Abstract We present a quantum-analogous experimental demonstration of variational quantum process tomography using an optical processor. This approach leverages classical one-hot encoding and unitary decomposition to perform the variational quantum algorithm on a photonic platform. We create the first benchmark for variational quantum process tomography evaluating the performance of the quantum-analogous experiment on the optical processor against several publicly accessible quantum computing platforms, including IBM’s 127-qubit Sherbrooke processor, QuTech’s five-qubit Tuna-5 processor, and Quandela’s 12-mode Ascella quantum optical processor. We evaluate each method using process fidelity, cost function convergence, and processing time per iteration for variational quantum circuit depths of d = 3 and d = 6. Our results indicate that the optical processors outperform their superconducting counterparts in terms of fidelity and convergence behavior reaching fidelities of 0.8 after nine iterations, particularly at higher depths, where the noise of decoherence and dephasing affect the superconducting processors significantly. We further investigate the influence of any additional quantum optical effects in our platform relative to the classical one-hot encoding. From the process fidelity results it shows that the (classical) thermal noise in the phase-shifters dominates over other optical imperfections, such as mode mismatch and dark counts from single-photon sources. The benchmarking framework and experimental results demonstrate that photonic processors are strong contenders for near-term quantum algorithm deployment, particularly in hybrid variational contexts. This analysis is valuable not only for state and process tomography but also for a wide range of applications involving variational quantum circuit based algorithms.
Abstract We present a quantum-analogous experimental demonstration of variational quantum process tomography using an optical processor. This approach leverages classical one-hot encoding and unitary decomposition to perform the variational quantum algorithm on a photonic platform. We create the first benchmark for variational quantum process tomography evaluating the performance of the quantum-analogous experiment on the optical processor against several publicly accessible quantum computing platforms, including IBM’s 127-qubit Sherbrooke processor, QuTech’s five-qubit Tuna-5 processor, and Quandela’s 12-mode Ascella quantum optical processor. We evaluate each method using process fidelity, cost function convergence, and processing time per iteration for variational quantum circuit depths of d = 3 and d = 6. Our results indicate that the optical processors outperform their superconducting counterparts in terms of fidelity and convergence behavior reaching fidelities of 0.8 after nine iterations, particularly at higher depths, where the noise of decoherence and dephasing affect the superconducting processors significantly. We further investigate the influence of any additional quantum optical effects in our platform relative to the classical one-hot encoding. From the process fidelity results it shows that the (classical) thermal noise in the phase-shifters dominates over other optical imperfections, such as mode mismatch and dark counts from single-photon sources. The benchmarking framework and experimental results demonstrate that photonic processors are strong contenders for near-term quantum algorithm deployment, particularly in hybrid variational contexts. This analysis is valuable not only for state and process tomography but also for a wide range of applications involving variational quantum circuit based algorithms.
In this work, we present two new methods for Variational Quantum Circuit (VQC) Process Tomography onto n qubits systems: PT_VQC and U-VQSVD.Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for process tomography and decreases the required seed states from 4nto 2n, ensuring high-fidelity reconstruction of the targeted unitary U. It is worth noting that, for a fixed reconstruction accuracy, PT_VQC achieves faster convergence per iteration compared to Quantum Deep Neural Network (QDNN) and tensor network schemes.U-VQSVD utilizes variational singular value decomposition to extract eigenvectors (up to a global phase) and their associated eigenvalues from an unknown unitary representing a general channel. We assess the performance of U-VQSVD by executing an attack on a non-unitary channel Quantum Physical Unclonable Function (QPUF), outperforming an uninformed impersonation attack by a factor of 2 to 5, depending on the qubit dimension.For the two presented methods, we propose a new approach to calculate the complexity of the displayed VQC, based on what we denote as optimal depth.
Abstract In this work, we present two new methods for variational quantum circuit (VQC) process tomography (PT) ontonqubits systems: unitary PT based on VQCs (PT_VQC) and unitary evolution-based variational quantum singular value decomposition (U-VQSVD). Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for unitary PT and decreases the required state initializations from 4nto just 2n, all while ensuring high-fidelity reconstruction of the targeted unitary channelU. It is worth noting that, for a fixed reconstruction accuracy, PT_VQC achieves faster convergence per iteration step compared to quantum deep neural network and tensor network schemes. The novel U-VQSVD algorithm utilizes variational singular value decomposition to extract eigenvectors (up to a global phase) and their associated eigenvalues from an unknown unitary representing a universal channel. We assess the performance of U-VQSVD by executing an attack on a non-unitary channel quantum physical unclonable function. By using U-VQSVD we outperform an uninformed impersonation attack (using randomly generated input states) by a factor of 2 to 5, depending on the qubit dimension. For the two presented methods, we propose a new approach to calculate the complexity of the displayed VQC, based on what we denote as optimal depth.
