arXiv subjects

Athena Caesura

Publications and source records attributed to Athena Caesura.

8 recordsLinked to original sources

arXiv2026

Compiling the 2D Fermi-Hubbard ground-state energy estimation algorithm for active volume quantum architectures

As quantum computing enters the early fault-tolerant era, circuit compilation choices will increasingly depend on details of the underlying architecture rather than solely optimizing for generic proxies such as non-Clifford count. We present an active-volume-aware compilation of the ground-state energy estimation algorithm for the two-dimensional Fermi-Hubbard model using quantum phase estimation and Trotterized time evolution. The proposed compilation reduces the active volume across $L\times L$ square lattices with $L=4$ to $20$, achieving up to a $3.9\times$ reduction over prior work optimized for non-Clifford cost. As a by-product of these compilation improvements, the resulting circuits also achieve state-of-the-art Toffoli counts, with a ~$2\times$ reduction for the $L=20$ case. Lastly, the active volume architecture and recent execution scheduling advances provide a means of translating these reduction trends into runtime. This demonstrates the increasing importance of architecture-aware compilation for practical early fault-tolerant quantum computing.

quant-ph↗

arXiv2026

Faster quantum chemistry simulations on a quantum computer with improved tensor factorization and active volume compilation

Electronic structure calculations of molecular systems are among the most promising applications for fault-tolerant quantum computing (FTQC) in quantum chemistry and drug design. However, while recent algorithmic advancements such as qubitization and Tensor Hypercontraction (THC) have significantly reduced the complexity of such calculations, they do not yet achieve computational runtimes short enough to be practical for industrially relevant use cases. In this work, we introduce several advances to electronic structure calculation for molecular systems, resulting in a two-orders-of-magnitude speedup of estimated runtimes over prior-art algorithms run on comparable quantum devices. One of these advances is a novel framework for block-invariant symmetry-shifted Tensor Hypercontraction (BLISS-THC), with which we achieve the tightest Hamiltonian factorizations reported to date. We compile our algorithm for an Active Volume (AV) architecture, a technical layout that has recently been proposed for fusion-based photonic quantum hardware. AV compilation contributes towards a lower runtime of our computation by eliminating overheads stemming from connectivity issues in the underlying surface code. We present a detailed benchmark of our approach, focusing primarily on the computationally challenging benchmark molecule P450. Leveraging a number of hardware tradeoffs in interleaving-based photonic FTQC, we estimate runtimes for the electronic structure calculation of P450 as a function of the device footprint.

quant-ph↗

arXiv2026

Superconducting qubits in the millions: the potential and limitations of modularity

The development of fault-tolerant quantum computers (FTQCs) is receiving increasing attention within the quantum computing community. Like conventional digital computers, FTQCs, which utilize error correction and millions of physical qubits, have the potential to address some of humanity's grand challenges. However, accurate estimates of the tangible scale of future FTQCs, based on transparent assumptions, are uncommon. How many physical qubits are necessary to solve a practical problem intractable for classical hardware? What costs arise from distributing quantum computation across multiple machines? This paper presents an architectural model of a potential FTQC based on superconducting qubits, divided into discrete modules and interconnected via coherent links. We employ a resource estimation framework and software tool to assess the physical resources required to execute specific quantum algorithms compiled into their graph-state form and arranged onto a modular superconducting hardware architecture. Our tool can predict the size, power consumption, and execution time of these algorithms based on explicit assumptions about the system's physical layout, thermal load, and modular connectivity. We assess the resources needed for quantum computation examples that serve as building blocks of proposed applications, quantifying the architectural bottlenecks and trade-offs that remain to be addressed to deliver utility.

quant-ph↗

arXiv2026

A Scheduler for the Active Volume Architecture

We improve the accuracy of Active Volume resource estimates by explicitly scheduling when Active Volume blocks execute. We present software that uses a greedy strategy to assign each logical qubit a role in each logical cycle (e.g., workspace, stale state storage, and bridge qubits). We empirically derive a novel formula for bridge- and stale-state-qubit overheads and improve the accuracy of runtime estimates, revealing that larger circuits can run on a given computer than previously predicted by analytic models. For a $4\times4$ Fermi-Hubbard simulation test circuit, this yields a $1.76\times$ runtime speedup with a $1.44\times$ reduction in bridge- and stale-state-qubit overheads compared to the model used in arXiv:2501.06165. Moreover, we show that for this test circuit, reaction times are insignificant in runtime estimates for computers with fewer than 600 logical qubits and that the number of reaction layers per logical cycle remains 1 in this regime. Our results pave the way for a full compilation pipeline for the Active Volume architecture and improved analytic resource estimates.

