Quantum Machine Learning: QML Algorithms & Quantum AI Applications
Quantum machine learning news: QML algorithms, quantum AI, quantum neural networks. Hybrid quantum-classical ML & quantum advantage research.
Quantum machine learning (QML) explores intersections between quantum computing and artificial intelligence, investigating whether quantum algorithms can accelerate data analysis, pattern recognition, and model training beyond classical capabilities.
Theoretical foundations include quantum advantages for linear algebra subroutines central to machine learning—matrix inversion, principal component analysis, and vector inner products. The HHL algorithm promises exponential speedup for specific sparse, well-conditioned systems.
India's Quantum Machine Learning Landscape
India's National Quantum Mission supports quantum machine learning research through its Quantum Computing Thematic Hub at IISc Bengaluru. The Indian Institute of Science offers a Certificate Programme in Quantum Computing and Artificial Intelligence through its Centre for Continuing Education, providing comprehensive training in quantum AI applications with hands-on coding using Qiskit and PennyLane.
Tata Consultancy Services (TCS) develops quantum machine learning algorithms for enterprise applications. Infosys explores quantum AI through its Quantum Living Labs. IIT Delhi offers certification programs in quantum computing and machine learning in collaboration with industry partners.
The NQM targets developing quantum algorithms for optimization, simulation, and machine learning, with human resource development including training programs for quantum professionals.
Current NISQ-era QML relies on hybrid quantum-classical approaches including variational quantum algorithms, quantum neural networks, and quantum kernel methods. Challenges include "barren plateaus" in optimization landscapes limiting trainability, and limited qubit counts restricting model complexity.
quantum-computingPan and Colleagues Implement Patch-Based Logical Operations for Surface-Code Processing
A new set of tools for fault-tolerant logical operations brings practical quantum computation closer to reality. Weiping Lin and colleagues from University of Science and Technology of China, Tsinghua University and Zhongguancun Laboratory, have experimentally realised key elements of surface-code logical processing using a 107-qubit superconducting quantum processor. They implemented reusable primitives for manipulating surface-code patches, enabling logical state routing and a full Clifford gate set, a sharp advance beyond storing protected logical memory. The demonstration represents a vital progression in superconducting surface-code experiments, paving the way for more complex quantum algorithms and fault-tolerant computation. Reusable qubit operations enable flexible surface code manipulation A new breakthrough hinged on developing a ‘primitive layer’ of reusable operations for manipulating surface-code patches; this is akin to a mosaic artist mastering a few key tile arrangements that can then be combined to create complex designs. Surface codes are a leading approach to quantum error correction, encoding logical qubits using multiple physical qubits arranged in a two-dimensional lattice. Protecting quantum information requires maintaining the delicate superposition and entanglement of qubits, which are highly susceptible to environmental noise. Surface codes achieve this by distributing the quantum information across the lattice and encoding it in the correlations between qubits. The developed primitive layer allows for dynamic rearrangement of these encoded qubits without destroying the encoded information. Merge, split, expansion, shrinkage, and deformations mediated by domain walls and twist defects allowed for precise reshaping of sections of the qubit grid without disrupting the encoded quantum information. Domain walls represent boundaries between regions with different logical properties, while twist defects introduce controlled changes in the lattice
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quantum-computingQUDORA Partners with QAI to Bring Ion-Trap Quantum Computing to South Korea
Insider Brief Press release – QUDORA Technologies GmbH (“QUDORA“), a European full-stack ion-trap quantum computing company, today announced the signing of a Memorandum of Understanding (MOU) with Korean quantum AI specialist QAI Co., Ltd. (“QAI”) to pursue the deployment of QUDORA’s ion-trap quantum computing technology in Korea and demonstrate hybrid AI-Quantum services for a wide […]
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quantum-computingRISC-V Vector Engine Addresses 128 Qubits With One Instruction
