Abstract While the capabilities of quantum hardware have significantly advanced in recent years, executing quantum algorithms as quantum circuits at the lowest possible cost remains crucial, regardless of the hardware progress. We are developing a quantum-state-dependent circuit optimizer called Aqcel . Our guiding principle, implemented as the Aqcel optimization protocol, is to optimize quantum circuits by measuring the states of the control qubits to identify and eliminate unnecessary control operations. In this paper, we introduce two key improvements: the state label manager that reduces unnecessary state measurements and the C X -pair removal process that eliminates redundant gate pairs. These enhancements significantly reduce the number of two-qubit gates, improving the fidelity of quantum circuits executed on quantum hardware. To demonstrate the effectiveness of our method, we apply Aqcel to quantum circuits for the quantum parton shower algorithm. Experimental results using the IBM quantum computer show a substantial reduction in gate counts and an improvement in fidelity compared to the conventional optimization technique as well as the original Aqcel protocol. These results demonstrate that fixed-initial-state information can enable state-dependent optimizations that are complementary to general-purpose, unitary-equivalence-preserving circuit optimizers.
Abstract While the capabilities of quantum hardware have significantly advanced in recent years, executing quantum algorithms as quantum circuits at the lowest possible cost remains crucial, regardless of the hardware progress. We are developing a quantum-state-dependent circuit optimizer called Aqcel . Our guiding principle, implemented as the Aqcel optimization protocol, is to optimize quantum circuits by measuring the states of the control qubits to identify and eliminate unnecessary control operations. In this paper, we introduce two key improvements: the state label manager that reduces unnecessary state measurements and the C X -pair removal process that eliminates redundant gate pairs. These enhancements significantly reduce the number of two-qubit gates, improving the fidelity of quantum circuits executed on quantum hardware. To demonstrate the effectiveness of our method, we apply Aqcel to quantum circuits for the quantum parton shower algorithm. Experimental results using the IBM quantum computer show a substantial reduction in gate counts and an improvement in fidelity compared to the conventional optimization technique as well as the original Aqcel protocol. These results demonstrate that fixed-initial-state information can enable state-dependent optimizations that are complementary to general-purpose, unitary-equivalence-preserving circuit optimizers.
There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivity, and coherence times, a quantum circuit optimization is essential to make the best use of near-term quantum devices. We introduce a new circuit optimizer called AQCEL, which aims to remove redundant controlled operations from controlled gates, depending on initial states of the circuit. Especially, the AQCEL can remove unnecessary qubit controls from multi-controlled gates in polynomial computational resources, even when all the relevant qubits are entangled, by identifying zero-amplitude computational basis states using a quantum computer. As a benchmark, the AQCEL is deployed on a quantum algorithm designed to model final state radiation in high energy physics. For this benchmark, we have demonstrated that the AQCEL-optimized circuit can produce equivalent final states with much smaller number of gates. Moreover, when deploying AQCEL with a noisy intermediate scale quantum computer, it efficiently produces a quantum circuit that approximates the original circuit with high fidelity by truncating low-amplitude computational basis states below certain thresholds. Our technique is useful for a wide variety of quantum algorithms, opening up new possibilities to further simplify quantum circuits to be more effective for real devices.
There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivities, and coherence times, circuit optimization is essential to make the best use of quantum devices produced over a next decade. We introduce two separate ideas for circuit optimization and combine them in a multi-tiered quantum circuit optimization protocol called AQCEL. The first ingredient is a technique to recognize repeated patterns of quantum gates, opening up the possibility of future hardware optimization. The second ingredient is an approach to reduce circuit complexity by identifying zero- or low-amplitude computational basis states and redundant gates. As a demonstration, AQCEL is deployed on an iterative and effcient quantum algorithm designed to model final state radiation in high energy physics. For this algorithm, our optimization scheme brings a significant reduction in the gate count without losing any accuracy compared to the original circuit. Additionally, we have investigated whether this can be demonstrated on a quantum computer using polynomial resources. Our technique is generic and can be useful for a wide variety of quantum algorithms.
