#quantumcircuits

Overview

This study proposes a universal method for extracting work from quantum systems without prior knowledge of the quantum state. Helmholtz free energy suggested that maximum work was feasible only if the experimenter had a complete classical description of the input. The authors demonstrate that Schur-Weyl duality and the symmetry of numerous state copies can maximize efficiency in a state-agnostic method. This innovation removes practical barriers by avoiding quantum state tomography's high energy and resource expenses.

The work applies these conclusions to infinite-dimensional systems, which is important for bosonic quantum computing. These results show that work extraction in the asymptotic limit is not limited by knowledge.

Researchers Get Energy from the Unknown

The University of Tokyo found that energy may be efficiently gathered from quantum systems even when the user is unaware of the system's state, pushing physics and information constraints. Kaito Watanabe and Ryuji Takagi tackle a quantum thermodynamics "information bottleneck" with their study.

Information Tax on Quantum Energy

Quantum thermodynamics seeks to maximize work from tiny systems. Physics has long considered the system's Helmholtz free energy the "gold standard" for this job. However, an experimenter needs a comprehensive classical description of the quantum state to reach this limit. These are called “state-aware” situations.

This is a major challenge in real life. Quantum states cannot be fully described since they are often the result of complex processes like deep quantum circuits or random noise. To map the state, “quantum state tomography” is theoretically possible but unproductive. Tomography employs many copies of the state to identify its identity, reducing the “work per copy” gathered. Tomography system measurement may be more laborious than the system itself.

A Global Approach

Watanabe and Takagi invented a “universal work extraction protocol”. Unlike other protocols, this one's description is input-independent. They found that the methodology yields the same optimal work extraction rate, or free energy, even without previous information, as if the experimenter knew the condition. Researchers found that having information about the state does not affect the optimal performance of asymptotic work extraction. This removes a fundamental operational barrier that has limited quantum technology for ten years.

The “Schur Pinching” Secret

Researchers say “Schur pinching,” a new technique, made this discovery possible. Schur-Weyl duality, which emphasizes the permutation symmetry of multiple copies of the quantum system, is used instead of directly measuring the unknown state.

When an unknown quantum state is simplified into a “classical” diagonalized form to extract energy, critical “coherence,” which gives quantum systems their potency, is lost. The protocol can diagonalize without wasting free energy via Schur pinching. By treating systems as a symmetrical collective, researchers can maintain the ideal work rate as the number of systems approaches infinite.

Three steps comprise the protocol:

Diagonalization is using the Schur pinching channel to preserve the system's intrinsic symmetry. Learning: Estimating relative entropy by “type measuring” a small, sublinear proportion of systems. That estimate is used to extract work and charge a quantum battery or "work storage" gadget.

Redefining Maxwell's Demon

The researchers also discussed Maxwell's Demon, a famous paradox. Knowing the system state allows an agent to extract work from heat in the standard demon experiment. In their study, Watanabe and Takagi say “knowledge doesn’t matter,” although they stress that the two conditions are different. This study examines the system's density matrix, or "blueprint," whereas Maxwell's Demon determines which state has been realized.

TII Quantum

As the UAE becomes a global hub for sophisticated technology, the Technology Innovation Institute (TII) released its first cloud-based quantum computing service. This milestone allows outside partners unprecedented access to superconducting Quantum Processing Units (QPUs) constructed in Abu Dhabi's center.

According to TII, the Advanced Technology Research Council's (ATRC) applied research arm, the Quantum Research Center has moved from researching basic technology to providing physical quantum infrastructure. By letting customers run quantum workloads on the cloud, TII is ushering in a new era of regional and worldwide quantum cooperation.

Journey to Hardware Sovereignty Over Four Years

Four years of intense development ended with the cloud service's launch. Since launching the Quantum Computing Hardware Lab, the team has gone from building institutional capabilities to creating cloud-accessible, fully integrated solutions.

QPU systems with 5–25 qubits underpin this offering. Most notably, these systems use local chips. TII specialists say these in-house components have quantum coherence durations ten times longer than the lab's first-generation prototypes. Instead of using off-the-shelf components, TII's aim to create knowledge across the quantum stack—design, fabrication, and system-level integration—led to this performance gain.