Researchers are forecasting significant advances in quantum control for 2026, centered around a new approach leveraging the RISC-V Vector (RVV) engine. The team reports demonstrating the ability to address 128 qubits with a single instruction, a critical step toward scaling quantum systems beyond current limitations. This vectorized quantum control design also incorporates a hardware-based halt-resume protocol capable of restarting pipeline execution in 80 nanoseconds after a mid-circuit measurement, essential for the rapidly developing field of hybrid quantum-classical algorithms. Comprehensive evaluation using RISC-V toolchains and FPGA prototypes showed a 2.52 times speedup in program execution time compared to baseline designs, suggesting a pathway to overcome the classical control bottleneck hindering quantum processor expansion. Within each circuit family, speedup grows with the number of qubits; for example, performance increased from Bell-4 to Bell-8 by a factor of 52. This progression indicates that larger, more complex quantum algorithms will increasingly benefit from hardware designed to efficiently manage and process a greater number of qubits, moving beyond the limitations of earlier, smaller-scale systems. This capability represents a substantial leap in addressing scalability for quantum systems, moving beyond the sequential control methods that previously limited performance. The ability to operate on a larger qubit space in parallel is critical for realizing the full potential of quantum algorithms, particularly those designed to tackle complex optimization and simulation problems. The hardware-based halt-resume protocol, achieving a restart time of 80 nanoseconds after a mid-circuit measurement, is crucial for enabling rapid iteration in hybrid quantum-classical programs. This speed is essential for minimizing latency and maximizing the efficiency of algorithms that require frequent communication between the quantum processor and classical control
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quantum-computingPhD proposal in energetic cost of fault-tolerant quantum computing
PhD proposal in energetic cost of fault-tolerant quantum computing Application deadline: Sunday, July 26, 2026Employer web page: https://recrutement.inria.fr/public/classic/en/offres/2026-10236Job type: PhDTags: #PhD #quantum computing #energy #fault-tolerance #quantum error-correction #power #energetics #noise #correlated-noise #scalability #theory #PhDThe MOCQUA team at the Loria laboratory in Nancy (France) is looking for a PhD student in quantum computing theory. More details about the offer and platform to apply is provided in the link The goal will be to analyze how the energy consumption of fault-tolerant quantum computers scales as a function of the size of quantum algorithms, in a regime where the computation is specifically optimized to minimize energy consumption rather than qubits or gates counts. The main objective will be to determine whether better energy scaling than that predicted by the quantum threshold theorems [1,2] can be achieved, following the approaches developed in [3,4]. In practice, the PhD student will mostly focus on fault-tolerant quantum computing theory, and interact with other researchers providing the hardware energetic and noise models. Because such models can introduce correlated noise, this project will indirectly help understanding how to better design fault-tolerant circuits to resist such noise. To design more resource-efficient and noise-resilient fault-tolerant circuits, the PhD might use tools from diagrammatic reasoning for quantum circuits currently developed in the group [5], as well as recent developments in fault-tolerant circuit transformations [6]. =============================================== This project will be supervised by Marco Fellous-Asiani (Starting faculty at INRIA Université de Lorraine; expert in energetics of fault-tolerant quantum computing [3,4]), Simon Perdrix (Research director at INRIA Université de Lorraine; expert in diagrammatic reasoning for quantum circuits [5]), and invo
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quantum-computingUniversal purification dynamics of monitored Clifford circuits
--> Quantum Physics arXiv:2607.06683 (quant-ph) [Submitted on 7 Jul 2026] Title:Universal purification dynamics of monitored Clifford circuits Authors:Beatrice Magni, Federico Gerbino, Xhek Turkeshi, Andrea De Luca View a PDF of the paper titled Universal purification dynamics of monitored Clifford circuits, by Beatrice Magni and 3 other authors View PDF HTML (experimental) Abstract:Quantum circuits under sufficiently weak monitoring purify on a timescale $T_P$ exponentially long in the system size. This slowness underlies a universal purification dynamics, whose quantitative description has so far required the replica trick, with a delicate analytic continuation. We show that monitored Clifford circuits on $L$ qudits of prime dimension $q$ bypass this construction entirely: in the scaling limit at fixed $x = t/T_P(L)$, purification reduces to the Markovian decay of the density-matrix rank, an exactly solvable death process descending from infinity. We compute the full scaling functions in compact form: all Rényi entropies collapse onto a universal curve $\langle S(x) \rangle$. Exact stabilizer simulations at $q=2,3,5$ confirm the predictions, with no fitting parameter for the global model and $T_P$ as the only fitted scale for local brick-wall circuits. Also, the replica problem amounts to a tilted version of the same Markov process, in agreement with exact computations from the Clifford commutant. Finally, the quantization of the rank leaves two hallmarks that distinguish Clifford dynamics from generic monitored circuits: the entropy fluctuations saturate at short scaled times $x\to0$ to an $O(1)$ variance, instead of vanishing, and observables develop a temporal modulation periodic in $\log_q x$, which cannot be captured by the replica approach. Comments: Subjects: Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech) Cite as: arXiv:2607.06683 [quant-ph] (or arXiv:2607.06683v1 [quant-ph] for this version) https://doi.org/10.48550/ar
