arXiv subjects

Victory Omole

Publications and source records attributed to Victory Omole.

11 recordsLinked to original sources

arXiv2026

Resource Estimation via Efficient Compilation of Key Quantum Primitives

Resource estimation is a significant challenge in evaluating fault tolerant quantum computers. Existing approaches often rely on either fixed architectural assumptions or coarse analytical models that fail to capture the interaction between hardware constraints and circuit compilation. This challenge is particularly acute for neutral atom quantum computers, where architectural features such as atom movement, measurement zones, and multi-species arrays introduce a broad design space for implementing fault tolerant computation. Addressing the need for a tighter feedback loop between hardware design and practical application development, we present a compilation-driven framework for quantum resource estimation that translates arbitrary quantum circuits into logical primitive operations with known physical resource costs. This framework allows for easily configurable hardware assumptions that enable rapid comparison of different architectural design choices. We apply our approach to two early fault tolerant quantum simulation and optimization workloads, assuming the use of the surface code, revealing several architectural trends. While the production of magic states continues to be the dominant source of overhead for these benchmarks, access to movement can save time on cultivation and important transversal gates. As problem size grows, routing and qubit movement become dominant bottlenecks, highlighting the need for movement-aware compiler optimizations and frugal routing strategies. Finally, our results suggest that neutral atom architectures combining dual-species arrays with controlled qubit movement offer a promising path toward near-term advantage on fault tolerant devices.

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arXiv2025

Toward Quantum-Enabled Biomarker Discovery: An Outlook from Q4Bio

We present a case study and forward-looking perspective on co-design for hybrid quantum-classical algorithms, centered on the goal of empirical quantum advantage (EQA), which we define as a measurable performance gain using quantum hardware over state-of-the-art classical methods on the same task. Because classical algorithms continue to improve, the EQA crossover point is a moving target; nevertheless, we argue that a persistent advantage is possible for our application class even if the crossover point shifts. Specifically, our team examines the task of biomarker discovery in precision oncology. We push the limitations of the best classical algorithms, improving them as best as we can, and then augment them with a quantum subroutine for the task where we are most likely to see performance gains. We discuss the implementation of a quantum subroutine for feature selection on current devices, where hardware constraints necessitate further co-design between algorithm and physical device capabilities. Looking ahead, we perform resource analysis to explore a plausible EQA region on near/intermediate-term hardware, considering the impacts of advances in classical and quantum computing on this regime. Finally, we outline potential clinical impact and broader applications of this hybrid pipeline beyond oncology.

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arXiv2025

Demonstration of a Logical Architecture Uniting Motion and In-Place Entanglement

We demonstrate a logical neutral atom architecture that integrates atom motion with in-place entanglement to achieve lower overheads than entangling-zone approaches. Using a 114-qubit device, we perform three proof-of-principle logical-qubit experiments. First, we implement a pre-compiled, non-scalable variant of Shor's algorithm, observing improved logical-over-physical performance, including with loss correction and leakage detection, achieving up to a 2x reduction in TVD. Second, we construct constant-depth logical CX ladders; on current hardware these execute with serial entangling operations, yet still yield 2-4x lower error for 8 and 12 logical qubits. Third, we prepare the [[16,4,4]] code and perform single-round decoding with post-processed error correction, achieving 8x improvement on logical vs physical. These results demonstrate how combining motion with in-place entanglement offers lower overhead than entangling-zone approaches.

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arXiv2024

Fault-Tolerant Operation and Materials Science with Neutral Atom Logical Qubits

We report on the fault-tolerant operation of logical qubits on a neutral atom quantum computer, with logical performance surpassing physical performance for multiple circuits including Bell states (12x error reduction), random circuits (15x), and a prototype Anderson Impurity Model ground state solver for materials science applications (up to 6x, non-fault-tolerantly). The logical qubits are implemented via the [[4, 2, 2]] code (C4). Our work constitutes the first complete realization of the benchmarking protocol proposed by Gottesman 2016 [1] demonstrating results consistent with fault-tolerance. In light of recent advances on applying concatenated C4/C6 detection codes to achieve error correction with high code rates and thresholds, our work can be regarded as a building block towards a practical scheme for fault tolerant quantum computation. Our demonstration of a materials science application with logical qubits particularly demonstrates the immediate value of these techniques on current experiments.