Bridge Hardware and Software with Qibo

To simplify use for partners, TII coupled the hardware with its open-source software platform, Qibo. Qibo, developed by the Quantum Middleware team and the hardware lab, is essential for task submission and complex workflows.

The Qibo allows researchers to:

Build complicated quantum circuits.

Create hybrid quantum-classical workflows, the best way to gain near-term quantum advantage.

A unified interface lets you run jobs on simulators and hardware QPU backends.

TII provides its partners with the smoothest transition from theoretical algorithm development to hardware implementation by standardizing on Qibo.

Ambition and Pace in Leadership

The unveiling was lauded by TII leadership as proof of UAE technological progress. TII Quantum Research Centre Chief Researcher Dr. Leandro Aolita stressed the strategic importance of this change.

Four years after the lab opened, Dr. Aolita stated, “launching a cloud-accessible QPU service shows both the pace and ambition of a quantum program.” He said the new model provides a “practical platform to accelerate experimentation and hybrid quantum-classical development on locally developed infrastructure,” which was previously only available to TII's Quantum Algorithms team for validation and benchmarking.

Global Quantum Context: Industry Busy February The global quantum ecosystem is active during TII's cloud service debut. The TII announcement and other major events showed a rapid pace.

Commercializing quantum technology:

Corporate Restructuring: Xanadu appointed four experienced international business executives to its Board of Directors, indicating a move toward more sophisticated corporate governance.

Public Markets: By combining with Real Asset Acquisition Corp., IQM Quantum Computers might go public and raise funds for hardware expansion.

National Investment: The Australian government promised more funding for quantum technology demonstration projects to bridge the gap between lab research and commercial application.

Quantum Security: Ameritec IPS introduced a biometric wallet and quantum-resistant QAmChain to meet post-quantum cryptography need. Quantonation created a €220 million fund to invest in mistake correction and quantum infrastructure.

TII's cloud transfer is part of a global competition to provide reliable, easily accessible quantum computing resources, not an isolated incident.

Considering the Future

A long-term plan begins with this launch. The service will increase, with TII plans to launch:

Upgraded qubits and system performance. Frequent system and hardware updates to meet international requirements.

Partners can gain access as the Abu Dhabi quantum ecosystem grows.

TII is becoming a quantum research powerhouse by providing cloud-based hardware access. The institute's focus on domestically produced systems, from chip fabrication to middleware operation, gives the UAE strong technological sovereignty in an area that is increasingly recognized as a cornerstone for future economic success and national security.

A new collaborative endeavor is making the search for “quantum advantage” a public, high-stakes relay race in a world where technical discoveries often emerge privately. The Quantum Advantage Tracker, launched recently by luminaries in the field, is the core arena where quantum and classical computing approaches are in a “neck-and-neck” race few predicted this early. According to IBM Quantum researchers Jay Gambetta and Robert Davis, this open-source project is tracking progress and influencing quantum scientific validation.

Classiq Quantum computing

AMD, Classiq, and Comcast successfully tested quantum algorithms to improve internet routing. This project determined the optimal backup routes to avoid service interruptions during hardware faults or maintenance to boost network resilience. The team employed AMD Instinct GPUs to perform quantum computations and high-scale simulations to overcome technical limitations. This hybrid technique allowed the researchers to solve complex combinatorial optimization problems, which are difficult for traditional computers as networks grow. The project's ultimate goal is to demonstrate that quantum software can meet modern digital infrastructure demands.

In the experiment, Classiq, the leading quantum software company in Israel, and the Philadelphia telecom giant are working together. Finding autonomous backup paths for network sites during maintenance and change management is a fundamental topic in modern network architecture.

Modern Internet Complexity Solution

Global networks become tougher to maintain as they grow. Comcast aims to effortlessly shift traffic without inconveniencing consumers if one network site goes offline for repair and another breaks at the same time. Finding fast, reliable, and low-latency backup paths is a combinatorial optimization problem with a huge search space.

Comcast Connection and Platforms Chief Network Officer Elad Nafshi remarked, “Our customers want simple connectivity that is fast, secure, and dependable.” He noted that while the goal is simple, modern networks' scale and dynamic nature make execution complex. The trial proved quantum computing for network optimization is viable and scalable, Nafshi said.

