arXiv subjects

Nathaniel Tornow

Publications and source records attributed to Nathaniel Tornow.

5 recordsLinked to original sources

arXiv2026

RushHour: A Dynamically Reconfigurable Lattice-Surgery Architecture

Practical fault-tolerant quantum computing (FTQC) requires efficient lattice surgery (LS), so that large algorithms fit on resource-constrained quantum chips. Existing approaches, however, are rigid: qubits, routing space, and resource states are allocated ahead of execution, which prevents running on small chips, leaves statically scheduled executions with large time overheads, and fixes each design at a single area of the space-time trade-off. We present dynamic LS, which enables efficient reconfiguration of the ancilla space, just-in-time allocation of resource states, and dynamic rotations of logical qubits, thereby spanning the entire space-time trade-off with a single, unified approach. We realize dynamic LS with RushHour through a hardware-compiler co-design: the RushHour ISA formalizes and programs our dynamic lattice model, the Lattice Management Unit abstracts dynamic lattice management and performs efficient lattice reconfiguration, and the RushHour Compiler compiles logical circuits for physical chips into optimized ISA programs while pipelining instructions. We evaluate RushHour against six state-of-the-art compilers and two resource models. On the smallest chips, 86% of benchmarks run only with RushHour, while existing approaches require 1.2-3.5$\times$ larger chips. On space-constrained early-FTQC chips, RushHour runs a median 2.3-7.2$\times$ faster than the best feasible alternative, while achieving results comparable to the state of the art on very large chips. RushHour's constructive results run 4.8$\times$ from an idealized-machine resource limit.

quant-ph↗

arXiv2024

Quantum-Classical Computing via Tensor Networks

Circuit knitting offers a promising path to the scalable execution of large quantum circuits by breaking them into smaller sub-circuits whose output is recombined through classical postprocessing. However, current techniques face excessive overhead due to a naive postprocessing method that neglects potential optimizations in the circuit structure. To overcome this, we introduce qTPU, a framework for scalable hybrid quantum-classical processing using tensor networks. By leveraging our hybrid quantum circuit contraction method, we represent circuit execution as the contraction of a hybrid tensor network (h-TN). The qTPU compiler automates efficient h-TN generation, optimizing the balance between estimated error and postprocessing overhead, while the qTPU runtime supports large-scale h-TN contraction using quantum and classical accelerators. Our evaluation shows orders-of-magnitude reductions in postprocessing overhead, a $10^4\times$ speedup in postprocessing, and a 20.7$\times$ reduction in overall runtime compared to the state-of-the-art Qiskit-Addon-Cutting (QAC).

quant-ph↗

arXiv2024

Qonductor: A Cloud Orchestrator for Quantum Computing

We describe Qonductor, a cloud orchestrator for hybrid quantum-classical applications that run on heterogeneous hybrid resources. Qonductor abstracts away the complexity of hybrid programming and resource management by exposing the Qonductor API, a high-level and hardware-agnostic API. The resource estimator strategically balances quantum and classical resources to mitigate resource contention and the effects of hardware noise. The hybrid scheduler automates job scheduling on hybrid resources and balances the tradeoff between users' objectives of QoS and the cloud operator's objective of resource efficiency. We implement an open-source prototype and evaluate Qonductor using more than 7000 real quantum runs on the IBM quantum cloud to simulate real cloud workloads. Qonductor achieves up to 54% lower job completion times (JCTs) while sacrificing 3% execution quality, balances the load across QPU, which increases quantum resource utilization by up to 66%, and scales with growing system sizes and loads.

quant-ph↗

arXiv2024

Scaling Quantum Computations via Gate Virtualization

We present the Quantum Virtual Machine (QVM), an end-to-end generic system for scalable execution of large quantum circuits with high fidelity on noisy and small quantum processors (QPUs) by leveraging gate virtualization. QVM exposes a virtual circuit intermediate representation (IR) that extends the notion of quantum circuits to incorporate gate virtualization. Based on the virtual circuit as our IR, we propose the QVM compiler - an extensible compiler infrastructure to transpile a virtual circuit through a series of modular optimization passes to produce a set of optimized circuit fragments. Lastly, these transpiled circuit fragments are executed on QPUs using our QVM runtime - a scalable and distributed infrastructure to virtualize and execute circuit fragments on a set of distributed QPUs. We evaluate QVM on IBM's 7- and 27-qubit QPUs. Our evaluation shows that using our system, we can scale the circuit sizes executable on QPUs up to double the size of the QPU while improving fidelity by 4.7$\times$ on average compared to larger QPUs and that we can effectively reduce circuit depths to only 40\% of the original circuit depths.

quant-ph↗

arXiv2024

QOS: A Quantum Operating System

Quantum computers face challenges due to hardware constraints, noise errors, and heterogeneity, and face fundamental design tradeoffs between key performance metrics such as \textit{quantum fidelity} and system utilization. This substantially complicates managing quantum resources to scale the size and number of quantum algorithms that can be executed reliably in a given time. We introduce QOS, a cloud operating system for managing quantum resources while mitigating their inherent limitations and balancing the design tradeoffs of quantum computing. QOS exposes a hardware-agnostic API for transparent quantum job execution, mitigates hardware errors, and systematically multi-programs and schedules the jobs across space and time to achieve high quantum fidelity in a resource-efficient manner. To achieve this, it leverages two key insights: First, to maximize utilization and minimize fidelity loss, some jobs are more compatible than others for multi-programming on the same quantum computer. Second, sacrificing minimal fidelity can significantly reduce job waiting times. We evaluate QOS on real quantum devices hosted by IBM, using 7000 real quantum runs of more than 70.000 benchmark instances. We show that the QOS achieves 2.6--456.5$\times$ higher fidelity, increases resource utilization by up to 9.6$\times$, and reduces waiting times by up to 5$\times$ while sacrificing only 1--3\% fidelity, on average, compared to the baselines.

quant-ph↗