizona. The objective is to utilize the principles of quantum mechanics to simulate biological processes that are computationally inaccessible to classical systems, such as protein folding and subatomic cellular functions. Quantum Advantage in Molecular Simulation The REPLIQA initiative focuses on the inherent alignment between quantum computing architectures and the quantum mechanics that govern molecular interactions. While classical computers rely on approximations to simulate complex chemistry, quantum technologies operate using the same subatomic logic as the molecules they analyze. A primary target for the program is the simulation of the P450 enzyme, a critical component in drug metabolism that has historically challenged traditional high-performance computing. Additionally, the program explores the role of quantum spin in cellular function and the development of quantum sensors capable of observing biological processes with atomic-scale precision. Foundational Tools for Biological Discovery Led by Hartmut Neven, Founder and Lead of Google Quantum AI, REPLIQA is structured as a long-term research effort to develop the essential toolkits required for future medical breakthroughs. This includes the creation of quantum-enhanced AI algorithms and high-precision sensing hardware to monitor real-time metabolic reactions. By establishing a collaborative ecosystem between Google’s researchers and leading universities, the program aims to close the gap between theoretical quantum physics and practical a
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quantum-computingPurdue Launches Comprehensive Quantum Degrees Program
Insider Brief PRESS RELEASE — Quantum technologies, which harness nature at the tiniest scales, hold the potential to transform how people compute, communicate and sense their environments. Critical areas that could benefit from emerging quantum technology include AI design of new functional materials for safer and cheaper consumer products, therapeutic drug development, cybersecurity, and deployment […]
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quantum-computingSemiconductors Or Quantum Computing - Or Semiconductors And Quantum Computing?
WisdomTree5.83K FollowersFollow5ShareSaveCommentsSummaryQuantum computing’s recent momentum, highlighted by IonQ’s World Quantum Day networking breakthrough, reinforces growing investor interest in specialized computing platforms that could reshape fields like drug discovery, optimization and cryptography.Despite excitement around quantum innovation, the sector still depends heavily on classical semiconductor infrastructure for qubit control, error correction and data processing, underscoring why the future of computing will likely be built on both architectures together.For investors seeking targeted exposure to the quantum theme, the WisdomTree Quantum Computing Fund emphasizes pure-play quantum innovators and offers differentiated access to companies positioned at the forefront of next-generation computing development. Just_Super/E+ via Getty Images By Christopher Gannatti, CFA & Jonathan Flynn Step One: What Quantum Computing Actually Is (And Isn't) Let's start with the concept, because confusion here can be real and consequential. Classical computers, specifically the kind in yourThis article was written byWisdomTree5.83K FollowersFollowIn 2006, WisdomTree launched with a big idea and an impressive mission — to create a better way to invest. We believed investors shouldn’t have to choose between cost efficiency and performance potential, so we developed the first family of ETFs designed to deliver both. Today, WisdomTree offers a leading product range that offers access to an unparalleled selection of unique and smart exposures.
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quantum-computingScientists Just Measured an Energy Pulse Smaller Than a Trillionth of a Billionth of a Joule
Science Scientists Just Measured an Energy Pulse Smaller Than a Trillionth of a Billionth of a JouleBy Aalto UniversityMay 14, 20263 Mins Read Facebook Twitter Pinterest Telegram LinkedIn WhatsApp Email Reddit Share Facebook Twitter LinkedIn Pinterest Telegram Email Reddit Researchers in Finland have developed an ultra-sensitive calorimeter capable of detecting energy levels below one zeptojoule. Credit: Ella Maru StudioA newly developed quantum sensor has measured unimaginably small amounts of energy with record-breaking precision.A newly developed technique for measuring unimaginably small amounts of energy could help advance quantum computing and improve the search for dark matter. The method is sensitive enough to detect less than a trillionth of a billionth of a joule and may eventually allow scientists to count individual photons.Quantum mechanics operates at extremely small scales, so researchers are continually developing more precise tools to study particles such as photons, which carry light. Better measurements could improve quantum technologies and help scientists detect hypothetical dark matter particles known as axions.Researchers in Finland recently used an ultra-sensitive heat-based sensor called a calorimeter to measure energy levels below one zeptojoule, equal to one trillionth of a billionth of a joule. For comparison, a zeptojoule is about the amount of energy needed to move a red blood cell upward by one nanometer in Earth’s gravity.The research team was led by Academy Professor Mikko Möttönen at Aalto University in collaboration with quantum computing company IQM and the Technical Research Centre of Finland (VTT). Their findings were published in Nature Electronics.How the Sensor WorksMeasuring energy at this scale is extremely challenging. To perform the experiment, the researchers sent a microwave pulse into a sensor made from two types of metals: superconductors, which allow electrical signals to move freely, and standard conductors, which c
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quantum-computingGoogle Launches $10 Million Quantum Biology Research Initiative