Combining Quantum and Classical High-Performance Computing

A combination method of high-performance classical computers and quantum approaches made the experiment successful. In accelerated simulation situations, researchers used AMD Instinct GPUs to achieve qubit scale computing. This allowed algorithm behavior validation and fast iterations at a scale beyond quantum technology.

AMD's corporate vice president of Compute and Enterprise AI Products, Madhu Rangarajan, said computing will merge quantum and traditional systems. By supporting quantum execution with high-performance conventional goods, the alliance showed how these two technologies may solve real-world operational difficulties.

Classiq Quantum Software Function

Classiq provided the engineering support and tools needed to describe these complex difficulties, which drove algorithmic progress. Classiq's platform generates hardware-ready, efficient quantum circuits using AI-driven coding and Qmod, a high-level modeling language.

Corporate quantum research and development requires repeatable methods and the ability to simulate on GPUs and quantum hardware, according to Classiq co-founder and CEO Nir Minerbi. He added this cooperation shows how teams may model and explore solutions to difficult problems while retaining portability as the quantum environment develops.

Classiq is a category leader and a 2025 Fast Company “Next Big Thing in Tech”. The company works with BMW, Rolls-Royce, and Deloitte to solve industrial problems using quantum technologies with the help of SoftBank, AMD, Qualcomm, and HSBC.

To the Future: Quantum Ecosystem

This study affects more than telecoms. Classiq works with NVIDIA, Rolls-Royce, and Citi on quantum applications such computational fluid dynamics and financial portfolio optimization. Healthcare, automotive, and finance are also studying quantum solutions, and “Energy & Networks” joined them after Comcast's trial success.

Overview

Researchers created the statistics-informed parameterized quantum circuit (SI-PQC) to solve the problem of transforming real-world data into quantum states. This method uses the maximum entropy concept to incorporate known statistical patterns into a set circuit design, reducing the need for complex data pre-processing. For advanced statistical modeling and machine learning, this strategy saves exponential resources while building probability distributions.

Beyond theoretical advances, the SI-PQC improves variational learning by optimizing the training space and ensuring interpretability. Practical testing in financial derivative pricing and risk assessment shows its value for time-sensitive, data-heavy enterprises. This idea is versatile and helps bridge the gap between quantum computing's promise and realistic, extensive applications.

SI-PQC bridges quantum computers and real-world data

A new method for transforming complex real-world data into a format quantum computers can process is a major step toward making quantum computing practical for industrial use. The Statistics-Informed Parameterized Quantum Circuit (SI-PQC) reduces the time and energy needed to prepare quantum states for sophisticated computations.

Overcoming the “Preparation” Issue

Quantum computing is touted for its potential to solve problems beyond supercomputers, although this is limited by quantum state preparation. Quantum algorithms need “encoding” real-world statistical data into a quantum state. This has traditionally required extensive pre-processing and computational resources.

Scientists from Origin Quantum Computing and the University of Science and Technology of China said preparing these states from “real-world data remains a critical challenge.” SI-PQC uses the maximum entropy principle to exploit data statistical symmetry.

Increasing Efficiency using Symmetries

SI-PQC's main innovation is its fixed-structure circuit with changeable settings for encoding prior data. SI-PQC uses the maximum entropy principle to create a more flexible and effective framework than previous methods that required complex circuits for each dataset.

The results are noteworthy. The study found that constructing “mixture models,” essential for machine learning and statistics, can save exponentially. The researchers created a “versatile and resource-efficient subroutine” that can be plugged into quantum algorithms to eliminate data pre-processing.

Transforming Healthcare and Finance

This discovery has broad applications outside the lab. SI-PQC showed “substantial improvements in end-to-end quantum resource efficiency” in various high-stakes domains.

Financial Services: Online risk assessments and financial derivatives pricing numerical experiments used SI-PQC. Quantum computers may help banks to manage risk in real time with unprecedented accuracy by simulating market distributions faster and more precisely.

Machine Learning: “Variational learning within an optimally dimensioned training space” increases quantum model learning and generalization from new data. This aids online machine learning, which processes data continuously. The abstract claims that SI-PQC's efficiency makes it a good choice for medical diagnostics, where processing complex statistical distributions quickly is essential for recognizing patient data trends.