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arXiv2024

Sphere Packing on a Quantum Computer for Chromatography Modeling

Column chromatography is an important process in downstream biopharmaceutical manufacturing that enables high-selectivity separation of proteins through various modalities, such as affinity, ion exchange, hydrophobic interactions, or a combination of the aforementioned modes. Current mechanistic models of column chromatography typically abstract particle-level phenomena, in particular adsorption kinetics. A mechanistic model capable of incorporating particle-level phenomena would increase the value derived from mechanistic models. To this end, we model column chromatography via sphere packing, formulating three versions, each with increasing complexity. The first, homogeneous circle packing, is recast as maximum independent set and solved by the Quantum Approximate Optimization Algorithm on a quantum computer. The second, heterogeneous circle packing, is formulated as a graphical optimization problem and solved via classical simulations, accompanied by a road map to a quantum solution. An extension to the third, heterogeneous sphere packing, is formulated mathematically in a manner suitable to a quantum solution. Finally, detailed resource scaling is conducted to estimate the quantum resources required to simulate the most realistic model, providing a pathway to quantum advantage.

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arXiv2024

Noise-Aware Circuit Compilations for a Continuously Parameterized Two-Qubit Gateset

State-of-the-art noisy-intermediate-scale quantum (NISQ) processors are currently implemented across a variety of hardware platforms, each with their own distinct gatesets. As such, circuit compilation should not only be aware of, but also deeply connect to, the native gateset and noise properties of each. Trapped-ion processors are one such platform that provides a gateset that can be continuously parameterized across both one- and two-qubit gates. Here we use the Quantum Scientific Computing Open User Testbed (QSCOUT) to study noise-aware compilations focused on continuously parameterized two-qubit $\mathcal{ZZ}$ gates (based on the M{\o}lmer-S{\o}rensen interaction) using $\textbf{Superstaq}$, a quantum software platform for hardware-aware circuit compiler optimizations. We discuss the realization of $\mathcal{ZZ}$ gates with arbitrary angle on the all-to-all connected trapped-ion system. Then we discuss a variety of different compiler optimizations that innately target these $\mathcal{ZZ}$ gates and their noise properties. These optimizations include moving from a restricted maximally entangling gateset to a continuously parameterized one, swap mirroring to further reduce total entangling angle of the operations, focusing the heaviest $\mathcal{ZZ}$ angle participation on the best performing gate pairs, and circuit approximation to remove the least impactful $\mathcal{ZZ}$ gates. We demonstrate these compilation approaches on the hardware with randomized quantum volume circuits, observing the potential to realize a larger quantum volume as a result of these optimizations. Using differing yet complementary analysis techniques, we observe the distinct improvements in system performance provided by these noise-aware compilations and study the role of stochastic and coherent error channels for each compilation choice.

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arXiv2024

Average circuit eigenvalue sampling on NISQ devices

Average circuit eigenvalue sampling (ACES) was introduced by Flammia in arXiv:2108.05803 as a protocol to characterize the Pauli error channels of individual gates across the device simultaneously. The original paper posed using ACES to characterize near-term devices as an open problem. This work advances in this direction by presenting a full implementation of ACES for real devices and deploying it to Superstaq arXiv:2309.05157, along with a device-tailored resource estimation obtained through simulations and experiments. Our simulations show that ACES is able to estimate one- and two-qubit non-uniform Pauli error channels to an average eigenvalue absolute error of under $0.003$ and total variation distance of under 0.001 between simulated and reconstructed probability distributions over Pauli errors with $10^5$ shots per circuit using 5 circuits of depth 14. The question of estimating general error channels through twirling techniques in real devices remains open, as it is dependent on a device's native gates, but simulations with the Clifford set show results in agreement with reported hardware data. Experimental results on IBM's Algiers and Osaka devices are presented, where we characterize their error channels as Pauli channels without twirling.