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quantum-computingFeynman's clock and hierarchy-informed sampling for quantum error mitigation
--> Quantum Physics arXiv:2607.06752 (quant-ph) [Submitted on 7 Jul 2026] Title:Feynman's clock and hierarchy-informed sampling for quantum error mitigation Authors:Theo Saporiti View a PDF of the paper titled Feynman's clock and hierarchy-informed sampling for quantum error mitigation, by Theo Saporiti View PDF HTML (experimental) Abstract:Near-term physical implementations of quantum algorithms require efficient quantum error mitigation schemes to reduce quantum noise. In this letter we propose a new mitigation technique, by extending the applicability of our BBGKY-ISM scheme from quantum simulations of spin chains to arbitrary quantum circuits. We map executions of quantum circuits using Feynman's clock Hamiltonian to the Hamiltonian dynamics of a corresponding quantum system, whose time evolution obeys a BBGKY-like hierarchy of equations informing the BBGKY-ISM mitigation. We show that the method's classical and quantum overheads are polynomial in the circuit size and in the number of qubits. We apply our method to numerical simulations of tunable Bell state preparation circuits under state-of-the-art quantum noise, and numerically demonstrate its systematic and controllable quantum error reduction capability. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2607.06752 [quant-ph] (or arXiv:2607.06752v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2607.06752 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Theo Saporiti [view email] [v1] Tue, 7 Jul 2026 19:33:58 UTC (421 KB) Full-text links: Access Paper: View a PDF of the paper titled Feynman's clock and hierarchy-informed sampling for quantum error mitigation, by Theo SaporitiView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2026-07 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX cita
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quantum-computingA quantum model for synchronizing finite state transition systems
--> Quantum Physics arXiv:2607.06953 (quant-ph) [Submitted on 8 Jul 2026] Title:A quantum model for synchronizing finite state transition systems Authors:Martin Lukac, Khaled El-Fakih, Uraz Turker View a PDF of the paper titled A quantum model for synchronizing finite state transition systems, by Martin Lukac and 2 other authors View PDF Abstract:We propose a quantum model for finding a resetting input sequence (RS) which can take a finite state transition system (FA), to particular state independent of its current state. The complexity of finding such sequences for various types of FA can be NP-Hard or even PSPACE-Complete. To this end, we represent the FA states, inputs, and transition function in quantum space. Accordingly, we propose a model to represent the execution of an input sequence of a particular length $l$ starting form an initial FA state. The model is extended considering the application in superposition of all input sequences of length $l$ to an initial state of the FA. The model is further extended considering the application of all input sequences to all initial states of the FA capturing for every input sequence the collection (ordered list) of states reached by applying the sequence to all states of the FA. The amplitude amplification algorithm is then used as it combines similar collections of reached states while preserving all input sequences that reach these collections. A Grover search for a reached collection where its elements correspond to the same FA state provides a RS for the FA. Our approach offers a quadratic gain over the exponential complexity of traditional brute-force method, which is the only method that can be applied to a general FA class. As a proof of concept we provide results of several simulated FAs on a quantum simulator. Comments: Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET) ACM classes: D.2.5; F.1.1; F.2.1; I.1.2; J.6 Cite as: arXiv:2607.06953 [quant-ph] (or arXiv:2607.06953v1 [quant-
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quantum-computingPostdoctoral Fellow in “Deploying geometrically-designed algorithms”
Postdoctoral Fellow in “Deploying geometrically-designed algorithms” Application deadline: Saturday, December 12, 2026Employer web page: https://ntu.wd3.myworkdayjobs.com/Careers/job/NTU-Main-Campus-Singapore/Research...Job type: PostDocMarek Gluza is starting a group at NTU Singapore and is looking for input from a postdoc. The group will work on the entire quantum computing stack a) geometrically designed quantum algorithms, b) deploying them, c) improving quantum hardware. The formal announcement: https://ntu.wd3.myworkdayjobs.com/Careers/job/NTU-Main-Campus-Singapore/... Conversation starters: https://marekgluza.github.io/ Members of underrepresented groups are particularly encouraged to inquire early and e.g. provide a cover letter to contextualize their pathway. Competence and role fit will be evaluated by: - Form of advances communicated in your lead author publications and the breadth of your portfolio. - Your GitHub repositories. - Your ability of critical thinking, clear communication, mentoring younger peers, thought leadership and individual unique technical strengths. Log in or register to post comments
