#kipuquantum

Analog Counter diabatic Quantum Computing ACQC

A team of Kipu Quantum, QuEra Computing, and Pasqal researchers has demonstrated a revolutionary approach to substantially enhance neutral atom quantum processor performance, advancing quantum advantage. Analog Counterdiabatic Quantum Computing (ACQC) eliminates the flaws of traditional quantum evolution to solve difficult industrial problems.

By applying this method to the well-known combinatorial Maximum Independent Set (MIS) problem with up to 100 qubits, the researchers tripled convergence time and solution quality. This result marks a turning point for “noisy” near-term quantum systems with non-adiabatic faults and low coherence durations.

Time Rush: Overcoming Coherence Limits

Quantum computers are designed to optimize logistical routes, schedule tasks, and build effective telecommunications networks. In analog quantum computing, adiabatic evolution is a cutting-edge method that starts from a known ground state and progressively develops into a final state that encodes a problem's solution.

However, the coherence window hinders this process. If evolution is too fast, “non-adiabatic errors” arise when the system jumps out of its optimal course, diminishing final result fidelity; if too sluggish, noise destroys the quantum state.

Researchers found that “these errors limit scalability in state-of-the-art adiabatic protocols.” Counterdiabatic (CD) procedures inhibit unwanted energy state transitions by adding an auxiliary term to the system's Hamiltonian. CD terms have been predicted for a long time, but they were impossible to compute for large systems or required complex “many-body” interactions that are problematic with current hardware.

Customized Neutral Atom Solution

The neutral atom processor-specific ACQC protocol is innovative. These processors use optical tweezers to encode information via Rydberg blockade, which occurs when an excited atom keeps its neighbors from being excited.

The researchers developed an analytical method to calculate counterdiabatic adjustments without energy spectrum knowledge or resource-intensive variational iterations. By dynamically adjusting the driving lasers' Rabi frequency, detuning, and laser phase, ACQC provides a "shortcut" to the answer.

Unlike digital quantum processors that use discrete, error-prone gate sequences to implement CD protocols, ACQC uses analog, continuous control. This is ideal for today's hardware's limited coherence since it allows faster, higher-quality solutions.

Performance Benchmarking: 100 Qubits+

ACQC was tested using Pasqal's Orion Alpha CPU and QuEra's Aquila device. The team focused on the MIS problem of finding the most non-adjacent nodes in a graph. This problem applies to computational biology, resource allocation, and network resilience.

Key findings from the experiments include:

Rapid Solutions: In the “fast quench” region, ACQC surpassed conventional adiabatic approaches with a 1 microsecond evolution time.

Higher Success Probabilities: ACQC exhibited a 60% success rate on a 15-node simulation, compared to 27.8% for linear adiabatic procedures.

Scalability: The Aquila processor allowed the team to handle 100-node graphs and maintain a 6–8% solution quality edge over smooth adiabatic schedules even at short periods.

ACQC improved the approximation ratio by 5x against traditional methods on a 27-node graph using Pasqal hardware during short evolution durations.

The researchers stressed that finding high-quality solutions quickly is vital, even while performance converges for longer evolution durations. According to the report, the fast-quench feature can be used in sequential operations where one part of the computation time prepares a state and the other performs high-fidelity computing inside the coherence time.

Industry Relevance and Future Outlook

Industry impacts are significant. This MIS study addresses complex logistical issues including selecting non-conflicting financial asset groupings or finding the best communication networks. ACQC's comprehensive analytical method doesn't require processor optimization, making it a "plug-and-play" upgrade for existing and future devices.

ACQC will benefit from single-qubit addressability and local detuning, according to the team. These qualities would enable the development of more advanced CD techniques, which could lead to unprecedented precision in spin system critical dynamics research.

As quantum technology scales to hundreds and thousands of qubits, ACQC will be needed to reduce experimental requirements for quantum advantage. In contrast to a distant, error-corrected future, this paper proposes exploiting analog quantum processors to solve industrial problems.

With Rimay, Kipu Quantum, a top Berlin-based quantum software developer, moved from experimental demonstrations to industrial deployment. This new quantum feature extraction service is a “plug-and-play” complement to traditional machine learning (ML) models, improving performance in manufacturing, medical sciences, and finance.

The Kipu Quantum Hub makes the service, which complements the company's Illay and Miray quantum optimizers, widely available. Rimay makes “industrial quantum usefulness” possible by extracting mathematically undetectable data connections as organizations struggle with traditional AI, especially with noisy or insufficient data.

Resolving the “Noisy Data” Problem

In artificial intelligence, traditional models fail when data is few, imbalanced, or loud. Traditional algorithms may fail to perceive complex correlations in small datasets, resulting in inaccurate predictions or overfitting, where a model becomes too specialized to its training data and fails to generalize.

Rimay has a “closed-loop ecosystem” where quantum and classical computers reinforce each other to solve this. By mapping complex facts into a quantum state space, the service can reveal “higher-order” linkages and profound patterns that conventional computers cannot recreate or recognize. When these quantum-extracted properties are sent back into the classical pipeline, the current ML model gets a “sharper lens” to see its data.

