#surfacecode

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Iceberg Quantum raises $6M for Quantum Computing Innovation

Iceberg Quantum

The entire quantum community was shaken by Iceberg Quantum “Pinnacle” architecture, which solves the “overhead problem” that has slowed quantum computer development. The startup's technological debut and $6 million seed funding round headed by LocalGlobe signaled a shift in how the industry handles fault-tolerant quantum computing (FTQC).

For years, scientists believed that millions of physical qubits would be needed to account for the massive error correction needed for a quantum computer to crack RSA-2048, the world's standard encryption. According to Iceberg Quantum, their Pinnacle design can reach this milestone with less than 100,000 qubits, accelerating the “Q-Day” timetable by many years.

Fixing “Overhead Problem”

The biggest challenge in quantum computing is qubits' vulnerability to electromagnetic interference and thermal vibrations, which causes “noise”. This produces decoherence, mistakes, and qubit quantum state loss. Scientists have used Quantum Error Correction to create a steady “logical qubit” from thousands of physical qubits. Traditional models like the Surface Code require so much redundancy that large-scale machines appear decades away.

Instead of the Surface Code, Iceberg Quantum's Pinnacle architecture employs qLDPC codes. Because qubits have complex physical routing, qLDPC codes were theoretically viable but difficult to implement. Iceberg claims these constraints have been overcome, reducing hardware overhead by an order of magnitude.

Quantum Era “ARM”

While others build hardware like dilution refrigerators and vacuum chambers, Iceberg Quantum is focusing on architecture. Similar to ARM, Iceberg focuses on software layers and “blueprints” to improve hardware efficiency in the semiconductor industry.

The company is working with major hardware manufacturers on IonQ (trapped ions), Diraq (spin qubits), and PsiQuantum (photonic). Andre Saraiva, Diraq Head of Theory, said Iceberg's devices would have “utility-scale applications” sooner than planned.

Global Security and “Q-Day” Search

The most crucial result of Iceberg's work is the massive reduction of cryptographic significance timeline. The company told cybersecurity and intelligence communities that RSA-2048 may be hacked with fewer than 100,000 qubits. PsiQuantum and Diraq, hardware leaders, estimated this large system would be built in three to five years. If Pinnacle is effectively included, the window for breaching encryption may open earlier than 2035–2040.

Three Sydney PhDs

DCVC and Blackbird Ventures invested in the $6 million seed round for rapid global development. University of Sydney PhDs Felix Thomsen (CEO), Larry Cohen (CSO), and Sam Smith (CTO) founded the company.

Iceberg Quantum is opening its first European office in Berlin and expanding to the US after securing funding to be closer to its hardware partners. CEO Felix Thomsen said it aims to hasten the transition to and power the fault-tolerant age of quantum computing.

The Quantum Utility Path

Despite industry insiders' cautious confidence, going from a theoretical architecture to a real machine is still a “Herculean task”. Investors believe Iceberg's plan is the best route to accomplish "Quantum Utility," when quantum systems outperform supercomputers in materials science, financial modeling, and drug discovery.

The "iceberg" of quantum potential appears to be closer to the surface than previously thought as quantum supremacy race heats up.

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Phantom Codes: A Breakthrough In Quantum Error Correction

Phantom Codes, a revolutionary family of quantum error-correcting codes that enable logical entanglement without physical processes.

The “Phantom” Breakthrough: Relabelling Entanglement

A quantum computing architecture roadmap from the University of Maryland and NIST is groundbreaking. The work offers “phantom codes”—quantum error-correcting codes (QECCs) that establish logical entanglement by classically relabeling physical qubits during compilation—led by Jin Ming Koh, Anqi Gong, and Andrei C. Diaconu.

Entanglement of logical qubits traditionally requires resource-intensive physical interactions like laser pulses or microwave bursts. These physical gates are known for generating noise and faults that slow computing. Phantom codes escape this “physical toll” by inserting qubit permutations into classical circuit compilation, making entangling gates “ghost-like” in the code's mathematical structure.

Resolving Physical Error

Standard quantum error correction combines numerous brittle physical qubits into a strong logical qubit to prevent decoherence. However, the physical activities needed to execute logic between these qubits often cause the errors the codes are supposed to prevent.

