#physicalqubit

Elevator Code News

Elevator Codes are a quantum error correction (QEC) innovation that reduces logical error rates by 10,000x with only a 3x increase in physical qubit overhead. This novel design by French-American company Alice & Bob uses a “concatenated” approach to prevent bit-flip faults, which prevent “scientific-grade” quantum computers.

Closing the “Scaling Gap”

The Scaling Gap is quantum computing's biggest challenge. Quantum systems are fragile and prone to two types of errors:

Bit-flips: When a logical "0" accidentally becomes a "1."

Phase-flips: Jumbled quantum phases in superpositions.

Alice and Bob's cat qubits feature "passive" hardware security that makes them resistant to bit-flips and have recently attained hour-long bit-flip lifetimes, but sophisticated algorithms require more. The current hardware cannot meet the required logical error rate of 10−9 (one error per billion operations) for FeMoco molecular simulation in chemistry applications with passive protection.

The “Floor and Sweep” Architecture of Elevator Codes

In place of Google and IBM's massive 2D grids (Surface Codes), the Elevator Code builds simpler structures like a Russian doll using concatenation.

Floors (Inner Code): The system handles phase-flips with 1D repetition codes. Buildings stack these like floors.

Elevator (Outer Code): One “logical ancilla” strip of qubits. It “sweeps” floors to check parity.

The elevator uses transversal CNOT operations to detect bit-flip defects across logical qubits as it goes through each floor. Recycling: After a check, the ancilla is measured, reset, and utilized for the next floor. A distinct ancilla for each check is eliminated by this “sweep-and-recycle” strategy, making the code more efficient.

Comparison between Efficiency and Performance

The Elevator Code handles the two error types differently to reduce hardware waste. Because cat qubits inhibit bit-flips at the hardware level, the Elevator Code may focus on phase-flips and use the “elevator” to clean up any remaining bit-flips.

Elevator Codes have a higher "rate," allowing them to pack more logical qubits into a smaller footprint than the Thin Surface Code or XZZX Code. Obtain a 10−12 high-fidelity error rate. Elevator Code requires 30% less qubits than Rectangular Surface Code (−12).

Why It Matters to Industry

According to standard architectures, a “scientific-grade” quantum computer with 100 high-fidelity logical qubits requires hundreds of thousands of physical qubits. Alice & Bob's simulation suggests utilizing Elevator Codes with 1,500 cat qubits. By using a linear, stackable architecture and decoupling error types, Alice & Bob have solved numerous 2D lattice connection constraints. This aids hardware fabrication and scaling.

Path to “Near Extinction” of Errors

Elevator Codes essentially “future-proof” cat qubits. Instead than waiting for new chip generations, it lets the company achieve ultra-low logical error rates on current technology.

A physical qubit is the foundation of a quantum computer. It's a qubit's physical example, like a transistor.

To elaborate:

A physical qubit?

To effectively control the |0⟩ and |1⟩ states, a physical qubit must encode its degree of freedom. More than two states can be employed with physical qubits, although two-level systems are best. They represent quantum hardware.

A conventional bit can only exist in either state, whereas a physical qubit can exist in both. Entanglement, which occurs when two or more qubits share the same fate, and the ability to analyse vast volumes of data in parallel and possibly perform some calculations exponentially faster than traditional computers make this possible.

One physical qubit is delicate and error-prone. Noise from inherent qualities or ambient interactions can cause small manipulation errors in physical qubits. These differences can worsen with time and undermine a computation. In the early-stage Noisy Intermediate-Scale Quantum (NISQ) phase, “qubit” is synonymous with “physical qubit.” A 256-qubit neutral atom quantum computer has 256 atoms, a 1:1 ratio.

History

Paul Benioff and Richard Feynman proposed in the 1980s that quantum computers could better simulate physical processes. Schumacher coined “qubit” in 1995. The first quantum algorithm was demonstrated in 1998, advancing the field quickly. Instead than growing physical qubits, the focus has been on developing significant logical qubits by improving their quality (fidelity) and error correction.

The Process

A physical qubit encodes information using a quantum system feature. The |0⟩ and |1⟩ states can be modelled using atom energy levels or electron spin. Path, time-bin, or polarisation can encode photonic qubits.

To perform quantum processes, these states must be carefully managed. Several methods are employed:

Microwave pulses manipulate superconducting qubits.

