Q-CTRL runs 100-qubit quantum algorithm on IBM hardware

Q-CTRL researchers have successfully executed a 100-qubit Quantum Fourier Transform on an IBM Quantum computer, establishing a new benchmark that doubles the size of any previously demonstrated experimental QFT.

The team encoded signals into quantum registers representing a Hilbert space of 2¹⁰⁰ possibilities, more than 1030 potential answers, and isolated the single correct frequency despite inherent hardware noise.“At Q-CTRL, our mission is to make quantum technology useful,” the company states, highlighting how this work optimizes hardware performance and extracts meaningful results from high-dimensional quantum states. When run on a 156-qubit IBM Heron r3 processor, the correct target integer frequency emerged as the unique mode result across all test circuits up to 100 qubits.Executing a 100-qubit Quantum Fourier Transform represents a significant leap forward in quantum computing, as demonstrated by researchers at Q-CTRL on IBM Quantum hardware. This achievement surpasses previous experimental QFTs by a factor of two, establishing a new benchmark for implementing crucial algorithmic subroutines on physical quantum processors. The QFT, a cornerstone of many quantum algorithms including Shor’s algorithm for factoring, has long been hampered by error accumulation and routing challenges when scaled up on real devices.The core of this advancement lies in Q-CTRL’s ability to extract meaningful information from a Hilbert space encompassing 2¹⁰⁰ possibilities, a staggering number exceeding 1030. Successfully isolating the correct target frequency within this vast landscape confirms that current quantum processors can handle high-dimensional quantum states despite inherent noise.At 50 qubits, the target bitstring was 8.4 times more frequent than any incorrect output in the raw measurement data, achieving a unitary process fidelity of 11.4%. This success wasn’t simply a matter of scaling up existing techniques; it required a combined approach of hardware-aware compilation and active error suppression. Q-CTRL developed a novel QFT compilation strategy, termed Convolutional QFT, which incorporates a single ancilla qubit to compress circuit logic into a compact kernel.This kernel steps sequentially along the qubit register, minimizing the number of entangling gates and, consequently, noise accumulation within each qubit’s causal history. The design also allows for uninterrupted dynamical decoupling sequences, protecting the fragile quantum state from decoherence and crosstalk during idle periods.

The team employed a technique of truncating the smallest rotations within the QFT, striking a balance between algorithmic synthesis error and the reduction of noisy two-qubit gates. This prevents hardware noise from overwhelming the circuit.Precise scheduling of algorithmic operations, error-suppression sequences, and quantum measurements further enhanced error suppression. Statistical mitigation of measurement errors was also used to isolate the fidelity of the unitary QFT itself. The Convolutional QFT achieves a gate complexity of n² – n + 2 CX gates for a n-qubit QFT on the IBM Heron processor, virtually matching the gate count of an idealized all-to-all connected architecture, thereby minimizing routing penalties.The demonstration of a 100-qubit QFT underscores the potential of this technology to unlock hardware for executing algorithms at scale. The ability to maintain coherence and low gate errors across a 100-qubit linear chain confirms that pre-fault-tolerant processors can now extract computationally meaningful results at an unprecedented scale, demonstrating that complex algorithmic building blocks can survive across large qubit counts.Extracting a definitive signal from a quantum computation becomes exponentially more difficult as the number of qubits increases, yet Q-CTRL researchers recently demonstrated the ability to discern a target frequency within a Hilbert space of 2¹⁰⁰ possibilities. This immense computational space, exceeding 1030 potential outcomes, presented a significant hurdle overcome through a combination of optimized algorithm design and hardware-aware compilation.Successfully identifying the correct frequency amidst such vastness confirms the potential for extracting meaningful information from increasingly complex quantum systems. This achievement represents a 100% increase in register width over any previously benchmarked experimental QFT, establishing a new scale for implementing these fundamental building blocks of quantum computation. Source: https://q-ctrl.com/blog/breaking-the-100-qubit-barrier-executing-the-quantum-fourier-transform-at-scale-on-ibm-hardware See today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals.Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing.

For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.