arXiv subjects

Ed Younis

Publications and source records attributed to Ed Younis.

At least 19 recordsLinked to original sources

arXiv2026

Quantum Simulations of Two-Dimensional Non-Abelian Adjoint String Breaking

Quantum computers offer the potential to directly probe the dynamics of strongly coupled quantum field theories. As a step towards reaching this potential, local Krylov-based truncations of a pure SU(2) lattice gauge theory on a triangular lattice are constructed. Adjoint strings connected to dynamical gluons are constructed in this truncated theory, and the resonances dominating the long-term dynamics at a large value of the gauge coupling are determined. Local operators are constructed to identify string oscillations and breakings. This is used to perform a quantum simulation of adjoint string breaking on an $8\times8$ and $16\times8$-site lattice with ibm_boston using all 156 qubits. Quantitative agreement with tensor network simulations is obtained for circuits with 7,634 CZ gates with a two-qubit gate depth of 218. In this simulation, the rates of oscillations and glueball production are identified with a distinctly non-Abelian signature of the underlying gauge group.

hep-lat↗

arXiv2026

Multi-Qubit Dyadic Phase Fixing for Fault-Tolerant Quantum Compilation

Fault-tolerant quantum computing requires translating application-level quantum circuits into the Clifford+$T$ gate set, where the $T$ gate is the dominant resource cost. Phase kickback is an ancilla-based technique that can dramatically reduce $T$-count for rotations with dyadic angles, but has previously been limited to highly structured circuit families. We present Dyadic Phase Fixing (DPF), a general multi-qubit synthesis tool that extends phase kickback to general quantum circuits. DPF uses numerical unitary synthesis to greedily extract dyadic angle rotations from any input circuit. Combined with a decision matrix to automatically size the final phase gradient register, our end-to-end workflow achieves up to 70% reduction in $T$-count compared to \texttt{gridsynth} and up to 60% compared to Repeat-Until-Success synthesis on a diverse set of benchmarks. We map these compiled circuits to a surface-code architecture to evaluate space-time volume, demonstrating up to a 60\% reduction in this metric as well. However, for some circuits and mapping strategies the two metrics diverge significantly, demonstrating that $T$-count alone is a useful but incomplete proxy for fault-tolerant program costs.

quant-ph↗

arXiv2026

Scaling Qubit Mapping and Routing With Position Graph Abstraction and Memoization

Scalable qubit mapping and routing remain major bottlenecks in quantum compilation, especially for Trapped-Ion Quantum Charge-Coupled device (TI-QCCD) architectures, where qubit interactions require physically shuttling ions under strict movement, congestion, and trap-capacity constraints. We present a compilation framework built around the position graph abstraction, a unified representation of executable locations, movement paths, and routing constraints that enables heuristic mappers to operate directly on shuttling-based hardware. Using this abstraction, we accelerate the SWAP-based BidiREctional heuristic search (SABRE) by implementing relative move scoring, which caches repeated heuristic move evaluations that arise during search, and memoized congestion resolution, which speeds up the resolution of repeated congestion. This optimization removes redundant computation without changing routing/shuttling decisions, improving the scalability of SABRE-based methods on TI-QCCD systems. Our results show that combining an architecture-aware abstraction with memoized heuristic evaluation yields a practical and effective path toward scalable qubit mapping and routing across heterogeneous quantum architectures.

quant-ph↗

arXiv2026

T Count as a Numerically Solvable Minimization Problem

We present a formulation of the problem of finding the smallest T -Count circuit that implements a given unitary as a binary search over a sequence of continuous minimization problems, and demonstrate that these problems are numerically solvable in practice. We reproduce best-known results for synthesis of circuits with a small number of qubits, and push the bounds of the largest circuits that can be solved for in this way. Additionally, we show that circuit partitioning can be used to adapt this technique to be used to optimize the T -Count of circuits with large numbers of qubits by breaking the circuit into a series of smaller sub-circuits that can be optimized independently.

quant-ph↗

arXiv2025

OpenQudit: Extensible and Accelerated Numerical Quantum Compilation via a JIT-Compiled DSL

High-performance numerical quantum compilers rely on classical optimization, but are limited by slow numerical evaluations and a design that makes extending them with new instructions a difficult, error-prone task for domain experts. This paper introduces OpenQudit, a compilation framework that solves these problems by allowing users to define quantum operations symbolically in the Qudit Gate Language (QGL), a mathematically natural DSL. OpenQudit's ahead-of-time compiler uses a tensor network representation and an e-graph-based pass for symbolic simplification before a runtime tensor network virtual machine (TNVM) JIT-compiles the expressions into high-performance native code. The evaluation shows that this symbolic approach is highly effective, accelerating the core instantiation task by up to $\mathtt{\sim}20\times$ on common quantum circuit synthesis problems compared to state-of-the-art tools.