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quantum-computingMillisecond coherence times in gigahertz-frequency mechanical oscillators
MainLong-lived phonons are a compelling resource, as they permit numerous quantum operations within their coherence time, which enables high-performance quantum sensors1,2,3,4, transducers5,6,7,8 and memories9,10,11. The efficient control of long-lived phonons using optomechanical12,13,14, electromechanical15,16 and superconducting qubit systems17,18,19 has generated renewed interest in phononic device physics and technologies for quantum applications20,21. Although various mechanical oscillators have produced such long-lived phonons over a range of frequencies21,22, high-frequency (gigahertz) phonons are often desirable, as they have improved immunity to unwanted noise, permit ground-state operation at cryogenic temperatures and are more readily controlled using quantum optics and circuit quantum electrodynamics techniques21,23. In theory, crystalline media are ideal for hosting such long-lived phonons, as they have vanishing internal dissipation at cryogenic temperatures24,25,26,27. However, it has proven difficult to extend the coherence times of such gigahertz-frequency crystalline oscillators to millisecond timescales.Silicon-based nanomechanical phononic crystal resonators have shown long phonon lifetimes (>1 s) at gigahertz frequencies; however, strong coupling between phonons and the two-level system limits their coherence times to ~100 μs (refs. 10,11,14). Tight phonon confinement and strong boundary reflections within these systems make them vulnerable to complex surface interactions that may contribute to excess noise and dephasing14. Alternatively, micro-fabricated high-overtone bulk acoustic-wave resonators (μHBARs) of the type seen in Fig. 1a support gigahertz-frequency phonon modes with orders of magnitude lower surface participation13,28. In principle, lower surface participation could translate to lower dephasing rates and longer coherence times. However, in practice, μHBARs have yielded modest coherence times (<1 ms)13,23, shorter than can be
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quantum-computingWu and Colleagues Introduces Cyclic Control Strategy for Fast CZ Gate Fidelity
A new cyclic control strategy overcomes the trade-off between gate speed and accuracy in quantum computing. Ze-An Zhao and colleagues expand the parameter space for pulse design, enabling strong suppression of coherent errors in a superconducting controlled-Z gate. The average error reduces from 0.27% to 0.12% without extending gate duration. This advancement provides a general pathway towards achieving both fast and high-fidelity quantum gates, representing a key step towards scalable quantum computation. Restored qubit controllability enables high-fidelity, fast superconducting controlled-Z gates Error rates dropped to 0.12%, a significant reduction from 0.27% in superconducting controlled-Z gates, representing a major leap in quantum gate fidelity. The team at University of Science and Technology of China achieved this improvement without increasing gate duration, surpassing the conventional speed-fidelity trade-off which previously demanded slower gate speeds for higher accuracy. By addressing distortions in control pulses, tiny imperfections disrupting precise qubit operation, they expanded the range of adjustable parameters during gate operation, effectively restoring controllability. A novel cyclic control strategy provides a general pathway towards building faster and more reliable quantum computers, circumventing a key limitation in current superconducting quantum circuit designs. Validation of the approach used cross-entropy benchmarking, a method for assessing quantum gate performance by measuring preservation of quantum information. This revealed a reduction in coherent errors, stemming from imperfections within the quantum system, and successful suppression of leakage, unwanted transitions to unintended quantum states, alongside phase errors, all without extending gate operation duration, a critical step towards practical quantum computation. The team discovered that short-term distortions in control pulses disrupt the time-reflection symmetry of wavefo
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quantum-computingUniversity of Augsburg Team Designs Valence Bond Embeddings for Deep Chemistry Simulations
Scientists at the University of Augsburg have developed a new methodology addressing a fundamental challenge in quantum chemistry: the accurate and efficient simulation of large molecular systems. Francisco Javier del Arco Santos and Jakob S. Kottmann have combined hybrid Fermionic-Bosonic encodings with Quantum Valence Bond Theory to construct quantum circuits capable of representing more complex molecules than previously achievable, offering a potential pathway towards resolving bottlenecks in quantum computation and expanding the scope of variational quantum eigensolvers. Hybrid encoding and Quantum Valence Bond Theory expand accessible molecular simulation scales A six-fold increase in the size of molecular systems simulated using variational quantum eigensolvers has been demonstrated, significantly exceeding the limitations inherent in traditional active space methods. Published on June 