At the 2025 Quantum Developer Conference, IBM Research Director Jay Gambetta and colleagues detailed advancements in algorithms, hardware, and software designed to facilitate the achievement of quantum advantage. The presentation outlined three candidate advantage experiments—observable estimation, variational algorithms, and problems with efficient classical verification—currently being tracked via a new, open, community-led initiative developed in collaboration with Flatiron, BlueQubit, and Algorithmiq. This tracker systematically evaluates potential quantum advantages against leading classical methods, focusing on metrics of efficiency, cost-effectiveness, and accuracy, as IBM argues rigorous validation remains a critical prerequisite for demonstrably surpassing classical computation.
Quantum Advantage: Current Status and Framework
Quantum advantage—where quantum computers demonstrably outperform classical ones—is nearing reality, but isn’t a simple finish line. IBM is actively tracking candidates across observable estimation, variational algorithms, and problems with efficient classical verification. A key framework focuses on rigorous validation and measurable improvements in efficiency, cost, or accuracy. An open, community-led “Advantage Tracker” is now available to systematically compare quantum results against leading classical methods, fostering transparency and collaborative progress.
Recent hardware advancements are crucial for scaling towards advantage. The new 120-qubit IBM Quantum Nighthawk chip features a square qubit topology with 218 couplers—a 30% increase in circuit complexity over previous designs. IBM also unveiled improvements to the Heron processor, achieving 330,000 CLOPS (compared to 200,000 in late 2024) and demonstrating significant speed-ups in quantum utility experiments—over 100x faster than in 2023. This represents a tangible step toward tackling more complex problems.
Software optimization is equally vital. The latest Qiskit SDK v2.2 boasts an 83x speed increase in transpilation compared to competitor Tket. New features like circuit annotations and the Samplomatic package allow developers greater control over error mitigation, reducing sampling overhead by up to 100x with techniques like probabilistic error cancellation. Dynamic circuits, incorporating classical computation mid-run, are now viable at scale—showing up to a 25% accuracy improvement with a 58% reduction in two-qubit gates.
New Hardware: IBM Quantum Nighthawk and Heron
IBM unveiled the 120-qubit “Nighthawk” processor, a key step toward achieving quantum advantage. Notably, Nighthawk features a square qubit topology with 218 couplers—an increase from Heron’s 176—allowing for the design of 30% more complex circuits with fewer SWAP gates. This improved connectivity aims to tackle larger, more challenging problems. IBM projects revisions capable of running circuits with 5,000, 7,500, 10,000, and ultimately 15,000 quantum gates, with a 5,000-gate milestone targeted by the end of 2025.
Alongside Nighthawk, IBM highlighted advancements in the “Heron” processor, now in its third revision. This version boasts the lowest two-qubit gate errors to date, with 57 of 176 couplings achieving less than one error per 1,000 operations. The entire fleet of Herons now delivers 330,000 CLOPS (quantum computational operations per second), a significant jump from 200,000 at the end of 2024. This increased performance enabled the quantum utility experiment to run in under 60 minutes—over 100x faster than in 2023.
Software is equally critical, and IBM’s open-source Qiskit SDK continues to lead in performance. Benchmarks show Qiskit v2.2 is 83x faster in transpiling than competitor Tket 2.6.0. New tools like the “Samplomatic” package offer greater control over circuit customization and error mitigation, reducing sampling overhead for techniques like probabilistic error cancellation by 100x. These advancements demonstrate a commitment to a collaborative, hybrid quantum-classical approach—essential for realizing practical quantum advantage.
Qiskit SDK: Performance and Latest Updates
The IBM Quantum team recently unveiled the 120-qubit Nighthawk processor, a key step towards scaling quantum advantage. Nighthawk features a square qubit topology with 218 couplers—a 30% increase over the Heron chip—allowing for more complex circuit designs with fewer SWAP gates. Future revisions aim to run circuits with 5,000, 7,500, 10,000, and ultimately 15,000 quantum gates, with a 5,000-gate milestone projected by year-end. This focus on both modularity and performance is critical for tackling increasingly complex problems.
Alongside hardware advancements, the open-source Qiskit SDK remains a cornerstone of IBM’s strategy. Benchmarks demonstrate Qiskit v2.2 is 83x faster in transpiling than the Tket 2.6.0 SDK, highlighting its continued performance leadership. New features, like circuit annotations and the Samplomatic package, offer granular control for circuit optimization and advanced error mitigation techniques. This enables developers to explore and refine circuits more efficiently, accelerating the path towards demonstrable quantum advantage.