Laser pulses can modulate neutral atoms or trapped ions.

Magnetism affects electron spin.

Analogous procedures work too.

Being in superposition gives qubits power. When a qubit is measured in superposition, its quantum state determines whether it “collapses” to 0 or 1. Since they transport data, physical qubits are necessary. Input data, single- and two-qubit quantum gate calculations, and outcomes are shown.

Advantages

Entanglement and superposition: Quantum computers can analyse massive volumes of data simultaneously, speeding up procedures.

Exponential scaling: One qubit doubles a system's computational space, allowing few qubits to represent multiple states. The phenomena is exponential scaling.

Physical qubits excel at optimisation, factorisation, and molecular simulation.

Disadvantages

Qubits can make mistakes and lose their quantum state due to environmental noise like temperature changes or electromagnetic radiation. Loss of quantum information limits calculation times.

System scalability: Adding qubits is difficult. Controlling qubits individually and minimising crosstalk becomes a major engineering difficulty as qubit numbers increase.

Due to the natural inclination of physical qubits to make mistakes, quantum error correcting solutions must be complex and resource-intensive. Several physical qubits are needed to create a stable logical qubit.

Implementations, types

Every physical qubit implementation has pros and cons, hence there is no “best” way. This encourages new modality research. Physical systems classify key architectures:

Using superconducting circuits at absolute zero, superconducting qubits are formed. They are fast and can be manufactured using current chip-making technology, but they are noise-sensitive and require severe cold. Google and IBM employ them.

Trapped Ion Qubits: Lasers and electromagnetic fields control charged atoms (ions) suspended. They offer high-fidelity operations and long coherence durations, but they are slower and harder to scale than superconducting qubits.

Lattices or optical tweezers hold neutral atom qubits, also known as cold atoms. Their long coherence periods and ability to form large, reconfigurable arrays make their scalability appealing. Since atoms are not generated, they are considered equivalent and faultless, eliminating the need for calibrations or mapping circuit qubits to physical qubits to lower error rates or improve connections. The largest publicly accessible quantum computer is Aquila, with 256 neutral atoms.

Data is encoded using photons, light particles. They run at room temperature and are good for quantum communication but not complex computations. Encoding photons has many methods: Polarisation encoding uses horizontal and vertical light modes.

Photons in one of two fibre optics are used in “dual-rail” route encoding.

Time-bin encoding uses photon timing within a defined period (early or late).

In carbon lattice vacancies, quantum dots, or vacuum chambers, electron spins can encode information. Electron-constricting semiconductor nanocrystals called quantum dots transmit information through electron presence superpositions.

NV Centre: The nitrogen vacancy centre encodes information in either electron spins or nuclear spins of nitrogen atoms. The largest NV Centre device contains two qubits.

Theoretical topological qubits encode information through qubit movements.

Challenges

A large-scale quantum computer made from physical qubits must overcome many challenges:

The length of a qubit's quantum state before decoherence must be extended for longer computations.

Scalability: Engineering and manufacturing must increase qubits while maintaining performance and low error rates.

Error Correction: A “fault-tolerant” quantum computer requires viable quantum error correction codes (QECC) that can be implemented in hardware. Physical qubits' intrinsic noise makes mistake correction necessary.

As systems grow, reducing qubit crosstalk becomes more critical.

Applications

Physical qubits have many uses, including:

Quantum simulation can simulate complex molecules and materials, which may progress material science and medicine.

Solving complex optimisation problems in supply chain management, finance, and logistics.

Cryptography uses methods like Shor's to break current encryption and construct quantum-safe encryption.

States Gottesman–Kitaev–Preskill

Xanadu leads photonic quantum computing with their development of a scalable building block for  fault-tolerant quantum computers. The achievement involves on-chip Gottesman–Kitaev–Preskill state production and was initially reported in January 2025 by Nature and summarised in June 2025. “First-of-its-kind achievement” and “key step towards scalable fault-tolerant quantum computing” describe this work.

Understanding GKP States' Importance

GKP states are error-tolerant photonic qubits. These complex quantum states consist of photons stacked in specific ways. Due to its unique structure, quantum error correcting methods may identify and fix phase shifts and photon loss. Zachary Vernon, CTO of Xanadu, calls GKP states “the optimal photonic qubit” because they enable quantum logic operations and error correction “at room temperature and using relatively straightforward, deterministic operations.” It has always been challenging to construct high-quality Gottesman–Kitaev–Preskill States on an integrated platform. This discovery advances continuous-variable quantum computing architectures by overcoming that obstacle.