Cooperative Work

SI-PQC was developed by the Hefei Institute of Artificial Intelligence, the Anhui Province Key Laboratory of Quantum Network, and the University of Science and Technology of China Laboratory of Quantum Information. Origin Quantum Computing Technology, the private sector participant, showed how academic and commercial quantum research are merging.

The SI-PQC method enhances "statistical interpretability," a common challenge in advanced AI and quantum models.

Road Ahead

By applying quantum algorithms to “real-time, data-driven fields,” SI-PQC takes the industry closer to “practical quantum speedup” as academics call it. While quantum technology is still developing, software advancements like SI-PQC can ensure that applications will be ready when hardware is.

Researchers Discover Why ‘Suboptimal’ is Often Better on Real-World Hardware in Quantum Computing Revolution

Introduction

This study examines IBM Q hardware's practical challenges in differentiating quantum operations. Despite theoretical models showing complex configurations are optimum, hardware noise reduces accuracy with excessive entanglement and deep circuit topologies.

Their IBM Brisbane CPU tests show that simpler, theoretically less-ideal designs frequently perform better in actual life. Setting a circuit depth threshold in the study provides a novel method for selecting dependable designs that function well on near-term quantum devices. Finally, the results suggest a paradigm shift toward noise resilience over theoretical perfection. A Czech and German team found that even the most “mathematically perfect” quantum circuits are often outperformed by simpler, theoretically inferior designs on real hardware, undermining quantum information science's theoretical assumptions.

Searching for the ‘Black Box’

The study, led by Adam Bílek, Jan Hlisnikovský, and associates from the Technical University of Munich and VSB-Technical University of Ostrava, focuses on a crucial issue in quantum computing: quantum channel discrimination. You must test a "black box" that performs an unknown quantum operation to determine which of two operations is hidden.

Multiple “shots” (or uses) of this black box with elaborate, highly entangled parallel circuits may improve identification. But when these notions meet the cold reality of hardware noise on the IBM Brisbane CPU, the math fails, the study team found.

Duality of Entanglement

The team concludes that excessive entanglement harms contemporary quantum technologies. Sharing a job amongst numerous entangled qubits is frequently the most effective approach to learn in quantum theory. However, the researchers observed that the defects caused by entangling gates, such as the two-qubit ECR gates in the IBM Eagle R3 architecture, often exceed the benefits.

“Our analysis demonstrates that circuits that generate excessive entanglement or are too deep in quantum are not appropriate for the discrimination task,” the authors write. Sequential systems, which use a single qubit repeatedly, were more resilient to hardware noise as long as the circuit did not exceed a depth “threshold value”.

Trying IBM Brisbane

The researchers tested these hypotheses with the 127-qubit IBM Brisbane gadget in two key tests. They attempted to distinguish between a rotation gate (RZ(ϕ)) and an identical operation in Experiment 1. They compared “short” and “XOR” measurement methods for circuit widths (qubits) and depths (operations).

The success was due to “hardware-aware” optimization. Since IBM hardware uses ECR gates instead of CNOT gates, manually mapping logical qubits to physical qubits on the device's topology could enhance accuracy by nearly 20% on an 11-qubit system. This shows the growing requirement for “topology-aware” circuit design, which considers quantum device gate orientations and physical structure.

The Bit-Flip Mystery

Researchers found “anomalous behavior” that is still debated. The device showed simultaneous random bit-flip faults across all qubits with five or more entangled qubits, “inverting” the findings. Interesting that this peculiarity was in the first experiment but not the second, more complex one.

The authors believe the IBM Quantum execution stack's secret internal optimizations or calibrations may suppress these abnormalities in more complex configurations. This explanation is “highly speculative” without public access to the compilation and calibration process.

Why Scale Wins ‘Suboptimal’

When the team challenged 1,024 black box duplicates, the result was possibly the most surprising. The “optimal” theoretical strategy, which requires a massive, precisely-tuned GHZ (entangled) state, failed with results as random as a coin flip.

