arXiv subjects

Andres Paz

Publications and source records attributed to Andres Paz.

8 recordsLinked to original sources

arXiv2026

qstack: Compositional End-to-End Compilation for Fault-Tolerant Quantum Programs

Compiling quantum programs for fault-tolerant execution requires transforming high-level operations through multiple abstraction layers: from logical gates to error-corrected encodings to hardware-native instructions. A key challenge is that quantum error correction turns purely quantum programs into hybrid quantum-classical programs, where classical feedback from syndrome measurements drives quantum corrections at runtime. Existing compilation frameworks handle these quantum and classical components separately, requiring manual adaptation of classical logic at each compilation stage, all while preserving program semantics. We present qstack, a compiler framework built around a purely quantum intermediate representation in which classical logic is accessed only through opaque callbacks, written in any classical language. The framework's central mechanism, callback wrapping, enables compositional compilation: each compiler pass automatically adapts both quantum operations and their associated classical callbacks, and any kernel dynamically generated by a callback is compiled through the full pipeline. This allows ISA translation and quantum error correction to be expressed as composable compiler passes, including concatenation of error-correcting codes, without manual intervention. We demonstrate end-to-end compilation from a high-level gate set through Clifford gates to trapped-ion native operations, with bit-flip and phase-flip repetition codes, the Steane code, and the Shor code obtained by composing two repetition passes.

quant-ph↗

arXiv2026

StabilizerBench: A Benchmark for AI-Assisted Quantum Error Correction Circuit Synthesis

As quantum hardware scales toward fault tolerant operation, the demand for correct quantum error correction (QEC) circuits far outpaces manual design capacity. AI agents offer a promising path to automating this synthesis, yet no benchmark exists to measure their progress on the specialized task of generating QEC circuits. We introduce StabilizerBench, a benchmark suite of 192 stabilizer codes spanning 12 families, 4-196 qubits, and distances 2-21, organized into three tasks of increasing difficulty: state preparation circuit generation, circuit optimization under semantic constraints, and fault tolerant circuit synthesis. Although motivated by QEC, stabilizer circuits exercise core competencies required for general quantum programming, including gate decomposition, qubit routing, and semantic preserving transformations, while admitting efficient verification via the Gottesman Knill theorem, enabling the benchmark to scale to large codes without the exponential cost of full unitary comparison. We define a unified generator weighted scoring system with two tiers: a capability score measuring breadth of success and a quality score capturing circuit merit. We also introduce continuous fault tolerance and optimization metrics that grade error resilience and circuit improvements beyond binary pass or fail. Following the design of classical benchmarks such as SWE-bench, StabilizerBench specifies inputs, verification oracles, and scoring but leaves prompts and agent strategies open. We evaluate three frontier AI agents and find the benchmark discriminates across models and tasks with substantial headroom for improvement.

quant-ph↗

arXiv2024

Fault-tolerant quantum computation with a neutral atom processor

Quantum computing experiments are transitioning from running on physical qubits to using encoded, logical qubits. Fault-tolerant computation can identify and correct errors, and has the potential to enable the dramatically reduced logical error rates required for valuable algorithms. However, it requires flexible control of high-fidelity operations performed on large numbers of qubits. We demonstrate fault-tolerant quantum computation on a quantum processor with 256 qubits, each an individual neutral Ytterbium atom. The operations are designed so that key error sources convert to atom loss, which can be detected by imaging. Full connectivity is enabled by atom movement. We demonstrate the entanglement of 24 logical qubits encoded into 48 atoms, at once catching errors and correcting for, on average 1.8, lost atoms. We also implement the Bernstein-Vazirani algorithm with up to 28 logical qubits encoded into 112 atoms, showing better-than-physical error rates. In both cases, "erasure conversion," changing errors into a form that can be detected independently from qubit state, improves circuit performance. These results begin to clear a path for achieving scientific quantum advantage with a programmable neutral atom quantum processor.

quant-ph↗

arXiv2024

End-to-End Quantum Simulation of a Chemical System

We demonstrate the first end-to-end integration of high-performance computing (HPC), reliable quantum computing, and AI in a case study on catalytic reactions producing chiral molecules. We present a hybrid computation workflow to determine the strongly correlated reaction configurations and estimate, for one such configuration, its active site's ground state energy. We combine 1) the use of HPC tools like AutoRXN and AutoCAS to systematically identify the strongly correlated chemistry within a large chemical space with 2) the use of logical qubits in the quantum computing stage to prepare the quantum ground state of the strongly correlated active site, demonstrating the advantage of logical qubits compared to physical qubits, and 3) the use of optimized quantum measurements of the logical qubits with so-called classical shadows to accurately predict various properties of the ground state including energies. The combination of HPC, reliable quantum computing, and AI in this demonstration serves as a proof of principle of how future hybrid chemistry applications will require integration of large-scale quantum computers with classical computing to be able to provide a measurable quantum advantage.