Google and IBM use the Surface Code, which requires more qubits and time for logical gates. Phantom codes, however, offer:

Zero-Depth Gates: Relabeling entanglement in the classical controller's "mind" causes no physical action, hence there is no temporal or spatial overhead.

Perfect Fidelity: The gate operation has no physical inaccuracy because no physical pulses are generated to form the entanglement.

When sequential physical time steps are omitted, qubit decay decreases, improving circuit dependability.

Massive Quantum Landscape Expansion

One error-correcting code and one error-detecting family were known for the phantom codes, a mathematical oddity. In “mathematical archaeology,” the Maryland and NIST team used numerical searches and Boolean satisfiability (SAT-based) methods to map these codes' wider geography.

The scale of this search was unprecedented:

The researchers listed about 27 billion inequivalent CSS (Calderbank-Shor-Steane) codes. Over 100,000 additional phantom codes were discovered.

They used Reed-Muller codes and qudit binarization to build higher-distance families for scalability.

This broad mapping shows that phantom codes are a practical and diverse family of tools for designing large-scale quantum systems.

Phantom vs. Surface Code Performance The researchers did end-to-end noisy simulations of QEC cycles and physical error rates to verify these codes' practicality. They compared the algorithms to the Surface Code in GHZ-state preparation and Trotterized many-body simulations, two demanding quantum jobs.

Phantom codes provide a considerable accuracy gain over industry standards for workloads with dense local entangling structures without adding hardware.

Moving the Weight to Software

The quantum architectural shift is one of the biggest effects of this finding. Hardware engineers building near-perfect physical gates have traditionally been responsible for scalability. Phantom codes move this load to classical circuit compilation.

Phantom-code-based systems require a more capable classical computer to manage quantum hardware. Complex qubit permutations must be tracked and “absorbed” into the instruction set. The hardware can “stay quiet” while the logic optimizes storage and compute with this “software-heavy” method.

Universal logic and practical applications

Perfect-fidelity entanglement has immediate implications for the “real-world” jobs quantum computers are expected to solve. The researchers noted that phantom codes are ideal for drug development, materials science, and quantum chemistry.

The researchers also confirmed that these codes are procedure-independent. All detected phantom codes support:

Fault-resistant logical Clifford gates. Universal quantum computation requires non-Clifford gates.

Transversal interblock CNOTs and zero-depth in-block gates create effective CNOT circuits.

Meeting Future Challenges

Phantom codes provide a shortcut to the fault-tolerant age but bring challenges. Their non-LDPC (Low-Density Parity-Check) makes decoding more difficult than for other codes. To address phantom codes' uniqueness, the study team created fault-tolerant state preparation and decoding tools.

In conclusion:

The finding of approximately 100,000 new phantom codes marks a quantum error correcting milestone. This work shows that logical qubits can be coupled without physical "touch," avoiding the behaviors that degrade quantum data most.

This suggests that quantum computing may require smart machine building rather than just better hardware. As the industry moves toward practical applications, the ability to “relabel” entanglement could enable large-scale, error-free quantum simulation.

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Quantum Surface Code Scaling For IBM Heavy-Hex Systems

Quantum Surfaces Code

The challenging endeavour of developing scalable quantum error correction (QEC) is slowly making progress according to new pioneering research announced by Arian Vezvaee, Cesar Benito, Mario Morford-Oberst, and their colleagues at the University of Southern California and Universidad Autonoma de Madrid. Their study demonstrates a crucial step towards attaining subthreshold scaling of a surface code memory, even when constrained by an unoptimal architecture, by utilising IBM's heavy-hex design.

This significant progress is the outcome of a carefully co-designed approach that blends a ground-breaking surface code embedding mechanism with highly dependable dynamical decoupling (DD) techniques. The validates improved security of quantum information over many error correction cycles and provides a straightforward way to systematically evaluate scalable surface-code performance under realistic, biassed noise scenarios.

Handling Architectures That Are Not Native

Surface codes are a strong candidate for realistic QEC because of their versatility for two-dimensional structures and potential for fault-tolerant computation. However, to effectively apply these codes, physical error rates must be kept to a minimum in order to reach the threshold needed for resilient logical qubits.

The difficulty rises with the use of hardware like IBM's heavy-hex lattice-based superconducting QPUs.

