The development of complex circuits for practical applications in the current quantum computing ecosystem is based on basic primitives such as Bell states, which provide superposition, entanglement, and coherence. The range of domain-specific quantum applications has been greatly expanded by the availability of simulators and platforms such as IBM Quantum, which are supported by Qiskit. However, disparities between ideal simulator outputs and actual quantum processing unit (QPU) executions in the Noisy Intermediate-Scale Quantum (NISQ) era require the application of quantum error mitigation techniques. Limitations arise from hardware constraints in superconducting qubit systems and from the limited resources of classical simulators as quantum circuits grow. Quantum decoherence, which lowers gate fidelity and builds up at the circuit level with increasing depth, is specifically caused by material-induced flaws and interfaces. This creates a clear connection between circuit reliability, device performance, and material attributes. To address this, the current work uses both simulation and actual hardware on the IBM Sherbrooke 127-qubit processor to study three basic circuit classes over 4 to 10 qubits: the quantum Fourier transform, the Greenberger-Horne-Zeilinger state, and the W state. The study examines trade-offs between circuit complexity, noise robustness, and resource utilization by contrasting simulator and QPU results. The results imply that circuit fidelity can serve as an indirect probe of material-limited noise, opening the door to a framework for designing quantum circuits that accounts for both hardware and materials to achieve scalable quantum advantage.
The development of complex circuits for practical applications in the current quantum computing ecosystem is based on basic primitives such as Bell states, which provide superposition, entanglement, and coherence. The range of domain-specific quantum applications has been greatly expanded by the availability of simulators and platforms such as IBM Quantum, which are supported by Qiskit. However, disparities between ideal simulator outputs and actual quantum processing unit (QPU) executions in the Noisy Intermediate-Scale Quantum (NISQ) era require the application of quantum error mitigation techniques. Limitations arise from hardware constraints in superconducting qubit systems and from the limited resources of classical simulators as quantum circuits grow. Quantum decoherence, which lowers gate fidelity and builds up at the circuit level with increasing depth, is specifically caused by material-induced flaws and interfaces. This creates a clear connection between circuit reliability, device performance, and material attributes. To address this, the current work uses both simulation and actual hardware on the IBM Sherbrooke 127-qubit processor to study three basic circuit classes over 4 to 10 qubits: the quantum Fourier transform, the Greenberger-Horne-Zeilinger state, and the W state. The study examines trade-offs between circuit complexity, noise robustness, and resource utilization by contrasting simulator and QPU results. The results imply that circuit fidelity can serve as an indirect probe of material-limited noise, opening the door to a framework for designing quantum circuits that accounts for both hardware and materials to achieve scalable quantum advantage.
The practical utility of Noisy Intermediate-Scale Quantum (NISQ) processors depends critically on their performance under demanding computational loads. We investigated the impact of circuit depth on the 127-qubit superconducting processor ibm_brisbane by executing stress-test circuits utilizing all available qubits. These circuits consisted of repeated layers combining random single-qubit rotations and linear chains of Controlled-Z entangling gates. We systematically increased the ideal circuit depth from 1 up to 30 layers, resulting in transpiled circuits ranging from approximately 550 to over 15,000 gates (optimization level 2). Using the qiskit-ibm-runtime Sampler primitive with 4096 shots and default error mitigation, we successfully obtained results for ideal depths up to 22 (transpiled depth ~11.7k gates), beyond which platform usage limits prevented execution. Our central finding is the immediate onset of noise saturation: the output distribution reached maximum sample entropy (12.0 bits) and exhibited near-zero average single-qubit Z magnetization even at the shallowest depth tested (ideal depth 1, transpiled depth ~550). These noise-dominated characteristics persisted despite significant increases in transpiled circuit depth. This demonstrates that for these specific full-width, complex random circuits, noise overwhelms the computation almost instantaneously on this device under default settings, highlighting the profound challenge of achieving computational fidelity for deep, wide algorithms in the NISQ era without advanced error handling.
Abstract The practical utility of Noisy Intermediate-Scale Quantum (NISQ) processors depends critically on their performance under demanding computational loads. We investigated the impact of circuit depth on the 127-qubit superconducting processor ibm_brisbane by executing stress-test circuits utilizing all available qubits. These circuits consisted of repeated layers combining random single-qubit rotations and linear chains of Controlled-Z entangling gates. We systematically increased the ideal circuit depth from 1 up to 30 layers, resulting in transpiled circuits ranging from approximately 550 to over 15,000 gates (optimization level 2). Using the qiskit-ibm-runtime Sampler primitive with 4096 shots and default error mitigation, we successfully obtained results for ideal depths up to 22 (transpiled depth ~ 11.7k gates), beyond which platform usage limits prevented execution. Our central finding is the immediate onset of noise saturation: the output distribution reached maximum sample entropy (12.0 bits) and exhibited near-zero average single-qubit Z magnetization even at the shallowest depth tested (ideal depth 1, transpiled depth ~ 550). These noise-dominated characteristics persisted despite significant increases in transpiled circuit depth. This demonstrates that for these specific full-width, complex random circuits, noise overwhelms the computation almost instantaneously on this device under default settings, highlighting the profound challenge of achieving computational fidelity for deep, wide algorithms in the NISQ era without advanced error handling.