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arXiv2023

Superstaq: Deep Optimization of Quantum Programs

We describe Superstaq, a quantum software platform that optimizes the execution of quantum programs by tailoring to underlying hardware primitives. For benchmarks such as the Bernstein-Vazirani algorithm and the Qubit Coupled Cluster chemistry method, we find that deep optimization can improve program execution performance by at least 10x compared to prevailing state-of-the-art compilers. To highlight the versatility of our approach, we present results from several hardware platforms: superconducting qubits (AQT @ LBNL, IBM Quantum, Rigetti), trapped ions (QSCOUT), and neutral atoms (Infleqtion). Across all platforms, we demonstrate new levels of performance and new capabilities that are enabled by deeper integration between quantum programs and the device physics of hardware.

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arXiv2023

Clifford-based Circuit Cutting for Quantum Simulation

Quantum computing has potential to provide exponential speedups over classical computing for many important applications. However, today's quantum computers are in their early stages, and hardware quality issues hinder the scale of program execution. Benchmarking and simulation of quantum circuits on classical computers is therefore essential to advance the understanding of how quantum computers and programs operate, enabling both algorithm discovery that leads to high-impact quantum computation and engineering improvements that deliver to more powerful quantum systems. Unfortunately, the nature of quantum information causes simulation complexity to scale exponentially with problem size. In this paper, we debut Super.tech's SuperSim framework, a new approach for high fidelity and scalable quantum circuit simulation. SuperSim employs two key techniques for accelerated quantum circuit simulation: Clifford-based simulation and circuit cutting. Through the isolation of Clifford subcircuit fragments within a larger non-Clifford circuit, resource-efficient Clifford simulation can be invoked, leading to significant reductions in runtime. After fragments are independently executed, circuit cutting and recombination procedures allow the final output of the original circuit to be reconstructed from fragment execution results. Through the combination of these two state-of-art techniques, SuperSim is a product for quantum practitioners that allows quantum circuit evaluation to scale beyond the frontiers of current simulators. Our results show that Clifford-based circuit cutting accelerates the simulation of near-Clifford circuits, allowing 100s of qubits to be evaluated with modest runtimes.

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arXiv2022

SupermarQ: A Scalable Quantum Benchmark Suite

The emergence of quantum computers as a new computational paradigm has been accompanied by speculation concerning the scope and timeline of their anticipated revolutionary changes. While quantum computing is still in its infancy, the variety of different architectures used to implement quantum computations make it difficult to reliably measure and compare performance. This problem motivates our introduction of SupermarQ, a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We define a set of feature vectors to quantify coverage, select applications from a variety of domains to ensure the suite is representative of real workloads, and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms. Looking forward, we envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites. We introduce SupermarQ as an important step in this direction.

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arXiv2021

Optimized fermionic SWAP networks with equivalent circuit averaging for QAOA

The fermionic SWAP network is a qubit routing sequence that can be used to efficiently execute the Quantum Approximate Optimization Algorithm (QAOA). Even with a minimally-connected topology on an n-qubit processor, this routing sequence enables O(n^2) operations to execute in O(n) steps. In this work, we optimize the execution of fermionic SWAP networks for QAOA through two techniques. First, we take advantage of an overcomplete set of native hardware operations [including 150 ns controlled-pi/2 phase gates with up to 99.67(1)% fidelity] in order to decompose the relevant quantum gates and SWAP networks in a manner which minimizes circuit depth and maximizes gate cancellation. Second, we introduce Equivalent Circuit Averaging, which randomizes over degrees of freedom in the quantum circuit compilation to reduce the impact of systematic coherent errors. Our techniques are experimentally validated on the Advanced Quantum Testbed through the execution of QAOA circuits for finding the ground state of two- and four-node Sherrington-Kirkpatrick spin-glass models with various randomly sampled parameters. We observe a ~60% average reduction in error (total variation distance) for QAOA of depth p = 1 on four transmon qubits on a superconducting quantum processor.

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