quant-ph↗

arXiv2025

Application Scale Quantum Circuit Compilation with Controlled Error

Compilation and optimization of quantum circuits are critical components in the execution of algorithms on quantum computers. These components must successfully balance two competing priorities: minimizing the number of expensive resources, such as two-qubit gates or arbitrary angle single-qubit rotations, and minimizing the approximation error of the compiled circuit to the ideal target unitary describing the quantum algorithm. We develop a practical workflow for managing and optimizing this tradeoff, which enables quantum circuit compilation and optimization at scales of hundreds of qubits. Our workflow is able to tackle circuits at such large scales while providing rigorous guarantees on circuit output error by leveraging circuit partitioning and the notion of averaging over circuit ensembles. We demonstrate our workflow on several benchmark algorithmic circuits acting on up to 380 qubits, and show that it can simultaneously achieve substantial reductions in resource-intensive gates and control output errors, offering a practical and scalable strategy for both near-term and fault-tolerant quantum computing.

quant-ph↗

arXiv2025

Collective Neutrino Oscillations in Three Flavors on Qubit and Qutrit Processors

Collective neutrino flavor oscillations are of primary importance in understanding the dynamic evolution of core-collapse supernovae and subsequent terrestrial detection, but also among the most challenging aspects of numerical simulations. This situation is complicated by the quantum many-body nature of the problem due to neutrino-neutrino interactions which demands a quantum treatment. An additional complication is the presence of three flavors, which often is approximated by the electron flavor and a heavy lepton flavor. In this work, we provide both qubit and qutrit encodings for all three flavors, and develop optimized quantum circuits for the time evolution and analyze the Trotter error. We conclude our study with a hardware experiment of a system of two neutrinos with superconducting hardware: the IBM Torino device for qubits and AQT device for qutrits. We find that error mitigation greatly helps in obtaining a signal consistent with simulations. While hardware results are comparable at this stage, we expect the qutrit setup to be more convenient for large-scale simulations since it does not suffer from probability leakage into nonphsycial qubit space, unlike the qubit setup.

quant-ph↗

arXiv2025

Efficient Compilation for Shuttling Trapped-Ion Machines via the Position Graph Architectural Abstraction

With the growth of quantum platforms for gate-based quantum computation, compilation holds a crucial role in deciding the success of the implementation. While there has been rich research in compilation techniques for the superconducting-qubit regime. The trapped-ion architectures, currently leading in robust quantum computations for their reliable operations, still lack competitive compilation strategies. This work introduces a unifying hardware abstraction, the ``position graph'', representing various hardware architectures. With this abstraction, we model trapped-ion Quantum Charge-Coupled Device (QCCD) architectures, enabling high-quality, scalable compilation methods. Specifically, we propose scheduling algorithms called SHuttling-Aware PERmutative (SHAPER) and SHuttling-AWare (SHAW) heuristic searches to tackle the complex constraints and dynamics of trapped-ion machines, with the cooperation of state-of-the-art permutation-aware mapping. These approaches generate executable circuits and native instructions that respect the physical constraints of shuttling-based architectures. We evaluate proposed algorithms across theorized and real architectures using the position graph framework. For completeness, we also introduce a linear program of trapped-ion scheduling that yields the optimal schedules, enabling a direct comparison with our heuristics. Our algorithm can successfully compile programs for extreme architectures where priori algorithms fail. When the baseline does complete, our produced schedules are $1.45$ times faster on average, with best-case speedups up to $4$ times faster.

quant-ph↗

arXiv2025

Unitary Expressions: A Necessary Abstraction for Extensible Quantum Programming Languages and Systems

Quantum gates are the fundamental instructions of digital quantum computers. Current programming languages, systems, and software development toolkits identify these operational gates by their titles, which requires a shared understanding of their meanings. However, in the continuously developing software ecosystem surrounding quantum computing -- spanning high-level programming systems to low-level control stacks -- this identification process is often error-prone, challenging to debug, maintenance-heavy, and resistant to change. In this paper, we propose replacing this nominal gate representation with a functional one. We introduce the OpenQudit system for describing, parsing, optimizing, analyzing, and utilizing programs comprising gates described as symbolic unitary expressions. As part of this effort, we design the Qudit Gate Language (QGL), a unitary-specific expression language, and implement a differentiating just-in-time compiler in OpenQudit towards embedding this language in quantum programming languages and systems. Additionally, we have precisely designed and implemented the Qudit Virtual Machine (QVM) to evaluate quantum programs and their gradients efficiently. This evaluation is performed millions of times during the compilation of quantum programs. Our QVM can compute gradients approximately ten times faster than current leading numerical quantum compilation frameworks in the most common use cases. Altogether, the OpenQudit system is envisioned to (1) support many-level or qudit-based quantum systems, (2) enable the safe composition of program transformation tools, (3) accelerate circuit optimizers and transpilers, (4) enable compiler extensibility, and (5) provide a productive, simple-to-use interface to quantum practitioners.

