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IEEE Computer Society Annual Symposium on VLSI

Unlocking the Benefits of Dynamic Quantum Circuits in Resource Constraint Architecture

Abhoy Kole, Mohammed Elkacem Djeridane, Arighna Deb, Kamalika Datta, Indranil Sengupta, Rolf Drechsler

Published Jul 6, 2025• 1 citation
Publisher

Abstract

The execution of large-scale quantum algorithms is currently constrained by the limited number of available qubits, qubit connectivity restrictions, and the inherent noise in quantum processors. To address these limitations, a design methodology known as Dynamic Quantum Circuits (DQC) has emerged. DQC leverages non-unitary operations-such as active reset, midcircuit measurement, and classically controlled gate operationsto reduce qubit requirements during circuit design. Recently, DQC-based implementations have been explored for various algorithms, including Shor’s Prime Factorization, Quantum Phase Estimation (QPE), and Bernstein-Vazirani (BV), as well as for key operations like state preparation, Toffoli networks, and nonlocal gates. While DQC offers a significant reduction in qubit usage, it introduces a trade-off in the form of increased circuit depth. Therefore, assessing the reliability of such circuits becomes crucial in the context of current quantum hardware architectures. In this paper, we analyze the reliability of DQC-based quantum circuit realizations as a function of qubit count and circuit depth. We present empirical results for two algorithms and evaluate how architectural parameters impact their reliability.