26, this advancement facilitates the simulation of chemically relevant systems that were previously intractable due to computational constraints and the inherent limitations of current quantum hardware. Conventional quantum chemistry calculations often struggle with molecules containing more than a few dozen electrons, owing to the exponential scaling of computational resources with system size. The University of Augsburg researchers overcame this hurdle by strategically combining hybrid Fermionic-Bosonic encodings with Quantum Valence Bond Theory to systematically construct quantum circuits, establishing a clear and direct relationship between the chosen encoding scheme and the resulting electronic structure representation. This allows for a more nuanced and controlled approach to quantum simulation. Quantum circuits now provide novel avenues for simulating molecular properties, circumventing the limitations of existing techniques and opening possibilities for more intricate chemical investigations. The core innovation lies in achieving a more compact and flexible representatio
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quantum-computingMax Planck Institute for the Science of Light: Researchers Use AI to Design Improved Quantum Error Correction Codes
A new method for building quantum error-correcting codes, key for unlocking the potential of quantum computation, has been devised by Zidu Liu and Florian Marquardt at the Max Planck Institute for the Science of Light, in collaboration with Friedrich-Alexander University and 1Max Planck Institute. Their work presents structured concept evolution (SCE), a framework using large language models to discover families of quantum low-density parity-check (qLDPC) codes. The method moves beyond traditional, challenging discrete design problems by evolving algebraic specifications alongside executable programs, resulting in the identification of competitive code families, including those utilising non-abelian groups, and demonstrating the potential of artificial intelligence in advancing quantum information science. Discovery of efficient quantum error correction via language model guided code search A reduction in the cost per logical qubit by roughly an order of magnitude has been achieved compared to the surface code, overcoming a key obstacle to scalable quantum computation. Previously, the surface code’s quadratic overhead in physical qubits limited progress as quantum platforms approached the thousand-qubit threshold; this new advancement enables constant-rate codes, offering a pathway to fault-tolerant computation with sharply reduced resource requirements. At the Institute for the Science of Light, in collaboration with Friedrich-Alexander University, scientists developed structured concept evolution (SCE), a framework combining large language models with algebraic mutation grammar to discover lifted-product code families, a type of quantum low-density parity-check (qLDPC) code. Quantum error correction is crucial because qubits, the fundamental units of quantum information, are inherently susceptible to noise and decoherence, leading to errors in computation. Without effective error correction, even small error rates would quickly overwhelm quantum algorithms, render
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quantum-computingOhio University Team Proposes Bivariate Bicycle Codes for Low-Overhead Error Correction
Current quantum computers are susceptible to errors which impede network performance, and existing correction techniques demand many qubits to function effectively. Alejandro Rosales and Animesh Yadav at Ohio University have developed a method to improve the reliability of QCNNs, a type of computer program that combines the strengths of quantum processing with image recognition techniques, similar to how facial recognition software works. Quantum computers are prone to errors, hindering the performance of these networks; existing error correction methods require a substantial number of qubits, creating a key obstacle to progress. This new technique employs a distance-4 code, offering a constant encoding rate and linear code distance, and represents a step towards practical quantum machine learning. Bivariate bicycle error correction enables substantial gains in quantum convolutional neural Previously, such networks failed to converge at all without error correction. This advancement addresses the vital issue of noise affecting near-term quantum devices, severely limiting the performance of QCNNs, and also reduces the substantial qubit overhead associated with established methods like surface codes. Integrating a constant-overhead QEC protocol with QCNNs provides a viable path towards practical quantum machine learning applications. The error threshold of 0.3% allows for sustained performance even with additional qubit requirements for error correction. Simulations utilising realistic noise sources demonstrated the BB code’s ability to maintain a constant encoding rate and linear code distance, essential for scaling to larger QCNNs; the team also benchmarked their approach against a feed-forward neural network used for error correction. Bivariate bicycle codes enhance stability in near-term quantum convolutional neural networks Quantum convolutional neural networks promise potential speedups for complex tasks like image recognition, but their inherent instability of