cs.PL↗

arXiv2024

AC/DC: Automated Compilation for Dynamic Circuits

Dynamic quantum circuits incorporate mid-circuit measurements and feed-forward operations originally intended to realize Quantum Error Correction. This paradigm has recently been utilized to prepare certain states and long-range entangling gates as well as reduce resource overhead in quantum algorithms such as Quantum Fourier Transformation and Quantum Phase Estimation. In this paper, we present a novel framework for generating dynamic quantum circuits that automatically prepare any state or unitary operator. This procedure is powered by numerical optimization-based circuit synthesis methods. The first contribution is introducing optimization objective functions incorporating mid-circuit measurement and feed-forward operations. The second contribution is incorporating these into a popular open-source quantum circuit synthesis framework. We demonstrate the generation of dynamic circuits for state preparation, long-range entangling gates, circuit optimization, and the application of dynamic circuits to lattice simulations. The resulting circuits are validated through simulation and execution on quantum hardware. Furthermore, we perform noise analysis to explore the impact of different error ratios in mid-circuit measurements and gate errors, identifying scenarios where dynamic circuits offer the most significant benefits. The dynamic circuits generated by our framework show substantial improvements in reducing circuit depth and, in some cases, the number of gates required. To our knowledge, this is the first practical procedure to generate dynamic quantum circuits. Our objective functions are independent of the underlying synthesis framework and can be easily reused. This framework opens new possibilities for circuit generation and optimization methods, highlighting the potential of dynamic circuits to enhance the performance of quantum algorithms on near-term quantum computers.

quant-ph↗

arXiv2024

High-Precision Multi-Qubit Clifford+T Synthesis by Unitary Diagonalization

Resource-efficient and high-precision approximate synthesis of quantum circuits expressed in the Clifford+T gate set is vital for Fault-Tolerant quantum computing. Efficient optimal methods are known for single-qubit RZ unitaries, otherwise the problem is generally intractable. Search-based methods, like simulated annealing, empirically generate low resource cost approximate implementations of general multi-qubit unitaries so long as low precision (Hilbert-Schmidt distances of e>10^-2) can be tolerated. These algorithms build up circuits that directly invert target unitaries. We instead leverage search-based methods to first approximately diagonalize a unitary, then perform the inversion analytically. This lets difficult continuous rotations be bypassed and handled in a post-processing step. Our approach improves both the implementation precision and run time of synthesis algorithms by orders of magnitude when evaluated on unitaries from real quantum algorithms. On benchmarks previously synthesizable only with analytical techniques like the Quantum Shannon Decomposition, diagonalization uses an average of 95% fewer non-Clifford gates.

quant-ph↗

arXiv2024

Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation

Existing numerical optimizers deployed in quantum compilers use expensive $\mathcal{O}(4^n)$ matrix-matrix operations. Inspired by recent advances in quantum machine learning (QML), QFactor-Sample replaces matrix-matrix operations with simpler $\mathcal{O}(2^n)$ circuit simulations on a set of sample inputs. The simpler the circuit, the lower the number of required input samples. We validate QFactor-Sample on a large set of circuits and discuss its hyperparameter tuning. When incorporated in the BQSKit quantum compiler and compared against a state-of-the-art domain-specific optimizer, We demonstrate improved scalability and a reduction in compile time, achieving an average speedup factor of 69 for circuits with more than 8 qubits. We also discuss how improved numerical optimization affects the dynamics of partitioning-based compilation schemes, which allow a trade-off between compilation speed and solution quality.

quant-ph↗

arXiv2024

Quantum Hardware Roofline: Evaluating the Impact of Gate Expressivity on Quantum Processor Design