quant-ph↗

arXiv2025

Assessing Finite Scalability in Early Fault-Tolerant Quantum Computing for Homogeneous Catalysts

As quantum hardware advances toward fault-tolerant operation, an intermediate stage known as early fault-tolerant quantum computing (EFTQC) is emerging, where partial error correction enables meaningful computation. In this regime, the ability of quantum processors to scale in size and depth has become a crucial factor shaping their achievable performance. This study investigates how finite scalability influences resource requirements for simulating open-shell catalytic systems using Quantum Phase Estimation (QPE). The analysis compares hardware archetypes distinguished by fidelity or operation speed under two representative scalability models. Finite scalability increases qubit and runtime demands yet leaves overall scaling behavior intact, with high-fidelity architectures requiring lower minimum scalability to solve equally sized problems. These effects are largely independent of the chosen scalability model. Extending this framework, we examine runtime competitiveness across hardware and code configurations, incorporating surface-code and quantum Low-Density Parity-Check (LDPC)-based fault tolerance under finite scalability. The results identify operating regimes where high-fidelity architectures remain competitive despite slower gate speeds and show that LDPC codes further expand this regime by reducing space-time overhead. Together, these findings highlight the central role of scalability in quantifying performance and guiding the design of next-generation quantum hardware. Continued progress in scalable architectures will be essential for extending quantum computing to increasingly complex scientific and industrial applications.

quant-ph↗

arXiv2025

Design and efficiency in graph-state computation

The algorithm-specific graph and circuit etching are two strategies for compiling a graph state to implement quantum computation. Benchmark testing exposed limitations to the proto-compiler, Jabalizer giving rise to Etch (https://github.com/QSI-BAQS/Etch), an open-source, circuit-etching tool for transpiling a quantum circuit to a graph state. The viability of circuit etching is evaluated, both as a resource allocation strategy for distilling magic states and as an alternative to the algorithm-specific graph strategy as realised in Jabalizer. Experiments using Etch to transpile IQP circuits to an equivalent graph state resulted in higher ratios of Pauli qubits to non-Pauli qubit than required for efficient magic state distillation. Future research directions for the algorithm-specific graph and circuit-etching strategies are proposed.

quant-ph↗

arXiv2024

Quantifying fault tolerant simulation of strongly correlated systems using the Fermi-Hubbard model

Understanding the physics of strongly correlated materials is one of the grand challenge problems for physics today. A large class of scientifically interesting materials, from high-$T_c$ superconductors to spin liquids, involve medium to strong correlations, and building a holistic understanding of these materials is critical. Doing so is hindered by the competition between the kinetic energy and Coulomb repulsion, which renders both analytic and numerical methods unsatisfactory for describing interacting materials. Fault-tolerant quantum computers have been proposed as a path forward to overcome these difficulties, but this potential capability has not yet been fully assessed. Here, using the multi-orbital Fermi-Hubbard model as a representative model and a source of scalable problem specifications, we estimate the resource costs needed to use fault-tolerant quantum computers for obtaining experimentally relevant quantities such as correlation function estimation. We find that advances in quantum algorithms and hardware will be needed in order to reduce quantum resources and feasibly address utility-scale problem instances.

quant-ph↗

arXiv2023

Early Fault-Tolerant Quantum Computing

Over the past decade, research in quantum computing has tended to fall into one of two camps: near-term intermediate scale quantum (NISQ) and fault-tolerant quantum computing (FTQC). Yet, a growing body of work has been investigating how to use quantum computers in transition between these two eras. This envisions operating with tens of thousands to millions of physical qubits, able to support fault-tolerant protocols, though operating close to the fault-tolerant threshold. Two challenges emerge from this picture: how to model the performance of devices that are continually improving and how to design algorithms to make the most use of these devices? In this work we develop a model for the performance of early fault-tolerant quantum computing (EFTQC) architectures and use this model to elucidate the regimes in which algorithms suited to such architectures are advantageous. As a concrete example, we show that, for the canonical task of phase estimation, in a regime of moderate scalability and using just over one million physical qubits, the ``reach'' of the quantum computer can be extended (compared to the standard approach) from 90-qubit instances to over 130-qubit instances using a simple early fault-tolerant quantum algorithm, which reduces the number of operations per circuit by a factor of 100 and increases the number of circuit repetitions by a factor of 10,000. This clarifies the role that such algorithms might play in the era of limited-scalability quantum computing.

quant-ph↗