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quantum-computingJin and Colleagues Develop Learning-Based Methods for Quantum Sensing and Networking
A thorough review of the increasingly intertwined fields of artificial intelligence and quantum information has been completed by Min Chen of University of Pittsburgh and colleagues. The review details how AI acts as a set of tools for advancing quantum system learning, design, control, and verification, whilst quantum information presents new computational models and learning paradigms for AI development. This survey organises recent advances around key tasks including information extraction from limited measurements, quantum algorithm training and discovery, hardware stabilisation, workflow automation, and the extension of learning methods to sensing and networking. Furthermore, the work examines the impact of quantum computation and quantum-inspired structures on learning, considering algorithmic speedups, expressivity, and neural-network design, highlighting the vital need for integrated theory, experiment, and hybrid quantum-classical systems to enable overcoming challenges in reproducibility and scalability. Using tensor networks for advances in quantum and machine learning Tensor-network representations proved central to enabling these advances, functioning as a way of organising complex data into a network of interconnected nodes, similar to how a family tree shows relationships between individuals. Data represented in this interconnected format reduced the computational burden of processing high-dimensional information, a key challenge in both quantum simulations and advanced AI algorithms. These networks were used to model the intricate connections within quantum states and machine learning models, allowing for more efficient computation and analysis. The fields of artificial intelligence and quantum information are becoming increasingly intertwined. A recent survey details progress in using AI to improve quantum systems, focusing on tasks like interpreting limited measurements and training quantum algorithms. The team also examined how quantum computation
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quantum-computingBTQ Technologies Finalizes Full Acquisition of QPerfect to Establish Unified Quantum Infrastructure and Security Ecosystem
BTQ Technologies Finalizes Full Acquisition of QPerfect to Establish Unified Quantum Infrastructure and Security Ecosystem BTQ Technologies Corp. (Nasdaq: BTQ | Cboe CA: BTQ) has officially finalized its full acquisition of French quantum software developer QPerfect SA. Following the receipt of regulatory foreign direct investment (FDI) clearance from the French Ministry for the Economy and Finance, BTQ exercised its definitive option to absorb the remaining outstanding securities of the Strasbourg-based deep-tech startup, rendering QPerfect a wholly owned corporate subsidiary. The transaction integration links BTQ’s existing post-quantum cryptography (PQC) validation structures with QPerfect’s specialized hardware modeling, software emulation, and automation frameworks to deliver a combined, quantum-ready network architecture. [ BTQ - QPerfect Transaction Close Matrix ] Subsidiary Status ──► QPerfect SA finalized as a wholly owned subsidiary of BTQ Technologies Corp. Regulatory Baseline ──► Executed under the June 18, 2026 Prospectus and French FDI sovereign mandates. Core Software Stack ──► MIMIQ™ quantum emulator, Digital Twin modeling, and Quantum Logical Unit (QLU). Integration Mandate ──► Hardening defense, telecom, and critical infrastructure against quantum-enabled risks. The completion of the acquisition allows BTQ to directly monetize and deploy QPerfect’s three proprietary software pillars into industrial networks requiring post-quantum transition verification. The primary layer, MIMIQ™, functions as a high-density software emulator capable of running stable 100+ qubit circuit simulations on conventional classical computing systems to benchmark next-generation Transport Layer Security (TLS) handshakes and stress-test PQC protocol resilience under severe network overhead. This is paired with the Digital Twin framework, which generates software-based structural representations of neutral-atom processors to optimize physical layouts prior to cleanroom fabric
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quantum-computingJin and Colleagues Designs Quantum Circuit for Simulating Incompressible Stokes Flow
A new quantum algorithm tackles the key computational challenges of simulating incompressible Stokes flow, a process vital for understanding microfluidics and low-Reynolds number hydrodynamics. Shi Jin of the University of Science and Technology of China and colleagues use the Schrödingerisation technique and artificial compressibility to reduce the costs of high-dimensional simulations. Their approach designs an explicit quantum circuit encoding the regularised system, showing an exponential speedup in problem dimensionality and validating the method through numerical simulations on Qiskit. The algorithm offers a promising pathway towards efficient simulation of complex fluid dynamics using quantum computation. Quantum algorithm circumvents dimensionality limitations in Stokes equation modelling The developed quantum algorithm achieves an exponential speedup in