Insider Brief Google launched the REPLIQA program, committing $10 million to five universities to explore how quantum computing, quantum sensing and AI could advance biological and medical research. The initiative focuses on foundational research into areas such as molecular simulation, quantum-enhanced AI algorithms and quantum sensors that may help scientists better study proteins, enzymes and cellular behavior. Google said the program is a long-term scientific effort rather than a near-term commercialization project, reflecting the early-stage limitations of current quantum computing technology. Google is investing $10 million in a new research effort aimed at applying quantum computing, quantum sensing and artificial intelligence to biology and medicine, as large technology companies continue searching for practical uses for emerging quantum systems. The initiative, called the Research Program at the Intersection of Life Sciences & Quantum AI, or REPLIQA, was announced by Google Quantum AI and Google.org in a blog post. The funding will support research at five universities: Harvard University, Massachusetts Institute of Technology, University of California San Diego, University of California, Santa Barbara and University of Arizona. The program reflects growing interest in whether quantum technologies could eventually help researchers better understand biological systems that remain difficult to model with conventional computers. Google said biological processes such as protein folding, enzyme behavior and cellular reactions involve interactions at the atomic scale that are often computationally expensive or impractical to simulate accurately using classical systems. Quantum computers process information using quantum mechanics, the same physics that governs molecules and atoms. Researchers say that this could make quantum systems better suited for certain chemistry and materials problems than traditional computers, though most experts believe practical lar
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quantum-computingA Quantum Multi-Programming Framework to Maximize Quantum Resources for the LUCJ Ansatz
--> Quantum Physics arXiv:2605.12614 (quant-ph) [Submitted on 12 May 2026] Title:A Quantum Multi-Programming Framework to Maximize Quantum Resources for the LUCJ Ansatz Authors:Milana Bazayeva, Abigail McClain Gomez, Kenneth M. Merz Jr View a PDF of the paper titled A Quantum Multi-Programming Framework to Maximize Quantum Resources for the LUCJ Ansatz, by Milana Bazayeva and 2 other authors View PDF HTML (experimental) Abstract:In the context of quantum computing, efficient resource management is crucial for optimizing throughput on cloud-based platforms and maximizing hardware utilization. In the present work, we propose an approach to tackle quantum chemistry problems via quantum multi-programming of the Local Unitary Cluster Jastrow (LUCJ) ansätze. The ground-state energy of the molecular system is obtained via Sample-based quantum diagonalization (SQD), further refined by its extended version (ext-SQD). Building upon the Qiskit Experiments package, which already supports parallel execution functionality for general tasks, we developed a novel parallel experiment class tailored for quantum chemistry problems. Cross-talk is a known issue in the multi-programming frameworks and can corrupt the ground-energy estimation of the simulated systems. To assess its impact within our approach, we simulated two conformations of the ethanol molecule: one at the equilibrium state (EtOH$_{Eq}$), and one with the O-H bond stretched to 1.2 ${Å}$ (EtOH$_{1.2}$). We defined three different layouts that we executed in a randomized fashion, alternating serial and parallel execution within 10 independent replicates. The single modality of each circuit was kept as a baseline to evaluate the effect of cross-talk induced by quantum multi-programming. The energies obtained at the first-, last- and ext-SQD iteration were compared to the classical Heat-bath Configuration Interaction (HCI) reference. Our findings highlight the viability of a quantum multi-programming workflow for quantum ch
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quantum-computingExplicitly Correlated Gaussian Basis Approach to Periodic Systems
--> Quantum Physics arXiv:2605.12781 (quant-ph) [Submitted on 12 May 2026] Title:Explicitly Correlated Gaussian Basis Approach to Periodic Systems Authors:Kalman Varga View a PDF of the paper titled Explicitly Correlated Gaussian Basis Approach to Periodic Systems, by Kalman Varga View PDF HTML (experimental) Abstract:Closed-form expressions for all matrix elements required for variational calculation of the electronic structure of periodic solids have been derived using a basis of explicitly correlated Gaussians (ECGs). Periodic basis functions are constructed by summing shifted correlated Gaussians over all composite lattice translations, where a generalized unfolding theorem reduces the resulting double lattice sum to a single sum through a unified computational framework for overlap, kinetic energy, and Coulomb potential operators. The formalism has been validated through application to an infinite one-dimensional hydrogen chain, where the ground-state energy per atom computed in the thermodynamic limit is shown to agree with finite-chain results extrapolated by other many-body methods. Subjects: Quantum Physics (quant-ph); Chemical Physics (physics.chem-ph) Cite as: arXiv:2605.12781 [quant-ph] (or arXiv:2605.12781v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.12781 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kalman Varga [view email] [v1] Tue, 12 May 2026 21:50:41 UTC (56 KB) Full-text links: Access Paper: View a PDF of the paper titled Explicitly Correlated Gaussian Basis Approach to Periodic Systems, by Kalman VargaView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2026-05 Change to browse by: physics physics.chem-ph References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided
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