A less-than-ideal approach with “majority voting” worked well. By running multiple smaller trials on 32 qubits and taking the majority outcome, the researchers achieved 57% accuracy, which is still low but much better than the “optimal” method's failure.For large-scale challenges, theoretically ideal techniques failed, says the study. Suboptimal majority voting worked well. This indicates that modern effective quantum computing should often partition a complex problem into smaller, simpler sections that noisy hardware can handle.

The Future: A New Benchmarking Method

Finding black boxes is just one study suggestion. The results are immediately applicable to quantum sensor design and phase estimation. It also suggests that “circuit geometries beyond square layouts” may better depict NISQ devices' capabilities.

Ablation analysis, which separated noise sources, confirmed that readout errors and two-qubit gate noise remain performance constraints. Until these hardware faults are much reduced, the “theoretically suboptimal circuit is, counterintuitively, often the superior choice” for quantum computer practical applications.

Researchers Unlock High-Capacity Quantum Machine Learning with Physical Ground States

Researchers from Exeter and Sheffield have developed a new data loading method for quantum computers that may outperform classical ones. Ground state-based quantum feature mapping encodes machine learning information using the lowest-energy states of physical systems. This technique addresses one of the biggest challenges in Quantum Machine Learning (QML): efficiently and effectively translating classical input into quantum representation.

Quantum Data Embedding Challenge

Quantum machine learning uses quantum circuits to solve complex AI issues including classification, regression, and generative modeling. The data embedding, or “feature map” that converts classical input points into quantum states, limits the power of any QML model.

Researchers previously used rotation-based feature maps to encode data using single-qubit Pauli rotations. Despite being easy to use, they often produce “Fourier-type” models that classical computers can emulate, eliminating the quantum advantage. Amplitude encoding can cram more data into a state, but it requires “quantum RAM” or oracles that are hard to construct.

The paper by Chukwudubem Umeano and Oleksandr Kyriienko introduces a third method: embedding data into the ground state ∣ψG​(x)⟩ of a non-trivial, parameterized Hamiltonian H(

Using Physical Systems

The new procedure centers on Ground State Preparation. The lowest energy state of a system is the ground state in physics. Researchers found they could create a sophisticated mapping process by parameterizing the system's Hamiltonian (the operator expressing total energy) with a data characteristic x.

To test this, scientists combined GSP and adiabatic state preparation. This method starts a quantum system in a known ground state and "anneals" it to a complex target state. The researchers translated the features of these ground states back to standard QML mathematical frameworks by “Trotterizing” their progression into numerous little, digital steps.

Unprecedented model capacity

The study's most notable finding is model capability. The researchers showed that ground state-based feature maps build a frequency spectrum degree rapidly as qubits are added. With N qubits, the frequency gap spectrum expands at least as a high-degree polynomial, and for complex Hamiltonians, it can be exponential.

Traditional rotation-based systems have insufficient capacity compared to this large capacity. The authors conclude that “Ground state-based feature maps can be seen as models with large capacity,” allowing them to develop models that traditional machines cannot imitate.

Balance Expressivity with Trainability

Despite their power, the researchers said these models are not “wild” or uncontrollable. The spectrum of ground state maps has huge frequency degeneracies and “highly structured coefficients”.

This means that while the model may access many frequencies, it inherently favors structured patterns. This limits the model's expressivity, or how many functions it can describe, but the authors think this is good. In QML, a model's expressiveness and ease of training sometimes conflict. GSP-type maps can "tame" the vast capacity with structured coefficients to improve trainability and generalization from small data sets.

Testing Ising Model

To prove their idea, Umeano and Kyriienko implemented the protocol on a chain of qubits controlled by an Ising-type Hamiltonian, a magnetic mathematical model. They recreated ground state preparation around "critical points," when the system phases.

Despite digital "Trotterization" issues, the adiabatic foundation worked. The models use high-frequency components, but the ordered coefficient design prevents noisy oscillations, allowing the model to focus on the most important data.

A Future Roadmap

The implications of this discovery go beyond theoretical physics. In the pursuit of "quantum advantage," the moment at which quantum computers execute something valuable that conventional computers cannot, data embedding is the "prime source" of that potential edge.