Recent Qiskit enhancements focus on dynamic circuits – circuits that incorporate classical operations mid-run. Utilizing circuit annotations, IBM demonstrated up to a 25% accuracy improvement and a 58% reduction in two-qubit gates at 100+ qubits using dynamic circuits for a 46-site Ising model simulation. Moreover, Samplomatic reduces the sampling overhead of probabilistic error cancellation (PEC) by 100x. These improvements showcase the synergistic potential of combining quantum and classical computing for substantial gains.
Scaling Advantage with Dynamic Circuits
IBM’s push for quantum advantage centers on hardware capable of running increasingly complex circuits. The new 120-qubit Nighthawk chip features a square qubit topology with 218 couplers—a 30% increase over previous designs—allowing developers to tackle larger problems with fewer SWAP gates. Importantly, IBM aims to scale Nighthawk’s performance, targeting revisions capable of executing circuits with 5,000, 7,500, 10,000, and ultimately 15,000 quantum gates, with a 5,000-gate milestone projected by year-end 2025.
Beyond raw qubit count, software optimization is crucial. The open-source Qiskit SDK v2.2 is now 83x faster in transpiling than competing frameworks like Tket. This speed boost, combined with new tools like the Samplomatic package, allows for finer-grained control over circuit execution and the implementation of advanced error mitigation techniques. Specifically, Samplomatic enables customized circuit randomization, significantly reducing the overhead associated with methods like probabilistic error cancellation (PEC) – up to 100x improvement.
A key advancement lies in the development and deployment of dynamic circuits. These circuits integrate classical computation during quantum execution, leveraging mid-circuit measurements to make conditional changes. Demonstrations at QDC showed that dynamic circuits, utilizing features like deferred timing and stretched operations, deliver up to 25% more accurate results with a 58% reduction in two-qubit gates at scales exceeding 100 qubits – exemplified by a 46-site Ising model simulation.
Advanced Error Mitigation Techniques
Recent advancements at IBM Quantum focus heavily on scaling error mitigation, crucial for achieving demonstrable quantum advantage. The new IBM Quantum Nighthawk processor, boasting 120 qubits with a square topology and 218 couplers, allows for 30% more complex circuit designs with fewer SWAP gates. Further, improvements to the Heron processor now deliver less than one error in 1000 operations on 57 of its qubit couplings, and the fleet now achieves 330,000 CLOPS – a significant jump from 200,000 last year.
Beyond hardware, the Qiskit SDK v2.2 demonstrates impressive software performance, transpiling circuits 83x faster than competing SDKs like Tket. Crucially, the Samplomatic package provides granular control over circuit regions via annotations, enabling efficient implementation of advanced error mitigation techniques. This allows developers to efficiently apply methods like probabilistic error cancellation (PEC) while minimizing overhead—reducing PEC sampling requirements by up to 100x.
Dynamic circuits, integrated through Qiskit’s annotation features, represent a significant leap forward. By incorporating classical operations mid-run, these circuits achieved up to 25% more accurate results with a 58% reduction in two-qubit gates at scales exceeding 100 qubits. Demonstrations with a 46-site Ising model simulation highlight the tangible benefits of dynamic circuits over static approaches at utility-scale, proving that combined quantum and classical approaches are key to realizing practical quantum computing.
HPC Integration and the Quantum Community
IBM is prioritizing integration between quantum hardware and high-performance computing (HPC) to accelerate the path to quantum advantage. The newly unveiled 120-qubit Nighthawk processor boasts a square qubit topology with 218 couplers – a 30% increase in circuit complexity capacity over previous designs. Beyond hardware, software is critical; the Qiskit SDK v2.2 demonstrates an 83x speed improvement in transpilation compared to competitor Tket 2.6.0, showcasing a commitment to robust software tools for advantage workloads.
Scaling quantum advantage requires advanced error mitigation techniques, and IBM is delivering with tools like Samplomatic. This package allows for customized circuit randomization and significantly reduces the sampling overhead of Probabilistic Error Cancellation (PEC) – by up to 100x – enabling more efficient noise reduction. Demonstrations showed a 25% accuracy improvement and a 58% reduction in two-qubit gates at 100+ qubit scales using dynamic circuits—showing utility-scale benefits over static designs.
The drive isn’t solely about quantum; HPC integration is vital. IBM highlighted utility-scale dynamic circuits—which incorporate classical operations mid-run—and demonstrated their ability to leverage mid-circuit measurements and feedforward operations. By combining quantum processing with classical control, IBM is aiming to enhance accuracy and reduce gate counts, ultimately pushing toward realizing impactful quantum applications that outperform classical methods – a core tenet of achieving true quantum advantage.