Cloud-accessible quantum processors enable direct execution of quantum algorithms on heterogeneous hardware platforms. Unlike classical systems, however, identical quantum circuits may exhibit substantially different behavior across devices due to architectural variations in qubit connectivity, gate fidelity, and coherence times.
In this work, we systematically benchmark five representative quantum algorithms - Bell state preparation, GHZ state generation, Quantum Fourier Transform (QFT), Grover's Search, and the Quantum Approximate Optimization Algorithm (QAOA) - across trapped-ion, superconducting, and simulator backends using Amazon Braket. Performance metrics including fidelity, CHSH violation, success probability, circuit depth, and gate counts are evaluated.
Our results demonstrate a strong dependence of algorithmic performance on hardware topology and noise characteristics. For example, 10-qubit GHZ states achieved fidelities above 0.8 on trapped-ion hardware, while superconducting platforms dropped below 0.15 due to routing overhead and accumulated two-qubit gate errors. These findings highlight the importance of hardware-aware algorithm selection and provide practical guidance for benchmarking in the NISQ era.
The Berkeley gate is a high-performance, two-qubit entangling operation with particular potential for quantum error correction and fault-tolerant protocols. However, harnessing this potential on current noisy intermediate-scale quantum (NISQ) processors, requires efficient compilation and robust performance under realistic noise conditions. In this work, we demonstrate a hardware-efficient implementation of the Berkeley gate on a superconducting quantum processor. Using quantum process tomography (QPT), we experimentally characterize its performance and benchmark it against a noiseless quantum simulator to evaluate its practical reliability in the NISQ era. Experimental measurements confirm the gate's correct logical action, producing the target partially entangled state with a subspace confinement probability of [Formula: see text] on real quantum hardware compared to 100% in quantum simulation. Results from QPT experiments show a simulated process fidelity of [Formula: see text], while the experimental process fidelity on hardware is [Formula: see text]. The observed discrepancy is analyzed in the context of device-specific noise sources, including qubit relaxation, dephasing, and state preparation and measurement (SPAM) errors. Our work provides a concrete fidelity benchmark for the Berkeley gate on superconducting hardware and quantify the impact of realistic noise on a non-trivial two-qubit operation, supporting its use in near-term algorithmic and error-correction applications.
Spurious couplings and decoherence degrade the performance of solid-state quantum processors, demanding careful design, calibration, and mitigation protocols. These strategies often rely on characterization of the idling processor, but tomographic recovery of (time-independent) Lindblad dynamics scales exponentially with qubit count. Here, we experimentally benchmark and demonstrate that randomized ("shadow") measurements accelerate Lindblad tomography on a superconducting transmon processor. We first implement extensible Lindblad tomography, which estimates Lindblad parameters using a complete tomographic dataset, and use it as a baseline to benchmark a shadow tomography approach, shadow Lindblad tomography. The shadow approach recycles randomized configurations to estimate the same Lindblad parameters using far fewer resources under physically motivated locality assumptions. We experimentally verify these assumptions in our processor by implementing the protocols on one- and three-qubit subsystems; here, shadow Lindblad tomography reproduces extensible Lindblad tomography within uncertainties while using exponentially fewer configurations. Leveraging this efficiency, we apply shadow Lindblad tomography to the full five-qubit processor and recover all single qubit dissipation and two-qubit coupling parameters in 9 hours of acquisition time compared to an estimated 58 hours for extensible Lindblad tomography. Additionally, our shadow implementation is compatible with conventional Gaussian error propagation, avoiding the use of median-of-means estimators. Together, these results demonstrate how randomized shadow tomography protocols can be practically implemented to learn quantum processor dynamics at an increasing qubit count.
In recent years, variational quantum algorithms (VQA) have been receiving a great deal of attention, demonstrating their applicability in a wide range of tasks and implementable even in the noisy quantum processors currently available [1]. A VQA is a hybrid quantum-classical algorithm where a problem is encoded in a cost function that can be efficiently evaluated on a parametric quantum device and the minimum represents the desired solution. A classical optimizer can then be used to tune the parameters of the quantum device in order to minimize the loss function and find the optimal solution (Fig. 1). In the field of quantum computing, one of the main promising platforms is based on the use of single photons and integrated photonic circuits since photons are a natural choice for transferring information and have different degree of freedom that can be to encode the information. Moreover the use of integrated photonic allow the realization of compact and table complex interferometers with a high number of control parameters. Even if there are few applications [2], [3], VQA with photonic platforms remains a relatively unexplored subject. One of the main problems is the application of the classical optimizer to the photonic platform since most of the algorithms are based on the estimation of the gradient of the cost function with the finite difference method but that can not be applied to a noisy apparatus.