The design space of current quantum computers is expansive with no obvious winning solution. This leaves practitioners with a clear question: "What is the optimal system configuration to run an algorithm?". This paper explores hardware design trade-offs across NISQ systems to guide algorithm and hardware design choices. The evaluation is driven by algorithmic workloads and algorithm fidelity models which capture architectural features such as gate expressivity, fidelity, and crosstalk. We also argue that the criteria for gate design and selection should be extended from maximizing average fidelity to a more comprehensive approach that takes into account the gate expressivity with respect to algorithmic structures. We consider native entangling gates (CNOT, ECR, CZ, ZZ, XX, Sycamore, $\sqrt{\text{iSWAP}}$), proposed gates (B Gate, $\sqrt[4]{\text{CNOT}}$, $\sqrt[8]{\text{CNOT}}$), as well as parameterized gates (FSim, XY). Our methodology is driven by a custom synthesis driven circuit compilation workflow, which is able to produce minimal circuit representations for a given system configuration. By providing a method to evaluate the suitability of algorithms for hardware platforms, this work emphasizes the importance of hardware-software co-design for quantum computing.

quant-ph↗

arXiv2024

Ground state energy and magnetization curve of a frustrated magnetic system from real-time evolution on a digital quantum processor

Models of interacting many-body quantum systems that may realize new exotic phases of matter, notably quantum spin liquids, are challenging to study using even state-of-the-art classical methods such as tensor network simulations. Quantum computing provides a promising route for overcoming these difficulties to find ground states, dynamics, and more. In this paper, we argue that recently developed hybrid quantum-classical algorithms based on real-time evolution are promising methods for solving a particularly important model in the search for spin liquids, the antiferromagnetic Heisenberg model on the two-dimensional kagome lattice. We show how to construct efficient quantum circuits to implement time evolution for the model and to evaluate key observables on the quantum computer, and we argue that the method has favorable scaling with increasing system size. We then restrict to a 12-spin star plaquette from the kagome lattice and a related 8-spin system, and we give an empirical demonstration on these small systems that the hybrid algorithms can efficiently find the ground state energy and the magnetization curve. For these demonstrations, we use four levels of approximation: exact state vectors, exact state vectors with statistical noise from sampling, noisy classical emulators, and (for the 8-spin system only) real quantum hardware, specifically the Quantinuum H1-1 processor; for the noisy simulations and hardware demonstration, we also employ error mitigation strategies based on the symmetries of the Hamiltonian. Our results strongly suggest that these hybrid algorithms present a promising direction for studying quantum spin liquids and more generally for resolving important unsolved problems in condensed matter theory and beyond.

quant-ph↗

arXiv2023

Empowering high-dimensional quantum computing by traversing the dual bosonic ladder

High-dimensional quantum information processing has emerged as a promising avenue to transcend hardware limitations and advance the frontiers of quantum technologies. Harnessing the untapped potential of the so-called qudits necessitates the development of quantum protocols beyond the established qubit methodologies. Here, we present a robust, hardware-efficient, and extensible approach for operating multidimensional solid-state systems using Raman-assisted two-photon interactions. To demonstrate its efficacy, we construct a set of multi-qubit operations, realize highly entangled multidimensional states including atomic squeezed states and Schr\"odinger cat states, and implement programmable entanglement distribution along a qudit array. Our work illuminates the quantum electrodynamics of strongly driven multi-qudit systems and provides the experimental foundation for the future development of high-dimensional quantum applications.

quant-ph↗

arXiv2023

QSweep: Pulse-Optimal Single-Qudit Synthesis

The synthesis of single-qudit unitaries has mainly been understudied, resulting in inflexible and non-optimal analytical solutions, as well as inefficient and impractical numerical solutions. To address this challenge, we introduce QSweep, a guided numerical synthesizer that produces pulse-optimal single-qudit decompositions for any subspace gateset, outperforming all prior solutions. When decomposing ququart gates, QSweep created circuits 4100x (up to 23500x) faster than QSearch with an average of 7.9 fewer pulses than analytical solutions, resulting in an overall 1.54x and 2.36x improvement in experimental single-qutrit and ququart gate fidelity as measured by randomized benchmarking.

quant-ph↗

arXiv2023

Powerful Quantum Circuit Resizing with Resource Efficient Synthesis

In the noisy intermediate-scale quantum era, mid-circuit measurement and reset operations facilitate novel circuit optimization strategies by reducing a circuit's qubit count in a method called resizing. This paper introduces two such algorithms. The first one leverages gate-dependency rules to reduce qubit count by 61.6% or 45.3% when optimizing depth as well. Based on numerical instantiation and synthesis, the second algorithm finds resizing opportunities in previously unresizable circuits via dependency rules and other state-of-the-art tools. This resizing algorithm reduces qubit count by 20.7% on average for these previously impossible-to-resize circuits.

quant-ph↗

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.

quant-ph↗