DOI
10.1109/isvlsi65124.2025.11130231

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The execution of large-scale quantum algorithms is currently constrained by the limited number of available qubits, qubit connectivity restrictions, and the inherent noise in quantum processors. To address these limitations, a design methodology known as Dynamic Quantum Circuits (DQC) has emerged. DQC leverages non-unitary operations-such as active reset, midcircuit measurement, and classically controlled gate operationsto reduce qubit requirements during circuit design. Recently, DQC-based implementations have been explored for various algorithms, including Shor’s Prime Factorization, Quantum Phase Estimation (QPE), and Bernstein-Vazirani (BV), as well as for key operations like state preparation, Toffoli networks, and nonlocal gates. While DQC offers a significant reduction in qubit usage, it introduces a trade-off in the form of increased circuit depth. Therefore, assessing the reliability of such circuits becomes crucial in the context of current quantum hardware architectures. In this paper, we analyze the reliability of DQC-based quantum circuit realizations as a function of qubit count and circuit depth. We present empirical results for two algorithms and evaluate how architectural parameters impact their reliability.
Recent advances in fault tolerant quantum systems allow to perform non-unitary operations like mid-circuit measurement, active reset and classically controlled gate operations in addition to the existing unitary gate operations. Real quantum devices that support these non-unitary operations enable us to execute a new class of quantum circuits, known as Dynamic Quantum Circuits (DQC). This helps to enhance the scalability, thereby allowing execution of quantum circuits comprising of many qubits by using at least two qubits. Recently DQC realizations of multi-qubit Quantum Phase Estimation (QPE) and Bernstein-Vazirani (BV) algorithms have been demonstrated in two separate experiments. However the dynamic transformation of complex quantum circuits consisting of Toffoli gate operations have not been explored yet. This motivates us to: (a) explore the dynamic realization of Toffoli gates by extending the design space of DQC for Toffoli networks, and (b) propose a general dynamic transformation algorithm for the first time to the best of our knowledge. More precisely, we introduce two dynamic transformation schemes (dynamic-1 and dynamic-2) for Toffoli gates, that differ with respect to the required number of classically controlled gate operations. For evaluation, we consider the Deutsch-Jozsa (DJ) algorithm composed of one or more Toffoli gates. Experimental results demonstrate that dynamic DJ circuits based on dynamic-2 Toffoli realization scheme provides better computational accuracy over the dynamic-1 scheme. Further, the proposed dynamic transformation scheme is generic and can also be applied to non-Toffoli quantum circuits, e.g. BV algorithm.
Dynamic quantum circuits (DQCs) provide a hardware-efficient route to quantum computing by reducing physical-qubit overhead and compressing circuit topology through mid-circuit measurements, qubit reset and reuse, and classical feed-forward control. Here, we demonstrate the advantages of DQCs on a single hybrid superconducting qubit-cavity processor by implementing a hierarchy of algorithms with increasing complexity. This hybrid architecture consists of a high-dimensional cavity qudit serving as the computational register and a dispersively coupled superconducting transmon ancilla that is repeatedly measured, reset, and reused to enable dynamic control. Using this device, we implement a 10-bit Bernstein-Vazirani algorithm with an average success probability of 82%, surpassing state-of-the-art dynamic and static implementations in both scale and performance; an 8-bit quantum phase-estimation protocol with estimation errors below 10-3; and the first dynamic-circuit implementation of Shor's algorithm on a superconducting platform, factoring 15 over all coprime bases with squared statistical overlap values above 99.8%. These results provide concrete benchmarks for future DQC implementations and highlight the versatile advantages of DQCs with the hybrid qubit-qudit architecture, establishing it as a promising route toward scalable, programmable quantum computation.
Over the last two decades there has been immense progress in the field of quantum computing. Although today we have demonstrable quantum computers with more than 1000 qubits, researchers are still trying to show how these machines can be utilized to get substantial benefit for certain applications. We are also in the Noisy Intermediate Scale Quantum (NISQ) era that imposes certain restrictions in utilizing the entire physical qubit space. Implementation of various large-scale quantum algorithms suffers from limited number of available qubits and also the qubit coupling restrictions of the target quantum processor. To this end an advanced class of quantum circuits called Dynamic Quantum Circuits (DQC) has been proposed, which can work with very few additional qubits using various non-unitary operations (viz., active reset, mid-circuit measurement and classically controlled gate operations). This paper particularly presents various design automation challenges that exists in the current NISQ era and shows how DQC can be exploited to overcome some of the challenges.
In this work we have presented design for scalability (DFS) for compiling dynamic quantum circuits in order to execute them on centralized monolithic or distributed environment. Dynamic quantum circuits allow the usage of mid-circuit measurement, active reset and classically controlled quantum operations for circuit designs. Since, quantum algorithms solving practical complex problems involve thousands of qubits that a targeted computing environments may not provide, it has become crucial to seek for schemes that enable dynamic circuit realization of such algorithms using less number of qubits to make them executable on the selected hardware platforms. To this end we have considered two different quantum circuits representing Bernstein–Vazirani Algorithm (BVA) and Variational Quantum Circuit (VQC) for deep Q network, and two computing environments, monolithic and distributed for framing our design solutions. For the monolithic environment we have shown that using only two qubits dynamic version of both these BVA and VQC are achievable. In the distributed environment with quantum communication channel being in place, the corresponding distributed design are also obtained by employing one or more such communication channels. We have also shown that even with structural similarities between the worst-case BVA (featuring a secret string of all 1s) and the VQC, the design alternatives might differ depending on the noise level of the chosen distributed hardware platform.
Fault tolerant quantum computation over distributed quantum computing (DQC) platforms requires careful evaluation of resource requirements and noise thresholds. As quantum hardware advances toward modular and networked architectures, various fault tolerant DQC schemes have been proposed, which can be broadly categorized into three architectural types. Type 1 architectures consist of small quantum nodes connected via Greenberger-Horne-Zeilinger (GHZ) states, enabling nonlocal stabilizer measurements. Type 2 architectures distribute a large error correcting code block across multiple modules, with most stabilizer measurements remaining local, except for a small subset at patch boundaries that are performed using nonlocal CNOT gates. Type 3 architectures assign code blocks to distinct modules and can perform fault tolerant operations such as transversal gates, lattice surgery, and teleportation to implement logical operations between code blocks. Using the planar surface code and toric code as representative examples, we analyze how the resource requirements, particularly the number of Bell pairs and the average number of generation attempts, scale with increasing code distance across different architectural designs. This analysis provides valuable insights for identifying architectures well suited to fault tolerant distributed quantum computation under near term hardware and resource constraints.
Distributed Quantum Computing (DQC) addresses the physical scaling limitations of monolithic quantum processors by networking modular Quantum Processing Units (QPUs). Efficient execution of quantum algorithms on DQC architectures requires compiling them across QPUs while minimizing inter-QPU communication bottlenecks, primarily through circuit partitioning. However, current evaluations of state-of-the-art partitioning heuristics focus primarily on the total entanglement cost of the partitions, failing to capture the broader structural and temporal overheads introduced by distributed network constraints. This paper addresses this evaluation gap by applying established monolithic benchmarking metrics to partitioned distributed circuits to quantify the performance impact of network constraints. Using an open-source, automated evaluation pipeline, we systematically assess diverse partitioning algorithms across standardized workloads and quantum network topologies. Our empirical results reveal that partitioning algorithms with comparable entanglement costs can still introduce drastically different physical execution penalties. By exposing these hidden trade-offs, such as severe increases in circuit depth and substantial reductions in gate density, this study demonstrates that comprehensive circuit-level metrics are essential for guiding the future design of DQC compilers.