The heavy-hex layout introduces a significant incompatibility compared to QPUs employing a 2D square lattice specifically designed to reflect surface code connectivity. This reduced connectivity often results in significant delays needed to transfer state across non-neighboring qubits. These extra delays result in "idle gaps," which make the qubits more susceptible to noise and seriously hinder attempts to demonstrate subthreshold scaling. To overcome these structural limitations, the researchers carefully collaborated on the control and code embedding methods.

The Solution Co-Designed: Decoupling, Unfolding, and Folding

The team used bridge ancillas in conjunction with a depth-minimizing SWAP-based "fold-unfold" embedding to map the surface code onto the non-native heavy-hex connection. This method cleverly decreases circuit depth by first folding weight-4 stabilisers into weight-2 operators, measuring them with ancilla qubits, and then unfolding them back to their original shape. Furthermore, circuit depth was reduced by doing away with reset gates and employing software to monitor previous measurement outcomes. This greatly lowered the syndrome extraction round and the associated idle errors.

Crucially, this hardware-aware embedding was coupled with robust Dynamical Decoupling (DD). DD is important in this context because it minimises coherence flaws such as ZZ crosstalk and non-Markovian dephasing that accumulate during the specific idle gaps that are specific to the heavy-hex layout. The researchers were able to maximise efficiency by altering sequences such as universally robust (URm) variations to fill in idle spaces based on time.

Measurements confirmed the significance of DD by eliminating the potential for erroneous claims of subthreshold scaling that could arise from comparing scaled codes without DD to smaller codes that benefit from it. Effective DD is essential for achieving true subthreshold performance by reducing noise to a level that the surface code can handle.

Anisotropic Scaling and Evaluation of Performance

The experimental work requires anisotropic scaling on Heron-generation devices to move from a uniform distance 3 code to anisotropic configurations of (3,5) and (5,3). This allowed them to examine how the protection of logical states for up to 10 QEC cycles was affected by expanding the code distance in a single direction (X or Z basis).

Z-basis logical states are better protected when d x increases, while X-basis logical states are better protected when d x increases. This is a significant finding that supports directional error suppression. For example, the (3, 5) code consistently displayed lower logical error probabilities for the condition than the averaged (3, 3) reference code.

However, real global subthreshold scaling remains challenging. The benefit of extending the coding distance for a single error type does not always outweigh the loss of the orthogonal basis caused by the necessary increase in circuit complexity. Therefore, total entanglement fidelity (EF) was often higher for the smaller programs on the main processor, ibm_aachen.

Entanglement Fidelity Metric: A New Standard

To circumvent the limitations of traditional measurements, the team created a rigorous, model-fit-free performance statistic based on Entanglement Fidelity (EF).

Sometimes, conventional approaches rely on quantum computing to calculate a suppression factor that is determined using a single-parameter fit that assumes small logical SPAM errors, stationary (cycle-independent) errors, and unitality (only Pauli errors). The researchers demonstrated that these assumptions are often incorrect for their data on IBM QPUs. The research demonstrated a continuous difference in mistake probabilities for logical eigenstates as an example of non-unital logical noise.

The new EF measure, which is computed directly from the X- and Z-basis logical-error data provided by the decoder, offers per-cycle, SPAM-aware limits on code performance. The faithfulness of the entire logical channel is clearly measured by this measure, which automatically combines all four possible basis states.

The Entanglement Infidelity Ratio, derived from the EF metric, is suggested as the best benchmark since it is fitting-model-free and does not require the assumption of stationarity. It compares each code in its optimal configuration, which is maximised by the presence or omission of DD, in order to ensure accurate evaluation.

Fault-Tolerant Computing's Future

This work offers a simple and practical way to show successful surface code scaling in non-native platforms. The necessary strategy includes:

Lowering circuit depth through connection-considered embedding.

Incorporating robust DD to effectively minimize non-Markovian and coherent error components.

Scaling is assessed using EF-based, SPAM-aware metrics to produce unambiguous results.

Future circuit-level simulations calibrated to the experimental data provide appealing objectives: access to a slightly larger heavy-hex QPU capable of running a (5,5) code and a slight reduction of approximately 30% in the current experimental noise rates would be sufficient for real global subthreshold scaling under the EF metric. These results set clear, device-level objectives that are critical to the future development of fault-tolerant.