quant-ph↗

arXiv2024

Demonstration of quantum computation and error correction with a tesseract code

A critical milestone for quantum computers is to demonstrate fault-tolerant computation that outperforms computation on physical qubits. The tesseract subsystem color code protects four logical qubits in 16 physical qubits, to distance four. Using the tesseract code on Quantinuum's trapped-ion quantum computers, we prepare high-fidelity encoded graph states on up to 12 logical qubits, beneficially combining for the first time fault-tolerant error correction and computation. We also protect encoded states through up to five rounds of error correction. Using performant quantum software and hardware together allows moderate-depth logical quantum circuits to have an order of magnitude less error than the equivalent unencoded circuits.

quant-ph↗

arXiv2023

How to choose a decoder for a fault-tolerant quantum computer? The speed vs accuracy trade-off

Achieving practical quantum advantage requires a classical decoding algorithm to identify and correct faults during computation. This classical decoding algorithm must deliver both accuracy and speed, but in what combination? When is a decoder "fast enough" or "accurate enough"? In the case of surface codes, tens of decoding algorithms have been proposed, with different accuracies and speeds. However, it has been unclear how to choose the best decoder for a given quantum architecture. Should a faster decoder be used at the price of reduced accuracy? Or should a decoder sacrifice accuracy to fit within a given time constraint? If a decoder is too slow, it may be stopped upon reaching a time bound, at the price of some time-out failures and an increased failure rate. What then is the optimal stopping time of the decoder? By analyzing the speed vs. accuracy tradeoff, we propose strategies to select the optimal stopping time for a decoder for different tasks. We design a protocol to select the decoder that minimizes the spacetime cost per logical gate, for logical computation of a given depth. Our protocol enables comparison of different decoders, and the selection of an appropriate decoder for a given fault-tolerant quantum computing architecture. We illustrate our protocol for the surface code equipped with a desktop implementation of the PyMatching decoder. We estimate PyMatching is fast enough to implement thousands of logical gates with a better accuracy than physical qubits. However, we find it is not sufficiently fast to reach 10^5 logical gates, under certain assumptions, due to the decoding delay which forces qubits to idle and accumulate errors while idling. We expect further improvements to PyMatching are possible by running it on a better machine or by reducing the OS interference.

quant-ph↗

arXiv2021

Systematically reviewing the layered architectural pattern principles and their use to reconstruct software architectures

Architectural reconstruction is a reverse engineering activity aiming at recovering the missing decisions on a system. It can help identify the components, within a legacy software application, according to the application's architectural pattern. It is useful to identify architectural technical debt. We are interested in identifying layers within a layered application since the layered pattern is one of the most used patterns to structure large systems. Earlier component reconstruction work focusing on that pattern relied on generic component identification criteria, such as cohesion and coupling. Recent work has identified architectural-pattern specific criteria to identify components within that pattern. However, the architectural-pattern specific criteria that the layered pattern embodies are loosely defined. In this paper, we present a first systematic literature review (SLR) of the literature aiming at inventorying such criteria for layers within legacy applications and grouping them under four principles that embody the fundamental design principles under-lying the architectural pattern. We identify six such criteria in the form of design rules. We also perform a second systematic literature review to synthesize the literature on software architecture reconstruction in the light of these criteria. We report those principles, the rules they encompass, their representation, and their usage in software architecture reconstruction.

cs.SE↗

arXiv2018

Q#: Enabling scalable quantum computing and development with a high-level domain-specific language

Quantum computing exploits quantum phenomena such as superposition and entanglement to realize a form of parallelism that is not available to traditional computing. It offers the potential of significant computational speed-ups in quantum chemistry, materials science, cryptography, and machine learning. The dominant approach to programming quantum computers is to provide an existing high-level language with libraries that allow for the expression of quantum programs. This approach can permit computations that are meaningless in a quantum context; prohibits succinct expression of interaction between classical and quantum logic; and does not provide important constructs that are required for quantum programming. We present Q#, a quantum-focused domain-specific language explicitly designed to correctly, clearly and completely express quantum algorithms. Q# provides a type system, a tightly constrained environment to safely interleave classical and quantum computations; specialized syntax, symbolic code manipulation to automatically generate correct transformations of quantum operations, and powerful functional constructs which aid composition.

quant-ph↗