The pursuit of a universal, fault-tolerant quantum computer has transformed quantum computing from a predominantly theoretical discipline into a rapidly diversifying hardware ecosystem. This review presents a comprehensive and technically grounded analysis of the principal physical qubit modalities: superconducting circuits, trapped ions, photonic platforms, neutral atoms, semiconductor spin qubits, and topological approaches, focusing on their underlying operating principles, performance characteristics, and system-level constraints. We examine how these qubit technologies are embedded within scalable quantum processor architectures, including control and readout infrastructures, connectivity topologies, and quantum error correction strategies. The review provides a comparative study of key performance metrics, such as coherence times, gate fidelities, error rates, and scalability, reveals fundamental trade-offs between speed, robustness, and operational complexity that shape distinct technological roadmaps and target applications. We further highlight the ongoing transition from noisy intermediate-scale quantum (NISQ) devices toward architectures centred on logical qubits, driven by advances in materials engineering, precision control, and quantum information theory. Together, these developments sketch the emerging routes toward fault-tolerant quantum computing and practical quantum advantage, while offering readers a coherent entry point and reference guide to the current quantum hardware landscape.
The practical utility of Noisy Intermediate-Scale Quantum (NISQ) processors depends critically on their performance under demanding computational loads. We investigated the impact of circuit depth on the 127-qubit superconducting processor ibm_brisbane by executing stress-test circuits utilizing all available qubits. These circuits consisted of repeated layers combining random single-qubit rotations and linear chains of Controlled-Z entangling gates. We systematically increased the ideal circuit depth from 1 up to 30 layers, resulting in transpiled circuits ranging from approximately 550 to over 15,000 gates (optimization level 2). Using the qiskit-ibm-runtime Sampler primitive with 4096 shots and default error mitigation, we successfully obtained results for ideal depths up to 22 (transpiled depth ~11.7k gates), beyond which platform usage limits prevented execution. Our central finding is the immediate onset of noise saturation: the output distribution reached maximum sample entropy (12.0 bits) and exhibited near-zero average single-qubit Z magnetization even at the shallowest depth tested (ideal depth 1, transpiled depth ~550). These noise-dominated characteristics persisted despite significant increases in transpiled circuit depth. This demonstrates that for these specific full-width, complex random circuits, noise overwhelms the computation almost instantaneously on this device under default settings, highlighting the profound challenge of achieving computational fidelity for deep, wide algorithms in the NISQ era without advanced error handling.
The practical utility of Noisy Intermediate-Scale Quantum (NISQ) processors depends critically on their performance under demanding computational loads. We investigated the impact of circuit depth on the 127-qubit superconducting processor ibm_brisbane by executing stress-test circuits utilizing all available qubits. These circuits consisted of repeated layers combining random single-qubit rotations and linear chains of Controlled-Z entangling gates. We systematically increased the ideal circuit depth from 1 up to 30 layers, resulting in transpiled circuits ranging from approximately 550 to over 15,000 gates (optimization level 2). Using the qiskit-ibm-runtime Sampler primitive with 4096 shots and default error mitigation, we successfully obtained results for ideal depths up to 22 (transpiled depth ~11.7k gates), beyond which platform usage limits prevented execution. Our central finding is the immediate onset of noise saturation: the output distribution reached maximum sample entropy (12.0 bits) and exhibited near-zero average single-qubit Z magnetization even at the shallowest depth tested (ideal depth 1, transpiled depth ~550). These noise-dominated characteristics persisted despite significant increases in transpiled circuit depth. This demonstrates that for these specific full-width, complex random circuits, noise overwhelms the computation almost instantaneously on this device under default settings, highlighting the profound challenge of achieving computational fidelity for deep, wide algorithms in the NISQ era without advanced error handling.
The practical utility of Noisy Intermediate-Scale Quantum (NISQ) processors depends critically on their performance under demanding computational loads. We investigated the impact of circuit depth on the 127-qubit superconducting processor ibm_brisbane by executing stress-test circuits utilizing all available qubits. These circuits consisted of repeated layers combining random single-qubit rotations and linear chains of Controlled-Z entangling gates. We systematically increased the ideal circuit depth from 1 up to 30 layers, resulting in transpiled circuits ranging from approximately 550 to over 15,000 gates (optimization level 2). Using the qiskit-ibm-runtime Sampler primitive with 4096 shots and default error mitigation, we successfully obtained results for ideal depths up to 22 (transpiled depth ~11.7k gates), beyond which platform usage limits prevented execution. Our central finding is the immediate onset of noise saturation: the output distribution reached maximum sample entropy (12.0 bits) and exhibited near-zero average single-qubit Z magnetization even at the shallowest depth tested (ideal depth 1, transpiled depth ~550). These noise-dominated characteristics persisted despite significant increases in transpiled circuit depth. This demonstrates that for these specific full-width, complex random circuits, noise overwhelms the computation almost instantaneously on this device under default settings, highlighting the profound challenge of achieving computational fidelity for deep, wide algorithms in the NISQ era without advanced error handling. .