problem dimensionality, a significant contrast to previous methods hampered by the curse of dimensionality in high-dimensional Stokes equation simulations. This breakthrough overcomes classical computational constraints, enabling the modelling of fluid dynamics in scenarios previously intractable due to the exponential growth of required resources with increasing dimensions. The computational cost of classical methods for solving the Stokes equations scales poorly with dimensionality, often requiring resources that grow exponentially with the number of dimensions. This limitation severely restricts the ability to model complex systems accurately. Combining Schrödingerisation with artificial compressibility, researchers designed an explicit quantum circuit to efficiently encode the regularised system, offering a unified framework for solving these complex equations. Schrödingerisation, a technique borrowed from quantum mechanics, transforms the partial differential equation into a time-dependent Schrödinger equation, allowing it to be solved using quantum algorithms. The artificial compressibility method intr
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quantum-computingGoogle Cuts Surface Code Error Rate to 7.72 × 10−4 With RL
Google Quantum AI researchers report achieving a logical error rate of 7.72 × 10−4 using the surface code, a crucial step toward stable quantum computation. The team unified calibration with computation by repurposing quantum error detection events, typically used for correction, as a learning signal for a reinforcement learning agent. This allows the quantum computer to continuously adjust its control parameters during computation, improving the logical stability of the surface code 3.5-fold against injected drift with complementary decoder steering. Numerical simulations suggest this framework’s optimization speed remains consistent even as quantum codes scale to include over a thousand control parameters, potentially enabling larger, more powerful machines. The researchers state that this work “enables a new paradigm: a quantum computer that learns from its errors and never stops computing.” A logical error rate of 7.72 × 10−4 achieved with the surface code demonstrates a significant advance toward stable quantum computation. Researchers at Google Quantum AI and Google DeepMind unified the processes of calibration and computation to reach this milestone. The core innovation lies in giving the quantum error correction process a dual role: not only correcting the quantum state, but also teaching a reinforcement learning agent to stabilize the system. This framework was experimentally demonstrated on a Willow superconducting processor, improving the logical stability of the surface code 3.5-fold with complementary decoder steering. Beyond surface codes, the team also achieved an average logical error rate of 8.19 × 10−3 with the color code. The reinforcement learning agent manages over a thousand control parameters, which specify how an abstract quantum error correction circuit translates into analog waveforms controlling the quantum system. Numerical simulations reveal the optimization speed of this reinforcement learning framework is independent of system size, su
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quantum-computingBenhemou and Colleagues Designs Automated Framework for Inter-Code Logical CNOT Synthesis
Scientists at Quantinuum have developed a new automated framework that establishes connections between diverse quantum error-correcting codes, addressing a fundamental challenge in the construction of practical, large-scale quantum computers. Asmae Benhemou and Noah Berthusen, from Quantum AI, present a system utilising chain maps to generate logical CNOT circuits between arbitrary CSS codes, resolving limitations encountered when integrating different code families. The approach not only rediscovers established connections between codes but also identifies new, low-depth solutions, potentially improving the efficiency of operations such as code switching and Pauli product measurements in heterogeneous quantum architectures. Automated framework enables low-depth connections between arbitrary quantum error correction Quantinuum researchers achieved a five-fold reduction in the complexity of connecting disparate quantum error correction codes, moving from circuits requiring a depth of ten to those with a depth of two in certain instances. Their automated framework, utilising ‘chain maps’, now enables logical CNOT circuits between arbitrary CSS codes, a key step towards building more flexible quantum computers. CSS codes, named after Calderbank-Shor-Steane, are a prominent class of quantum error-correcting codes defined by their structure relating to classical error-correcting codes. The ability to perform logical operations, such as the CNOT gate, between different CSS codes is crucial for modular quantum computation and fault-tolerant quantum information processing. Previously, such connections were largely limited to structurally related code families, hindering the development of heterogeneous quantum systems. The new method not only replicates established connections but also uncovers novel, low-depth solutions, including those preserving or partially preserving error detection capabilities, and can extend these to full code distance with additional measurements.