GKP states provide fault-tolerant computing by using linear optics and measurement algorithms, unlike probabilistic entanglement methods that require repeated trials and complex feed-forward control. They fit well with hybrid systems because they generate quantum networks that link chips or modules or create larger cluster states for measurement-based computation.

Quantum systems' interoperability with optical fibre makes scaling easy, allowing them to be distributed among system components or data centres. This demonstration changed photonic quantum computing by taking a different approach from superconducting and trapped-ion platforms and bringing these systems closer to utility-scale quantum machine error thresholds.

Aurora: Photonic Quantum Computing Architectur

The “sub-performant scale model of a quantum computer” “Aurora” represents Xanadu's work. This system uses scalable, rack-deployed modules connected by fibre optics to incorporate all basic components. With 35 photonic devices, 84 squeezers, and 36 photon-number-resolving (PNR) detectors, Aurora provides 12 physical qubit modes each clock cycle. All system components except the cryogenic PNR detection array are operated by a single server computer and fit into four server racks.

Aurora's key technologies and their functions:

Silicon nitride waveguides feature minimal optical losses. This waveguide uses 300 mm wafers, which are common in semiconductor production. Newer chips based on Ligentec SA's 200-mm silicon-nitride waveguide architecture show potential for better squeezing and lower chip-fiber coupling losses.

The efficiency of photon-number-resolving (PNR) detectors is above 99%. In 12-mK dilution coolers, 36 transition edge sensor (TES) arrays form its base. These TES detectors cycle at 1 MHz and detect up to seven photon counts with little miscategorization error. Despite being highly effective, PNR detection efficiencies of over 99% are needed to meet the architecture's strict P1 path loss constraints.

Loss-optimized optical packaging—including accurate alignment, chip mounting, and fibre connections—was emphasised. This protects critical quantum information during routing and measurement.

The refinery array has six photonic integrated circuits (PICs) on a thin-film lithium-niobate substrate. Each refinery's two binary trees of electro-optic Mach-Zehnder modulator switches dynamically select the best output state based on PNR detection system feedforward instructions. Even though current Aurora refinery chips use probability-boosting multiplexing and Bell pair synthesis, future generations will use homodyne detectors to complete the adaptive breeding method.

Interconnects: Phase- and polarization-stabilized fiber-optical delay lines connect the refinery to QPU and refinery modules. These delays allow temporal entanglement and buffer information heralding in the cluster state.

Experiments and Results

Two large trials benchmarked Aurora's main features.

To generate a 12 × N-mode Gaussian cluster state, the system was set to send squeezed states to the QPU array. Data was collected at 1 MHz for two hours to synthesise and measure a macronode cluster state with 86.4 billion modes. Despite substantial optical losses (approximately 14 dB), the nullifier variances remained below the vacuum noise threshold, proving squeezing and cluster state entanglement.

Detecting Repetition Code Errors: This experiment showed the system's feedforward and non-Gaussian-state synthesis using low-quality GKP states. In real time, the QPU decoder assessed the system's two (foliated) repetition code checks. The decoder calculated bit values and phase error probabilities to change the measurement basis for the next time step.

Limitations and Prospects

Despite these notable examples, the “component performance gap” between existing capabilities and fault tolerance needs remains large. The main limiter of quantum state purity and coherence is optical loss. Ideal designs for fault-tolerant operation require loss budgets of about 1%, whereas the Aurora system lost 56% for heralding pathways (P1) and nearly 95% for heralded optical paths (P1 and P2).

Xanadu's future projects include:

Hardware improvements: Chip fabrication, waveguide geometry, and packaging are optimised to improve fidelity and reduce optical loss. The photonic components' insertion loss must be improved by 20-30 times (on a decibel scale).

Architectural Refinements: Testing cutting-edge hardware-level photon generation and detection rates and error mitigation measures to reduce loss and imperfection.

Integration and Scaling: combining the new GKP generation methods with Aurora's networking, error correcting protocols, and logic gates. The company believes scalable, semiconductor-compatible platforms can mass-produce, modify, and monitor error-correcting components for modular quantum computing.