Umeano and Kyriienko formalized how ground states can be high-capacity feature maps, laying the groundwork for more efficient QML protocols. Effective Hamiltonians for data embeddings are expected to ensure smooth basis functions and great learning performance.

Foundational QIR Patents Give Zapata Quantum Global IP Dominance

Zapata Quantum has extended its intellectual property portfolio by patenting its Quantum Intermediate Representation (QIR) technology in key foreign regions. This technology acts as a universal translator, reducing the need for specialized integrations and allowing software to work seamlessly across quantum hardware platforms. With rights in Canada, Europe, Israel, and Australia, the company has strengthened its hardware-agnostic software development leadership. This notion provides infrastructure to facilitate how developers deploy complicated applications in a fragmented environment over time. This interoperable platform aims to accelerate quantum computing's commercial transition from experimental to industrial adoption.

The grants allowed the Boston-based developer to patent a technique that enables hardware-agnostic quantum applications worldwide, a major milestone. Zapata's QIR may unite the quantum sector, which battles with different hardware and programming frameworks.

The Quantum Computing ‘Universal Translator’

QIR is a hardware-agnostic translation layer. Its main objective is to make quantum applications run across hardware platforms and programming frameworks. Software compatibility has been a major issue in quantum computing, where manufacturers use superconducting qubits, trapped ions, and photonic devices.

As a “universal translator”, Zapata's QIR solves this. It allows developers to write and turn a quantum program into a universal representation that can be executed on any linked quantum hardware backend. In classical computing, intermediate representations like LLVM allow software to run on different processors without being recreated for each architecture.

A double benefit comes from the technique. It simplifies software development and reduces hardware supplier workload. A hardware manufacturer that connects to the QIR interface once can support several software tools and frameworks without special integrations for each new tool. Zapata wants to substantially reduce the time from basic development to full-scale implementation for hybrid quantum-classical systems by reducing compatibility barriers.

Deliberate Eight-Year Strategy

A long-term plan dating back to the company's founding led to these patents. Zapata Quantum CEO Sumit Kapur said the company has followed a proactive intellectual property strategy for almost eight years, starting with Harvard University quantum computing facilities.

Kapur said, “By entering the field early and staying focused on software, we were able to identify and invest in foundational technologies like QIR at a time when few others were focused on the higher layers of the stack.” By focusing on the “software stack” rather than hardware development, Zapata has become the only publicly listed, hardware-agnostic quantum software company.

The company now has over 60 issued and pending patents. This broad IP strategy protects the hybrid quantum-classical computing stack's essential building pieces, including application development, interoperability, and deployment, not just algorithms.

Demos to Repeatable Deployment

QIR has major implications for quantum technology commercialization. Dr. Jonathan Olson, Zapata's Strategic Advisor for Intellectual Property, says QIR provides the infrastructure needed for larger-scale hybrid quantum-classical computing.

Many early "Quantum Decade" attempts were one-time demonstrations or proofs of concept. With QIR standardization, the industry may move toward repeatable deployment across growing hardware. This ensures that current software applications are viable and scalable as technology evolves and new companies enter the market.

The QIR Alliance, led by Microsoft, NVIDIA, Oak Ridge National Laboratory, Quantinuum, Quantum Circuits Inc., and Rigetti Computing, recognizes Zapata's leadership in this field. Zapata supports this alliance's goal of a standardized and interoperable architecture with exclusive patent rights.

Industry-wide global reach

Zapata affects more than infrastructure. The company has a history of applying quantum discoveries to Fortune 500 companies and government agencies. Its software covers several critical applications, including:

Cryptography and Defense: Strategic planning and security.

Pharmaceutical and Materials Discovery: Accelerating drug and material discovery.

Finance: Optimizing complex portfolios and risk.

Zapata is the only firm to engage in all technical fields of DARPA's Quantum Benchmarking effort, demonstrating its technological depth and commitment to excellence.

Handling Future Uncertainties

Zapata acknowledges problems despite its robust patent portfolio. The company said its future success depends on numerous aspects, including more resources to restart material processes and satisfy goals.

The corporation fears inflation, interest rate instability, and U.S. tariff policy changes. The competitive nature of the sector means other competitors may develop better technology, making intellectual property rights preservation even more important.