Ibm_Eagle

Qubit Architecture and Technology: The IBM Quantum Eagle processor is built upon superconducting transmon technology, a widely adopted approach in the field due to its relative scalability and control precision. It features 127 physical qubits, a landmark achievement at its announcement, making it the first quantum processor to surpass the 100-qubit threshold. This significant increase in qubit count opened new avenues for exploring larger quantum algorithms and simulations that were previously intractable on smaller systems. The qubits are arranged in a heavy-hex lattice connectivity topology. This specific topology dictates which qubits can directly interact via two-qubit gates, influencing circuit routing and optimization. A heavy-hex lattice offers a balance between connectivity and minimizing crosstalk, but it also means that not all qubits are directly connected, requiring 'swap' operations for non-adjacent qubit interactions, which can add to circuit depth and error accumulation. The qubit mode is primarily gate-based computation, with ongoing early exploration of error mitigation techniques to combat the inherent noise of NISQ-era devices.

Performance Metrics and Benchmarks: Evaluating quantum hardware requires a multi-faceted approach, moving beyond simple qubit counts to understand actual computational power. For Eagle, key performance indicators include:

• Physical Qubits: 127. This metric, while fundamental, must be contextualized. It represents the raw capacity, but the 'effective' or 'useful' qubit count is often lower due to errors and connectivity constraints.
• Error Rates and Fidelity: The median ECR two-qubit error rate was reported at 7.58e-3 (0.758%) as of 2024-07-03. This figure is crucial for understanding the reliability of gate operations. Lower error rates enable deeper circuits and more accurate results. While this represents a significant improvement over earlier generations, it still means that complex circuits will accumulate errors, necessitating error mitigation strategies. Readout error rates were not specifically provided in the facts, which is an important omission for a complete analytical picture, as readout errors can significantly impact the final measurement fidelity.
• Benchmarks:
  • EPLG (Error Per Layer of Gates): Projected at 1.98e-2 (1.98%) for 2025. EPLG is a more holistic metric than individual gate errors, as it quantifies the average error accumulated across a layer of typical quantum gates. A lower EPLG indicates better overall system performance for executing quantum circuits. This metric is particularly useful for comparing the 'quality' of different quantum processors for running algorithms of a certain depth.
  • CLOPS (Circuit Layer Operations Per Second): Projected at 180K for 2025. CLOPS measures the throughput of the quantum processor, indicating how many layers of quantum gates can be executed per second. Higher CLOPS values mean faster execution times for quantum programs, which is critical for iterative algorithms or running many experiments.
  • Quantum Volume (QV): Reported as 'low due to errors' in 2022. Quantum Volume is an older, but still relevant, benchmark that attempts to quantify the largest 'square' quantum circuit (equal width and depth) that a quantum computer can reliably execute. A low QV, despite a high qubit count, underscores the challenge of maintaining coherence and fidelity across a large number of qubits simultaneously. This highlights the tradeoff between qubit scale and error rates, a common characteristic of NISQ devices.

Operational Limits: Understanding the practical constraints of a quantum system is vital for effective job submission and resource management.

• Shots: The system allows for unlimited shots per job, but this is practically constrained by the allocated time for the job. This model encourages users to optimize their shot count based on statistical requirements and available budget/time.
• Circuit Depth/Duration: Circuits can reach up to thousands of gates, though this is ultimately limited by coherence times. Coherence refers to the ability of a qubit to maintain its quantum state. As circuits get deeper, qubits spend more time interacting and are more susceptible to decoherence, leading to errors. This limitation is a primary driver for the need for error mitigation and, eventually, error correction.
• Queue Times: Typically, queue wait times are less than 1 hour, which is generally acceptable for experimental work, though it can fluctuate based on demand.

Software and Access: The IBM Quantum Eagle is primarily accessed via the IBM Quantum Platform and supports the Qiskit Runtime environment. The primary SDK for programming is Qiskit, IBM's open-source quantum computing framework, which provides tools for circuit construction, execution, and analysis. The native gate set includes SX, RZ, and ECR gates. SX and RZ are single-qubit gates, while ECR (Echoed Cross-Resonance) is a two-qubit entangling gate, forming a universal gate set capable of implementing any quantum algorithm.

Tradeoffs and Comparability: A key analytical takeaway for Eagle is the inherent tradeoff between qubit scale and higher error rates compared to newer, more optimized systems. While 127 qubits was a breakthrough, the error rates meant that not all 127 qubits could be reliably used in deep, complex circuits simultaneously. Furthermore, its CLOPS is slower compared to newer revisions and processors like Heron, indicating that while it offered scale, subsequent generations focused on improving the speed and fidelity of operations. When comparing Eagle to other systems, it's crucial to consider not just the raw qubit count but also the specific error rates, connectivity, and the type of benchmarks used, as these factors collectively determine the practical utility of the hardware for a given task. The 'low due to errors' Quantum Volume in 2022, despite the high qubit count, serves as a stark reminder of this complexity.

The IBM Quantum Eagle processor represents a significant chapter in IBM's ambitious quantum roadmap, marking a critical transition point in the industry's pursuit of larger-scale quantum systems. Its timeline reflects both rapid innovation and the iterative nature of hardware development.

• November 2021: First Announced. IBM officially unveiled the Eagle processor, touting it as the world's first quantum computer with over 100 qubits. This announcement generated considerable excitement, signaling a new era where quantum systems could begin to tackle more complex problems, even if still in the NISQ regime. For data analysts, this date marks the beginning of public discourse and initial performance claims that would later be refined.
• December 2021: First Available. Just a month after its announcement, Eagle became available to select partners and eventually to the broader public via the IBM Quantum Platform. This rapid deployment underscored IBM's commitment to making its cutting-edge hardware accessible, allowing researchers and developers to immediately begin experimenting with its unprecedented scale. This availability was crucial for gathering real-world performance data and feedback.
• 2024: Major Revisions (r3). The introduction of the 'r3' revision in 2024 brought significant improvements, primarily focusing on enhanced fidelity. This iterative development process is typical in quantum hardware, where initial designs are refined based on operational experience and advancements in fabrication and control techniques. For analysts, tracking these revisions is vital, as performance metrics can change substantially between versions, impacting the suitability of the hardware for specific tasks. The improved fidelity of r3 likely contributed to the projected 2025 benchmark figures like EPLG and CLOPS.
• Active but Limited; Roadmap to Phase Out for Heron by 2026. While Eagle remains active and accessible, its role is transitioning. IBM's roadmap indicates that Eagle is slated to be phased out in favor of newer, more performant architectures like Heron by 2026. This strategic decision reflects the relentless pace of quantum hardware development, where each generation aims to surpass its predecessor in terms of qubit count, connectivity, coherence, and error rates. For data analysts, this means that while Eagle provided invaluable experience with 100+ qubit systems, future work will increasingly shift to Heron and subsequent processors, which offer superior performance characteristics, particularly in terms of lower error rates and higher CLOPS. Eagle's legacy will be its pioneering role in demonstrating the feasibility and challenges of scaling superconducting quantum processors.

Verification confidence: High. Specs can vary by revision and access tier. Always cite the exact device name + date-stamped metrics.