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quantum-computingRigetti's Quantum Reality: Delays, Low Revenue, And An Unjustified Premium
Melissa Tucker1.5K FollowersFollow5ShareSavePlay(6min)Comments(2)SummaryRigetti Computing faces persistent delays in scaling its superconducting qubit technology, with milestone slippages and underwhelming fidelity improvements.RGTI maintains a robust balance sheet ($570M cash, no debt), but continues to burn ~$20M per quarter with limited revenue visibility and no new meaningful contracts.Despite a $100M Department of Commerce LOI, funding is not the constraint; commercial traction remains weak, with recent contracts appearing as one-offs.RGTI’s premium valuation (248–310x PS) appears unjustified without revenue growth or technical breakthroughs, risking multiple compression toward peer levels. Just_Super/iStock via Getty Images I have covered Rigetti Computing (RGTI) before, where I outlined the company’s background in detail, explained why I didn’t understand all the excitement about the company, and why I considered it a sell. Since the This article was written byMelissa Tucker1.5K FollowersFollowWith a professional background spanning multiple industries, from ecnomocis to logistics and construction to retail, I bring a diverse perspective to investing. My international education and career experiences have provided me with a global outlook and the ability to analyze market dynamics from different cultural and economic perspectives. I have been actively investing for over a decade, honing a strategy that focuses on cyclical industries while maintaining a diversified portfolio that includes bonds, commodities, and forex. My interest in cyclical sectors stems from their potential for significant returns during periods of economic recovery and growth. However, I also recognize the importance of balancing risk, which is why I incorporate fixed-income investments (long or short).Analyst’s Disclosure: I/we have a beneficial long position in the shares of IONQ, INFQ either through stock ownership, options, or other derivatives. I wrote this article myself, and it expr
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quantum-computingQuantum Software Lab: £6.2M Funds Quantum Cybersecurity for UK Energy Networks
£6.2 million has been awarded to the University of Edinburgh’s Quantum Software Lab to address growing cybersecurity vulnerabilities within the UK energy sector as quantum computing capabilities advance. The project, titled Network Security in a Quantum Future, will deliver the open-source Quantum Threat Tracker, a tool designed to estimate when existing energy systems will become susceptible to quantum attacks, shifting from reactive to proactive security measures. Collaboration is central to the effort, with Scottish Power Energy Networks and National Gas working alongside the University to prepare for these emerging risks. “By combining expertise in quantum computing, uncertainty quantification and energy systems, this project will provide evidence-based tools to support a secure and cost-effective transition to a post-quantum future,” says Dr. Petros Wallden, Deputy Director of Research at the Quantum Software Lab. While current computers would take millions of years to crack certain complex codes, quantum computing promises to dramatically reduce that timeframe, creating a significant cybersecurity threat that demands proactive mitigation. This project, formally known as Network Security in a Quantum Future, moves beyond theoretical risk assessment and into the development of practical tools for energy companies. Complementing this is the Quantum-Aware Risk Management tool, intended to support strategic planning for the adoption of quantum-safe technologies across a broad range of energy assets and prioritize future protective measures. The initiative builds upon earlier research identifying potential vulnerabilities and mitigation strategies, now progressing to deliver operational tools for network operators. Professor Chris Dent added, “In addition to the importance to the energy system of maintaining cyber security in the post-quantum world, our work on the consequences of uncertainty in technology projections is an exciting technical challenge, which we are
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