<!--HTML-->There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivities, and coherence times, circuit optimization is essential to make the best use of near-term quantum devices. We introduce two separate ideas for circuit optimization and combine them in a multi-tiered quantum circuit optimization protocol called AQCEL. The first ingredient is a technique to recognize repeated patterns of quantum gates, opening up the possibility of future hardware co-optimization. The second ingredient is an approach to reduce circuit complexity by identifying zero- or low-amplitude computational basis states and redundant gates. As a demonstration, AQCEL is deployed on an iterative and efficient quantum algorithm designed to model final state radiation in high energy physics. For this algorithm, our optimization scheme brings a significant reduction in the gate count without losing any accuracy compared to the original circuit. Additionally, we have investigated whether this can be demonstrated on a quantum computer using polynomial resources. Our technique is generic and can be useful for a wide variety of quantum algorithms.
Quantum state preparation initializes the quantum registers and is essential for running quantum algorithms. Designing state preparation circuits that entangle qubits efficiently with fewer two-qubit gates enhances accuracy and alleviates coupling constraints on devices. Existing methods synthesize an initial circuit and leverage compilers to reduce the circuit's gate count while preserving the unitary equivalency. In this study, we identify numerous conditions within the quantum circuit where breaking local unitary equivalences does not alter the overall outcome of the state preparation (i.e., don't cares). We introduce a peephole optimization algorithm that identifies such unitaries for replacement in the original circuit. Exploiting these don't care conditions, our algorithm achieves a 36% reduction in the number of two-qubit gates compared to prior methods.
This paper proposes a novel approach to quantum algorithm optimization through dynamically reconfigurable quantum circuits. The core concept involves adapting the structure of a quantum circuit during its execution, responding to real-time feedback derived from quantum state analysis. Traditional quantum circuit design often relies on static, pre-defined architectures, limiting their adaptability and potential for optimization. This work introduces a control system capable of monitoring qubit states and dynamically altering circuit topology – specifically, adjusting gate sequences and potentially even adding or removing qubits – to minimize gate count and maximize coherence, thereby improving overall algorithm efficiency. The theoretical framework presented here outlines the key components and operational principles of such a system, highlighting its potential to overcome the limitations of static circuits and usher in a new era of adaptive quantum computation. We demonstrate, through a mathematical model, how this dynamic reconfiguration can be strategically implemented to address specific algorithmic challenges, focusing on reducing circuit complexity and enhancing the fidelity of quantum computations. The key to this approach lies in continuous feedback loops and a sophisticated control algorithm that leverages quantum state information to guide circuit modifications. This allows for a more tailored and efficient execution of quantum algorithms compared to static designs.
We present an analysis of quantum circuit fidelity across the full compilation stack, from high-level gate optimization through pulse-level control. We connect a C++ circuit optimizer to a per-gate Lindblad master-equation fidelity model whose decoherence channels are cross-validated against qiskit-dynamics and whose absolute predictions are benchmarked against execution on real hardware. Across a campaign of 4,452 experiment runs over 371 benchmark circuits, gate cancellation provides the dominant improvement (d=1.66, 72% of circuits improved), while circuit size and pulse duration are the strongest negative predictors of process fidelity (input gates r=−0.78; pulse duration r=−0.73, R2=0.53). A formal ablation study shows that pass ordering has no significant effect on two-qubit gate reduction (Kruskal--Wallis p=0.302). Comparing against Qiskit transpilation levels, we show that two-qubit gate count, not total gate count, is the hardware-relevant metric: our optimizer attains superior two-qubit reduction on structured circuits (87.8% on QFT, 100% on QAOA) whereas Qiskit's larger total-gate reduction is dominated by single-qubit (u3) consolidation. Finally, executing eight circuits on the IQM Resonance Garnet processor (8/8 jobs completed, job identifiers released) reveals that the model is a consistent upper bound: it preserves the relative difficulty ordering of circuits but overestimates absolute fidelity by a mean of 0.49, quantifying the error budget (crosstalk, leakage, readout) outside a T1/T2/depolarizing model. We release the framework, data, and scripts as open source.