Quantum Art Achieves 10X Circuit Depth Compression and 30% Error Reduction Through NVIDIA Collaboration

Quantum Art, a developer of full-stack quantum computers based on trapped-ion qubits, has announced achieving 10x compression in circuit depth and a 30% reduction in error rates by compiling circuits with its all-to-all connected multi-qubit gates on NVIDIA accelerated computing using the NVIDIA CUDA-Q platform. This breakthrough represents a substantial advancement in quantum computing performance and brings commercial quantum applications closer to reality.

The company's fully programmable, all-to-all connected multi-qubit gates and advanced compiler serve as critical resources for implementing circuits at smaller depth, enabling faster runtime and higher performance. This technological achievement significantly shortens the path to commercial applications at scale, potentially accelerating quantum computing adoption across industries including pharmaceuticals, materials science, and financial modeling.

Quantum Art's general-purpose compiler automatically optimizes input circuits and substitutes standard operations with efficient multi-qubit gates, consistently delivering order-of-magnitude compression and substantial performance gains. These improvements, building on the CUDA-Q integration announced earlier this year, were verified in simulation on NVIDIA CUDA-Q quantum-classical integration framework, as documented in the company's technical resources available at https://www.quantum-art.tech/resources/quantum-art-achieves-10x-circuit-depth-compression.

Dr. Tal David, CEO of Quantum Art, emphasized the strategic importance of this development, stating that programmable all-to-all multi-qubit gates represent a critical advancement supporting the company's long-term goal of fault tolerant, commercially viable quantum computing. The architecture was specifically designed to deliver real performance gains rather than theoretical improvements.

Dr. Amit Ben-Kish, CTO and co-founder of Quantum Art, explained that their compilation technique demonstrates how multi-qubit gates and optimized compilers can compress quantum circuits by an order of magnitude while simultaneously improving performance by 30%. The general-purpose compiler optimizes very large quantum circuits with few multi-qubit gates, with this compilation verified using the NVIDIA CUDA-Q platform to operate NVIDIA AI infrastructure.

Sam Stanwyck, Group Product Manager for quantum computing at NVIDIA, highlighted the broader implications of this achievement, noting that by allowing researchers to draw on accelerated computing for their work, NVIDIA CUDA-Q is enabling next-generation breakthroughs in quantum computing. Quantum Art's use of CUDA-Q to achieve circuit depth compression and error reduction serves as a clear example of how meaningful performance improvements are being realized by leveraging the latest advances in AI supercomputing.

This breakthrough further validates and aligns with Quantum Art's broader roadmap, which centers on scaling multi-qubit gates and reconfigurable multi-core architectures to deliver increasingly powerful quantum systems. The successful integration of Quantum Art's hardware-aware compilation with the NVIDIA accelerated computing ecosystem underscores the promise of collaborative approaches in advancing quantum computing technology toward practical, commercial applications that could transform multiple industries and scientific research domains.