Technical Advances: Noise-Bypassing

Rimay's launch is notable since it uses digitized counterdiabatic driving. This complicated protocol allows the quantum system to grow quickly to overcome noise restrictions in quantum hardware.

Rimay uses k-local many-body spin dynamics to capture multi-layered correlations and linear variable-to-variable contributions. The IBM Quantum-specific service advances feature mapping beyond simulation and is optimized for 156-qubit processors. This technical foundation makes the service a useful tool that grows with global device roadmaps, not just a theory.

Real Results in Global Industries

With substantial corporate validation, Rimay was made widely available. Kipu Quantum says several well-known partners have successfully integrated the technology.

Manufacturing and Maintenance: Komatsu Peru, NTT Data Latam, and Europe used Rimay to estimate maintenance from limited equipment data. The service improved semiconductor defect detection by 20%.

Energy and Environment: Moeve improved oil and gas pipeline leak detection by 13% by analyzing thermal imaging. KPMG classified tree species from sparse satellite photos using Rimay to improve environmental intelligence.

Financial Services: The financial sector was also hit hard. Rimay increased credit risk assessment accuracy by 5% and business bankruptcy prediction by 4%.

In life sciences, molecular toxicity prediction improved by 5–10% and drug-induced autoimmune reaction prediction improved by 7%, which could speed up drug discovery.

Strategic Partnership with IBM Quantum

The introduction of Rimay relies on Kipu's strategic cooperation with IBM Quantum. Scott Crowder, VP of IBM Quantum Adoption, said IBM's global cloud fleet has the digital hardware enterprises need to study these benefits. Crowder was delighted to work with Kipu and ecosystem to find new ways to apply quantum computing into computer systems and change problem-solving. This agreement ensures Rimay users have fast access to more powerful hardware to boost their ML models as IBM grows its quantum processors.

Possible “Quantum Advantage” Era

According to Kipu Quantum CEO Enrique Solano, this launch fulfills the company's original purpose of providing value immediately rather than later. Solano defined industrial quantum usefulness as consumers outperforming their classical baselines using quantum-enhanced features at the quantum advantage level. He described the service as a “competitive advantage you subscribe to,” indicating that corporations are viewing quantum technology as a tool for daily operations rather than research.

Kipu Quantum says Rimay is the first QML toolkit module. These technologies in the Kipu Quantum Hub are making the startup a key platform for firms trying to remain ahead in the fast-changing AI area.

Greater Market Context

Rimay's introduction coincides with a boom in the quantum sector. Infleqtion went public on the NYSE through a SPAC transaction, and Pasqal delivered a neutral atom quantum computer to CINECA. Partnerships between AMD, Comcast, and Classiq are studying quantum algorithms to boost internet resiliency.

In light of this, Kipu Quantum's focus on feature extraction for machine learning finds a valuable market niche that bridges classical AI's dominance and the quantum age.

Huk Quantum Feature Mapping

Kipu Quantum Launches Huk Quantum Feature Mapping, the First Commercial Quantum-Advantage Machine Learning Service.

Kipu Quantum, a 2021 German startup, announced Huk Quantum Feature Mapping, a groundbreaking commercial quantum machine learning (QML) service. This new device is being called the “first commercial QML solution at a quantum-advantage level for real-world applications,” marking a turning point in quantum computing deployment. Huk is easily accessible to enterprises, researchers, industries, and academic institutions via Kipu Quantum Hub and PLANQK. Create a Quantum-Classical Hybrid Method Main invention of Huk Quantum Feature Mapping is patented analogue quantum feature mapping. This innovative method creates “substantially enriched quantum-enhanced feature representations” from regular datasets. The method uses quantum physics to detect and encode complicated data correlations that standard feature engineering cannot. QML techniques often focus on fully quantum algorithms, however Huk utilises a hybrid approach. Using computational resources near quantum supremacy, it merely uses quantum computation for feature extraction (preprocessing). Improved feature representations readily integrate into regular machine learning procedures, boosting dimensionality and representational capacity. Customers can immediately exploit quantum insights without infrastructure modification. Proven Quantum Advantage Early experiments on a molecular toxicity dataset reveal that Huk Quantum Feature Mapping enhances positive-class precision by 60%. This demonstrable performance improvement over the most advanced classical techniques shows the power of quantum preprocessing, which is the main reason of these astonishing results. Kipu Quantum anticipates even greater relative performance gains with each quantum gear iteration. Resolving Intractable Domain Issues with Limited Data This technology lets users “harness quantum-enhanced data insights, without waiting for fault-tolerant quantum computers,” says Kipu Quantum CEO Daniel Volz. Co-CEO Enrique Solano called Huk “a defining moment in the history of quantum computing,” solving previously unsolvable problems. Huk Quantum Feature Mapping works well in domains with difficult, expensive, or ethical data collection. This includes crucial uses like: Clinical trials

Rare illness identification

New drug discovered

Predicting financial risk

A security analysis